mirror of
https://github.com/getcompanion-ai/co-mono.git
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e1ce9c1f49
Previous test used compressed 8k images (0.01MB) which was meaningless. Now tests with actual large noise images that don't compress. Realistic payload limits discovered: - Anthropic: 6 x 3MB = ~18MB total (not 32MB as documented) - OpenAI: 2 x 15MB = ~30MB total - Gemini: 10 x 20MB = ~200MB total (very permissive) - Mistral: 4 x 10MB = ~40MB total - xAI: 1 x 20MB (strict request size limit) - Groq: 5 x 5760px images (5 image + pixel limit) - zAI: 2 x 15MB = ~30MB (50MB request limit) - OpenRouter: 2 x 5MB = ~10MB total Also fixed GEMINI_API_KEY env var (was GOOGLE_API_KEY). Related to #120
1120 lines
39 KiB
TypeScript
1120 lines
39 KiB
TypeScript
/**
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* Image limits test suite
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*
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* Tests provider-specific image limitations:
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* - Maximum number of images in a context (with small 100x100 images)
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* - Maximum image size (bytes)
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* - Maximum image dimensions
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* - Maximum payload (realistic large images stress test)
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*
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* ============================================================================
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* DISCOVERED LIMITS (Dec 2025):
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* ============================================================================
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*
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* BASIC LIMITS (small images):
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* | Provider | Model | Max Images | Max Size | Max Dim |
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* |-------------|--------------------|------------|----------|----------|
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* | Anthropic | claude-3-5-haiku | 100 | 5MB | 8000px |
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* | OpenAI | gpt-4o-mini | 500 | ≥25MB | ≥20000px |
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* | Gemini | gemini-2.5-flash | ~2000* | ≥40MB | 8000px |
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* | Mistral | pixtral-12b | 8 | ~15MB | 8000px |
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* | xAI | grok-2-vision | ≥100 | 25MB | 8000px |
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* | Groq | llama-4-scout-17b | 5 | ~5MB | ~5760px**|
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* | zAI | glm-4.5v | *** | ≥20MB | 8000px |
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* | OpenRouter | z-ai/glm-4.5v | *** | ~10MB | ≥20000px |
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*
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* REALISTIC PAYLOAD LIMITS (large images):
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* | Provider | Image Size | Max Count | Total Payload | Limit Hit |
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* |-------------|------------|-----------|---------------|---------------------|
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* | Anthropic | ~3MB | 6 | ~18MB | Request too large |
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* | OpenAI | ~15MB | 2 | ~30MB | Generic error |
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* | Gemini | ~20MB | 10 | ~200MB | String length |
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* | Mistral | ~10MB | 4 | ~40MB | 413 Payload too large|
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* | xAI | ~20MB | 1 | ~20MB | 413 Entity too large|
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* | Groq | 5760px | 5 | N/A | 5 image limit |
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* | zAI | ~15MB | 2 | ~30MB | 50MB request limit |
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* | OpenRouter | ~5MB | 2 | ~10MB | Provider error |
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*
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* Notes:
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* - Anthropic: 100 image hard limit, 5MB per image, but ~18MB total request
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* limit in practice (32MB documented but hit limit at ~24MB).
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* - OpenAI: 500 image limit but total payload limited to ~30-45MB.
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* - Gemini: * Very permissive. 10 x 20MB = 200MB worked!
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* - Mistral: 8 images max, ~40MB total payload.
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* - xAI: 25MB per image but strict request size limit (~20MB total).
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* - Groq: ** Most restrictive. 5 images max, 33177600 pixels max (≈5760x5760).
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* - zAI: 50MB request limit (explicit in error message).
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* - OpenRouter: *** Context-window limited (65536 tokens).
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*
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* ============================================================================
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* PRACTICAL RECOMMENDATIONS FOR CODING AGENTS:
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* ============================================================================
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*
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* Conservative cross-provider safe limits:
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* - Max 2 images per request at ~5MB each (~10MB total)
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* - Max 5760px dimension (for Groq pixel limit)
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*
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* If excluding Groq:
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* - Max 4 images per request at ~5MB each (~20MB total)
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* - Max 8000px dimension
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*
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* For Anthropic-only (most common case):
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* - Max 6 images at ~3MB each OR 100 images at <200KB each
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* - Max 5MB per image
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* - Max 8000px dimension
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* - Stay under ~18MB total request size
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*
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* ============================================================================
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*/
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import { execSync } from "node:child_process";
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import { mkdirSync, rmSync } from "node:fs";
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import { dirname, join } from "node:path";
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import { fileURLToPath } from "node:url";
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import { afterAll, beforeAll, describe, expect, it } from "vitest";
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import { getModel } from "../src/models.js";
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import { complete } from "../src/stream.js";
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import type { Api, Context, ImageContent, Model, OptionsForApi, UserMessage } from "../src/types.js";
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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// Temp directory for generated images
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const TEMP_DIR = join(__dirname, ".temp-images");
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/**
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* Generate a valid PNG image of specified dimensions using ImageMagick
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*/
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function generateImage(width: number, height: number, filename: string): string {
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const filepath = join(TEMP_DIR, filename);
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execSync(`magick -size ${width}x${height} xc:red "${filepath}"`, { stdio: "ignore" });
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const buffer = require("fs").readFileSync(filepath);
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return buffer.toString("base64");
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}
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/**
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* Generate a valid PNG image of approximately the specified size in bytes
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*/
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function generateImageWithSize(targetBytes: number, filename: string): string {
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const filepath = join(TEMP_DIR, filename);
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// Use uncompressed PNG to get predictable sizes
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// Each pixel is 3 bytes (RGB), plus PNG overhead (~100 bytes)
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// For a square image: side = sqrt(targetBytes / 3)
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const side = Math.ceil(Math.sqrt(targetBytes / 3));
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// Use noise pattern to prevent compression from shrinking the file
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execSync(`magick -size ${side}x${side} xc: +noise Random -depth 8 PNG24:"${filepath}"`, { stdio: "ignore" });
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// Check actual size and adjust if needed
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const stats = require("fs").statSync(filepath);
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if (stats.size < targetBytes * 0.8) {
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// If too small, increase dimensions
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const newSide = Math.ceil(side * Math.sqrt(targetBytes / stats.size));
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execSync(`magick -size ${newSide}x${newSide} xc: +noise Random -depth 8 PNG24:"${filepath}"`, {
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stdio: "ignore",
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});
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}
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const buffer = require("fs").readFileSync(filepath);
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return buffer.toString("base64");
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}
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/**
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* Create a user message with multiple images
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*/
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function createMultiImageMessage(imageCount: number, imageBase64: string): UserMessage {
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const content: (ImageContent | { type: "text"; text: string })[] = [
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{ type: "text", text: `I am sending you ${imageCount} images. Just reply with "received ${imageCount}".` },
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];
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for (let i = 0; i < imageCount; i++) {
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content.push({
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type: "image",
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data: imageBase64,
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mimeType: "image/png",
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});
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}
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return {
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role: "user",
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content,
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timestamp: Date.now(),
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};
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}
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/**
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* Test sending a specific number of images to a model
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*/
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async function testImageCount<TApi extends Api>(
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model: Model<TApi>,
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imageCount: number,
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imageBase64: string,
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options?: OptionsForApi<TApi>,
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): Promise<{ success: boolean; error?: string }> {
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const context: Context = {
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messages: [createMultiImageMessage(imageCount, imageBase64)],
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};
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try {
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const response = await complete(model, context, options);
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if (response.stopReason === "error") {
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return { success: false, error: response.errorMessage };
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}
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return { success: true };
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} catch (e) {
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return { success: false, error: e instanceof Error ? e.message : String(e) };
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}
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}
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/**
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* Test sending an image of a specific size
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*/
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async function testImageSize<TApi extends Api>(
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model: Model<TApi>,
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imageBase64: string,
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options?: OptionsForApi<TApi>,
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): Promise<{ success: boolean; error?: string }> {
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const context: Context = {
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messages: [
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{
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role: "user",
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content: [
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{ type: "text", text: "I am sending you an image. Just reply with 'received'." },
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{ type: "image", data: imageBase64, mimeType: "image/png" },
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],
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timestamp: Date.now(),
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},
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],
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};
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try {
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const response = await complete(model, context, options);
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if (response.stopReason === "error") {
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return { success: false, error: response.errorMessage };
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}
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return { success: true };
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} catch (e) {
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return { success: false, error: e instanceof Error ? e.message : String(e) };
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}
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}
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/**
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* Test sending an image with specific dimensions
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*/
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async function testImageDimensions<TApi extends Api>(
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model: Model<TApi>,
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imageBase64: string,
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options?: OptionsForApi<TApi>,
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): Promise<{ success: boolean; error?: string }> {
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const context: Context = {
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messages: [
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{
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role: "user",
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content: [
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{ type: "text", text: "I am sending you an image. Just reply with 'received'." },
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{ type: "image", data: imageBase64, mimeType: "image/png" },
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],
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timestamp: Date.now(),
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},
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],
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};
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try {
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const response = await complete(model, context, options);
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if (response.stopReason === "error") {
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return { success: false, error: response.errorMessage };
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}
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return { success: true };
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} catch (e) {
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return { success: false, error: e instanceof Error ? e.message : String(e) };
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}
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}
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/**
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* Find the maximum value that succeeds using linear search
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*/
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async function findLimit(
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testFn: (value: number) => Promise<{ success: boolean; error?: string }>,
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min: number,
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max: number,
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step: number,
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): Promise<{ limit: number; lastError?: string }> {
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let lastSuccess = min;
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let lastError: string | undefined;
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for (let value = min; value <= max; value += step) {
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console.log(` Testing value: ${value}...`);
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const result = await testFn(value);
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if (result.success) {
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lastSuccess = value;
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console.log(` SUCCESS`);
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} else {
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lastError = result.error;
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console.log(` FAILED: ${result.error?.substring(0, 100)}`);
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break;
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}
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}
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return { limit: lastSuccess, lastError };
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}
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// =============================================================================
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// Provider-specific test suites
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// =============================================================================
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describe("Image Limits E2E Tests", () => {
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let smallImage: string; // 100x100 for count tests
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beforeAll(() => {
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// Create temp directory
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mkdirSync(TEMP_DIR, { recursive: true });
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// Generate small test image for count tests
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smallImage = generateImage(100, 100, "small.png");
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});
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afterAll(() => {
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// Clean up temp directory
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rmSync(TEMP_DIR, { recursive: true, force: true });
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});
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// -------------------------------------------------------------------------
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// Anthropic (claude-3-5-haiku-20241022)
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// Limits: 100 images, 5MB per image, 8000px max dimension
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// -------------------------------------------------------------------------
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describe.skipIf(!process.env.ANTHROPIC_API_KEY)("Anthropic (claude-3-5-haiku-20241022)", () => {
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const model = getModel("anthropic", "claude-3-5-haiku-20241022");
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it("should accept a small number of images (5)", async () => {
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const result = await testImageCount(model, 5, smallImage);
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expect(result.success, result.error).toBe(true);
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});
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it("should find maximum image count limit", { timeout: 600000 }, async () => {
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// Known limit: 100 images
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const { limit, lastError } = await findLimit((count) => testImageCount(model, count, smallImage), 20, 120, 20);
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console.log(`\n Anthropic max images: ~${limit} (last error: ${lastError})`);
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expect(limit).toBeGreaterThanOrEqual(80);
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expect(limit).toBeLessThanOrEqual(100);
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});
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it("should find maximum image size limit", { timeout: 600000 }, async () => {
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const MB = 1024 * 1024;
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// Known limit: 5MB per image
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const sizes = [1, 2, 3, 4, 5, 6];
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let lastSuccess = 0;
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let lastError: string | undefined;
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for (const sizeMB of sizes) {
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console.log(` Testing size: ${sizeMB}MB...`);
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const imageBase64 = generateImageWithSize(sizeMB * MB, `size-${sizeMB}mb.png`);
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const result = await testImageSize(model, imageBase64);
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if (result.success) {
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lastSuccess = sizeMB;
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console.log(` SUCCESS`);
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} else {
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lastError = result.error;
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console.log(` FAILED: ${result.error?.substring(0, 100)}`);
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break;
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}
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}
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console.log(`\n Anthropic max image size: ~${lastSuccess}MB (last error: ${lastError})`);
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expect(lastSuccess).toBeGreaterThanOrEqual(1);
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});
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it("should find maximum image dimension limit", { timeout: 600000 }, async () => {
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// Known limit: 8000px
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const dimensions = [1000, 2000, 4000, 6000, 8000, 10000];
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let lastSuccess = 0;
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let lastError: string | undefined;
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for (const dim of dimensions) {
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console.log(` Testing dimension: ${dim}x${dim}...`);
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const imageBase64 = generateImage(dim, dim, `dim-${dim}.png`);
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const result = await testImageDimensions(model, imageBase64);
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if (result.success) {
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lastSuccess = dim;
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console.log(` SUCCESS`);
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} else {
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lastError = result.error;
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console.log(` FAILED: ${result.error?.substring(0, 100)}`);
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break;
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}
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}
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console.log(`\n Anthropic max dimension: ~${lastSuccess}px (last error: ${lastError})`);
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expect(lastSuccess).toBeGreaterThanOrEqual(6000);
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expect(lastSuccess).toBeLessThanOrEqual(8000);
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});
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});
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// -------------------------------------------------------------------------
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// OpenAI (gpt-4o-mini via openai-completions)
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// Limits: 500 images, ~20MB per image (documented)
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// -------------------------------------------------------------------------
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describe.skipIf(!process.env.OPENAI_API_KEY)("OpenAI (gpt-4o-mini)", () => {
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const model: Model<"openai-completions"> = { ...getModel("openai", "gpt-4o-mini"), api: "openai-completions" };
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it("should accept a small number of images (5)", async () => {
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const result = await testImageCount(model, 5, smallImage);
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expect(result.success, result.error).toBe(true);
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});
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it("should find maximum image count limit", { timeout: 600000 }, async () => {
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// Known limit: 500 images
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const { limit, lastError } = await findLimit(
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(count) => testImageCount(model, count, smallImage),
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100,
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600,
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100,
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);
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console.log(`\n OpenAI max images: ~${limit} (last error: ${lastError})`);
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expect(limit).toBeGreaterThanOrEqual(400);
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expect(limit).toBeLessThanOrEqual(500);
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});
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it("should find maximum image size limit", { timeout: 600000 }, async () => {
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const MB = 1024 * 1024;
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// Documented limit: 20MB
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const sizes = [5, 10, 15, 20, 25];
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let lastSuccess = 0;
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let lastError: string | undefined;
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for (const sizeMB of sizes) {
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console.log(` Testing size: ${sizeMB}MB...`);
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const imageBase64 = generateImageWithSize(sizeMB * MB, `size-${sizeMB}mb.png`);
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const result = await testImageSize(model, imageBase64);
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if (result.success) {
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lastSuccess = sizeMB;
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console.log(` SUCCESS`);
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} else {
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lastError = result.error;
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console.log(` FAILED: ${result.error?.substring(0, 100)}`);
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break;
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}
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}
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console.log(`\n OpenAI max image size: ~${lastSuccess}MB (last error: ${lastError})`);
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expect(lastSuccess).toBeGreaterThanOrEqual(15);
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});
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|
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it("should find maximum image dimension limit", { timeout: 600000 }, async () => {
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const dimensions = [2000, 4000, 8000, 16000, 20000];
|
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|
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let lastSuccess = 0;
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let lastError: string | undefined;
|
|
|
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for (const dim of dimensions) {
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console.log(` Testing dimension: ${dim}x${dim}...`);
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const imageBase64 = generateImage(dim, dim, `dim-${dim}.png`);
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const result = await testImageDimensions(model, imageBase64);
|
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if (result.success) {
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lastSuccess = dim;
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console.log(` SUCCESS`);
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} else {
|
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lastError = result.error;
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console.log(` FAILED: ${result.error?.substring(0, 100)}`);
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break;
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}
|
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}
|
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|
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console.log(`\n OpenAI max dimension: ~${lastSuccess}px (last error: ${lastError})`);
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expect(lastSuccess).toBeGreaterThanOrEqual(2000);
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});
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});
|
|
|
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// -------------------------------------------------------------------------
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// Google Gemini (gemini-2.5-flash)
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// Limits: Very high (~2500 images), large size support
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// -------------------------------------------------------------------------
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describe.skipIf(!process.env.GEMINI_API_KEY)("Gemini (gemini-2.5-flash)", () => {
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const model = getModel("google", "gemini-2.5-flash");
|
|
|
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it("should accept a small number of images (5)", async () => {
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const result = await testImageCount(model, 5, smallImage);
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expect(result.success, result.error).toBe(true);
|
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});
|
|
|
|
it("should find maximum image count limit", { timeout: 900000 }, async () => {
|
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// Known to work up to ~2500, hits errors around 3000
|
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const { limit, lastError } = await findLimit(
|
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(count) => testImageCount(model, count, smallImage),
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500,
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3000,
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500,
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);
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console.log(`\n Gemini max images: ~${limit} (last error: ${lastError})`);
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expect(limit).toBeGreaterThanOrEqual(500);
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});
|
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|
|
it("should find maximum image size limit", { timeout: 600000 }, async () => {
|
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const MB = 1024 * 1024;
|
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// Very permissive, tested up to 60MB successfully
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|
const sizes = [10, 20, 30, 40];
|
|
|
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let lastSuccess = 0;
|
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let lastError: string | undefined;
|
|
|
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for (const sizeMB of sizes) {
|
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console.log(` Testing size: ${sizeMB}MB...`);
|
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const imageBase64 = generateImageWithSize(sizeMB * MB, `size-${sizeMB}mb.png`);
|
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const result = await testImageSize(model, imageBase64);
|
|
if (result.success) {
|
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lastSuccess = sizeMB;
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console.log(` SUCCESS`);
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} else {
|
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lastError = result.error;
|
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console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
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break;
|
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}
|
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}
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|
|
console.log(`\n Gemini max image size: ~${lastSuccess}MB (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(20);
|
|
});
|
|
|
|
it("should find maximum image dimension limit", { timeout: 600000 }, async () => {
|
|
const dimensions = [2000, 4000, 8000, 16000, 20000];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const dim of dimensions) {
|
|
console.log(` Testing dimension: ${dim}x${dim}...`);
|
|
const imageBase64 = generateImage(dim, dim, `dim-${dim}.png`);
|
|
const result = await testImageDimensions(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = dim;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Gemini max dimension: ~${lastSuccess}px (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(2000);
|
|
});
|
|
});
|
|
|
|
// -------------------------------------------------------------------------
|
|
// Mistral (pixtral-12b)
|
|
// Limits: ~8 images, ~15MB per image
|
|
// -------------------------------------------------------------------------
|
|
describe.skipIf(!process.env.MISTRAL_API_KEY)("Mistral (pixtral-12b)", () => {
|
|
const model = getModel("mistral", "pixtral-12b");
|
|
|
|
it("should accept a small number of images (5)", async () => {
|
|
const result = await testImageCount(model, 5, smallImage);
|
|
expect(result.success, result.error).toBe(true);
|
|
});
|
|
|
|
it("should find maximum image count limit", { timeout: 600000 }, async () => {
|
|
// Known to fail around 9 images
|
|
const { limit, lastError } = await findLimit((count) => testImageCount(model, count, smallImage), 5, 15, 1);
|
|
console.log(`\n Mistral max images: ~${limit} (last error: ${lastError})`);
|
|
expect(limit).toBeGreaterThanOrEqual(5);
|
|
});
|
|
|
|
it("should find maximum image size limit", { timeout: 600000 }, async () => {
|
|
const MB = 1024 * 1024;
|
|
const sizes = [5, 10, 15, 20];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const sizeMB of sizes) {
|
|
console.log(` Testing size: ${sizeMB}MB...`);
|
|
const imageBase64 = generateImageWithSize(sizeMB * MB, `size-${sizeMB}mb.png`);
|
|
const result = await testImageSize(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = sizeMB;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Mistral max image size: ~${lastSuccess}MB (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(5);
|
|
});
|
|
|
|
it("should find maximum image dimension limit", { timeout: 600000 }, async () => {
|
|
const dimensions = [2000, 4000, 8000, 16000, 20000];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const dim of dimensions) {
|
|
console.log(` Testing dimension: ${dim}x${dim}...`);
|
|
const imageBase64 = generateImage(dim, dim, `dim-${dim}.png`);
|
|
const result = await testImageDimensions(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = dim;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Mistral max dimension: ~${lastSuccess}px (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(2000);
|
|
});
|
|
});
|
|
|
|
// -------------------------------------------------------------------------
|
|
// OpenRouter (z-ai/glm-4.5v)
|
|
// Limits: Context-window limited (~45 images at 100x100), ~15MB per image
|
|
// -------------------------------------------------------------------------
|
|
describe.skipIf(!process.env.OPENROUTER_API_KEY)("OpenRouter (z-ai/glm-4.5v)", () => {
|
|
const model = getModel("openrouter", "z-ai/glm-4.5v");
|
|
|
|
it("should accept a small number of images (5)", async () => {
|
|
const result = await testImageCount(model, 5, smallImage);
|
|
expect(result.success, result.error).toBe(true);
|
|
});
|
|
|
|
it("should find maximum image count limit", { timeout: 600000 }, async () => {
|
|
// Limited by context window, not explicit image limit
|
|
const { limit, lastError } = await findLimit((count) => testImageCount(model, count, smallImage), 10, 60, 10);
|
|
console.log(`\n OpenRouter max images: ~${limit} (last error: ${lastError})`);
|
|
expect(limit).toBeGreaterThanOrEqual(10);
|
|
});
|
|
|
|
it("should find maximum image size limit", { timeout: 600000 }, async () => {
|
|
const MB = 1024 * 1024;
|
|
const sizes = [5, 10, 15, 20];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const sizeMB of sizes) {
|
|
console.log(` Testing size: ${sizeMB}MB...`);
|
|
const imageBase64 = generateImageWithSize(sizeMB * MB, `size-${sizeMB}mb.png`);
|
|
const result = await testImageSize(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = sizeMB;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n OpenRouter max image size: ~${lastSuccess}MB (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(5);
|
|
});
|
|
|
|
it("should find maximum image dimension limit", { timeout: 600000 }, async () => {
|
|
const dimensions = [2000, 4000, 8000, 16000, 20000];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const dim of dimensions) {
|
|
console.log(` Testing dimension: ${dim}x${dim}...`);
|
|
const imageBase64 = generateImage(dim, dim, `dim-${dim}.png`);
|
|
const result = await testImageDimensions(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = dim;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n OpenRouter max dimension: ~${lastSuccess}px (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(2000);
|
|
});
|
|
});
|
|
|
|
// -------------------------------------------------------------------------
|
|
// xAI (grok-2-vision)
|
|
// -------------------------------------------------------------------------
|
|
describe.skipIf(!process.env.XAI_API_KEY)("xAI (grok-2-vision)", () => {
|
|
const model = getModel("xai", "grok-2-vision");
|
|
|
|
it("should accept a small number of images (5)", async () => {
|
|
const result = await testImageCount(model, 5, smallImage);
|
|
expect(result.success, result.error).toBe(true);
|
|
});
|
|
|
|
it("should find maximum image count limit", { timeout: 600000 }, async () => {
|
|
const { limit, lastError } = await findLimit((count) => testImageCount(model, count, smallImage), 10, 100, 10);
|
|
console.log(`\n xAI max images: ~${limit} (last error: ${lastError})`);
|
|
expect(limit).toBeGreaterThanOrEqual(5);
|
|
});
|
|
|
|
it("should find maximum image size limit", { timeout: 600000 }, async () => {
|
|
const MB = 1024 * 1024;
|
|
const sizes = [5, 10, 15, 20, 25];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const sizeMB of sizes) {
|
|
console.log(` Testing size: ${sizeMB}MB...`);
|
|
const imageBase64 = generateImageWithSize(sizeMB * MB, `size-${sizeMB}mb.png`);
|
|
const result = await testImageSize(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = sizeMB;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n xAI max image size: ~${lastSuccess}MB (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(5);
|
|
});
|
|
|
|
it("should find maximum image dimension limit", { timeout: 600000 }, async () => {
|
|
const dimensions = [2000, 4000, 8000, 16000, 20000];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const dim of dimensions) {
|
|
console.log(` Testing dimension: ${dim}x${dim}...`);
|
|
const imageBase64 = generateImage(dim, dim, `dim-${dim}.png`);
|
|
const result = await testImageDimensions(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = dim;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n xAI max dimension: ~${lastSuccess}px (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(2000);
|
|
});
|
|
});
|
|
|
|
// -------------------------------------------------------------------------
|
|
// Groq (llama-4-scout-17b)
|
|
// -------------------------------------------------------------------------
|
|
describe.skipIf(!process.env.GROQ_API_KEY)("Groq (llama-4-scout-17b)", () => {
|
|
const model = getModel("groq", "meta-llama/llama-4-scout-17b-16e-instruct");
|
|
|
|
it("should accept a small number of images (5)", async () => {
|
|
const result = await testImageCount(model, 5, smallImage);
|
|
expect(result.success, result.error).toBe(true);
|
|
});
|
|
|
|
it("should find maximum image count limit", { timeout: 600000 }, async () => {
|
|
const { limit, lastError } = await findLimit((count) => testImageCount(model, count, smallImage), 5, 50, 5);
|
|
console.log(`\n Groq max images: ~${limit} (last error: ${lastError})`);
|
|
expect(limit).toBeGreaterThanOrEqual(5);
|
|
});
|
|
|
|
it("should find maximum image size limit", { timeout: 600000 }, async () => {
|
|
const MB = 1024 * 1024;
|
|
const sizes = [1, 5, 10, 15, 20];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const sizeMB of sizes) {
|
|
console.log(` Testing size: ${sizeMB}MB...`);
|
|
const imageBase64 = generateImageWithSize(sizeMB * MB, `size-${sizeMB}mb.png`);
|
|
const result = await testImageSize(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = sizeMB;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Groq max image size: ~${lastSuccess}MB (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
});
|
|
|
|
it("should find maximum image dimension limit", { timeout: 600000 }, async () => {
|
|
const dimensions = [2000, 4000, 8000, 16000, 20000];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const dim of dimensions) {
|
|
console.log(` Testing dimension: ${dim}x${dim}...`);
|
|
const imageBase64 = generateImage(dim, dim, `dim-${dim}.png`);
|
|
const result = await testImageDimensions(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = dim;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Groq max dimension: ~${lastSuccess}px (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(2000);
|
|
});
|
|
});
|
|
|
|
// -------------------------------------------------------------------------
|
|
// zAI (glm-4.5v)
|
|
// -------------------------------------------------------------------------
|
|
describe.skipIf(!process.env.ZAI_API_KEY)("zAI (glm-4.5v)", () => {
|
|
const model = getModel("zai", "glm-4.5v");
|
|
|
|
it("should accept a small number of images (5)", async () => {
|
|
const result = await testImageCount(model, 5, smallImage);
|
|
expect(result.success, result.error).toBe(true);
|
|
});
|
|
|
|
it("should find maximum image count limit", { timeout: 600000 }, async () => {
|
|
const { limit, lastError } = await findLimit((count) => testImageCount(model, count, smallImage), 10, 100, 10);
|
|
console.log(`\n zAI max images: ~${limit} (last error: ${lastError})`);
|
|
expect(limit).toBeGreaterThanOrEqual(5);
|
|
});
|
|
|
|
it("should find maximum image size limit", { timeout: 600000 }, async () => {
|
|
const MB = 1024 * 1024;
|
|
const sizes = [5, 10, 15, 20];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const sizeMB of sizes) {
|
|
console.log(` Testing size: ${sizeMB}MB...`);
|
|
const imageBase64 = generateImageWithSize(sizeMB * MB, `size-${sizeMB}mb.png`);
|
|
const result = await testImageSize(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = sizeMB;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n zAI max image size: ~${lastSuccess}MB (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(5);
|
|
});
|
|
|
|
it("should find maximum image dimension limit", { timeout: 600000 }, async () => {
|
|
const dimensions = [2000, 4000, 8000, 16000, 20000];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const dim of dimensions) {
|
|
console.log(` Testing dimension: ${dim}x${dim}...`);
|
|
const imageBase64 = generateImage(dim, dim, `dim-${dim}.png`);
|
|
const result = await testImageDimensions(model, imageBase64);
|
|
if (result.success) {
|
|
lastSuccess = dim;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n zAI max dimension: ~${lastSuccess}px (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(2000);
|
|
});
|
|
});
|
|
|
|
// =========================================================================
|
|
// MAX SIZE IMAGES TEST
|
|
// =========================================================================
|
|
// Tests how many images at (or near) max allowed size each provider can handle.
|
|
// This tests realistic payload limits, not just image count with tiny files.
|
|
//
|
|
// Note: A real 8kx8k noise PNG is ~183MB (exceeds all provider limits).
|
|
// So we test with images sized near each provider's actual size limit.
|
|
// =========================================================================
|
|
|
|
describe("Max Size Images (realistic payload stress test)", () => {
|
|
// Generate images at specific sizes for each provider's limit
|
|
const imageCache: Map<number, string> = new Map();
|
|
|
|
function getImageAtSize(targetMB: number): string {
|
|
if (imageCache.has(targetMB)) {
|
|
return imageCache.get(targetMB)!;
|
|
}
|
|
console.log(` Generating ~${targetMB}MB noise image...`);
|
|
const imageBase64 = generateImageWithSize(targetMB * 1024 * 1024, `stress-${targetMB}mb.png`);
|
|
const actualSize = Buffer.from(imageBase64, "base64").length;
|
|
console.log(` Actual size: ${(actualSize / 1024 / 1024).toFixed(2)}MB`);
|
|
imageCache.set(targetMB, imageBase64);
|
|
return imageBase64;
|
|
}
|
|
|
|
// Anthropic - 5MB per image limit, 32MB total request, 100 image count
|
|
// Using 3MB to stay under 5MB limit (generateImageWithSize has overhead)
|
|
it.skipIf(!process.env.ANTHROPIC_API_KEY)(
|
|
"Anthropic: max ~3MB images before rejection",
|
|
{ timeout: 900000 },
|
|
async () => {
|
|
const model = getModel("anthropic", "claude-3-5-haiku-20241022");
|
|
const image3mb = getImageAtSize(3);
|
|
// 32MB total limit / ~4MB actual = ~8 images
|
|
const counts = [1, 2, 4, 6, 8, 10, 12];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x ~3MB images...`);
|
|
const result = await testImageCount(model, count, image3mb);
|
|
if (result.success) {
|
|
lastSuccess = count;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 150)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Anthropic max ~3MB images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
},
|
|
);
|
|
|
|
// OpenAI - 20MB per image documented, we found ≥25MB works
|
|
// Test with 15MB images to stay safely under limit
|
|
it.skipIf(!process.env.OPENAI_API_KEY)(
|
|
"OpenAI: max ~15MB images before rejection",
|
|
{ timeout: 1800000 },
|
|
async () => {
|
|
const model = getModel("openai", "gpt-4o-mini");
|
|
const image15mb = getImageAtSize(15);
|
|
// Test progressively
|
|
const counts = [1, 2, 5, 10, 20];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x ~15MB images...`);
|
|
const result = await testImageCount(model, count, image15mb);
|
|
if (result.success) {
|
|
lastSuccess = count;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 150)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n OpenAI max ~15MB images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
},
|
|
);
|
|
|
|
// Gemini - very permissive, ≥40MB per image works
|
|
// Test with 20MB images
|
|
it.skipIf(!process.env.GEMINI_API_KEY)(
|
|
"Gemini: max ~20MB images before rejection",
|
|
{ timeout: 1800000 },
|
|
async () => {
|
|
const model = getModel("google", "gemini-2.5-flash");
|
|
const image20mb = getImageAtSize(20);
|
|
// Test progressively
|
|
const counts = [1, 2, 5, 10, 20, 50];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x ~20MB images...`);
|
|
const result = await testImageCount(model, count, image20mb);
|
|
if (result.success) {
|
|
lastSuccess = count;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 150)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Gemini max ~20MB images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
},
|
|
);
|
|
|
|
// Mistral - 8 image limit, ~15MB per image
|
|
// Test with 10MB images (safely under limit)
|
|
it.skipIf(!process.env.MISTRAL_API_KEY)(
|
|
"Mistral: max ~10MB images before rejection",
|
|
{ timeout: 600000 },
|
|
async () => {
|
|
const model = getModel("mistral", "pixtral-12b");
|
|
const image10mb = getImageAtSize(10);
|
|
// Known limit is 8 images
|
|
const counts = [1, 2, 4, 6, 8, 9];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x ~10MB images...`);
|
|
const result = await testImageCount(model, count, image10mb);
|
|
if (result.success) {
|
|
lastSuccess = count;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 150)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Mistral max ~10MB images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
},
|
|
);
|
|
|
|
// xAI - 25MB per image limit (26214400 bytes exact)
|
|
// Test with 20MB images (safely under limit)
|
|
it.skipIf(!process.env.XAI_API_KEY)("xAI: max ~20MB images before rejection", { timeout: 1200000 }, async () => {
|
|
const model = getModel("xai", "grok-2-vision");
|
|
const image20mb = getImageAtSize(20);
|
|
// Test progressively
|
|
const counts = [1, 2, 5, 10, 20];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x ~20MB images...`);
|
|
const result = await testImageCount(model, count, image20mb);
|
|
if (result.success) {
|
|
lastSuccess = count;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 150)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n xAI max ~20MB images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
});
|
|
|
|
// Groq - very limited (5 images, ~5760px max due to 33M pixel limit)
|
|
// 8k images (64M pixels) exceed limit, so test with 5760px images instead
|
|
it.skipIf(!process.env.GROQ_API_KEY)(
|
|
"Groq: max 5760px images before rejection",
|
|
{ timeout: 600000 },
|
|
async () => {
|
|
const model = getModel("groq", "meta-llama/llama-4-scout-17b-16e-instruct");
|
|
// Generate 5760x5760 image (33177600 pixels = Groq's limit)
|
|
console.log(" Generating 5760x5760 test image for Groq...");
|
|
const image5760 = generateImage(5760, 5760, "stress-5760.png");
|
|
|
|
// Known limit is 5 images
|
|
const counts = [1, 2, 3, 4, 5, 6];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x 5760px images...`);
|
|
const result = await testImageCount(model, count, image5760);
|
|
if (result.success) {
|
|
lastSuccess = count;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 150)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Groq max 5760px images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
},
|
|
);
|
|
|
|
// zAI - ≥20MB per image, context-window limited (65k tokens)
|
|
// Test with 15MB images
|
|
it.skipIf(!process.env.ZAI_API_KEY)("zAI: max ~15MB images before rejection", { timeout: 1200000 }, async () => {
|
|
const model = getModel("zai", "glm-4.5v");
|
|
const image15mb = getImageAtSize(15);
|
|
// Context-limited, test progressively
|
|
const counts = [1, 2, 5, 10, 20];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x ~15MB images...`);
|
|
const result = await testImageCount(model, count, image15mb);
|
|
if (result.success) {
|
|
lastSuccess = count;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 150)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n zAI max ~15MB images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
});
|
|
|
|
// OpenRouter - ~10MB per image, context-window limited (65k tokens)
|
|
// Test with 5MB images (safer size)
|
|
it.skipIf(!process.env.OPENROUTER_API_KEY)(
|
|
"OpenRouter: max ~5MB images before rejection",
|
|
{ timeout: 900000 },
|
|
async () => {
|
|
const model = getModel("openrouter", "z-ai/glm-4.5v");
|
|
const image5mb = getImageAtSize(5);
|
|
// Context-limited, test progressively
|
|
const counts = [1, 2, 5, 10, 20];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x ~5MB images...`);
|
|
const result = await testImageCount(model, count, image5mb);
|
|
if (result.success) {
|
|
lastSuccess = count;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 150)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n OpenRouter max ~5MB images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
},
|
|
);
|
|
});
|
|
});
|