mirror of
https://github.com/getcompanion-ai/co-mono.git
synced 2026-04-20 22:02:38 +00:00
043a8416b0
Tested max 8kx8k images per provider: - Anthropic: 100 (explicit limit, fails at 101) - OpenAI: 100-200 (100 works, 200 times out) - Mistral: 8 (explicit limit, fails at 9) - xAI: 100-150 (100 works, 150 times out) - Groq: 0 (8k exceeds 33M pixel limit) - zAI: 400 (context window limited at 500) - OpenRouter: 40 (context window limited at 50) - Gemini: untested (no API key in test env) Key finding: Anthropic's 'many images' rule does NOT cause API errors. 100 x 8kx8k images work fine. Anthropic likely auto-resizes internally. Related to #120
1093 lines
38 KiB
TypeScript
1093 lines
38 KiB
TypeScript
/**
|
|
* Image limits test suite
|
|
*
|
|
* Tests provider-specific image limitations:
|
|
* - Maximum number of images in a context
|
|
* - Maximum image size (bytes)
|
|
* - Maximum image dimensions
|
|
* - Maximum 8k x 8k images (stress test)
|
|
*
|
|
* ============================================================================
|
|
* DISCOVERED LIMITS (Dec 2025):
|
|
* ============================================================================
|
|
*
|
|
* | Provider | Model | Max Images | Max Size | Max Dim | Max 8k Imgs |
|
|
* |-------------|--------------------|------------|----------|----------|-------------|
|
|
* | Anthropic | claude-3-5-haiku | 100 | 5MB | 8000px | 100 |
|
|
* | OpenAI | gpt-4o-mini | 500 | ≥25MB | ≥20000px | 100-200* |
|
|
* | Gemini | gemini-2.5-flash | ~2000** | ≥40MB | 8000px | (untested) |
|
|
* | Mistral | pixtral-12b | 8 | ~15MB | 8000px | 8 |
|
|
* | xAI | grok-2-vision | ≥100 | 25MB | 8000px | 100-150* |
|
|
* | Groq | llama-4-scout-17b | 5 | ~5MB | ~5760px | 0*** |
|
|
* | zAI | glm-4.5v | ≥100 | ≥20MB | 8000px | 400**** |
|
|
* | OpenRouter | z-ai/glm-4.5v | ~40**** | ~10MB | ≥20000px | 40**** |
|
|
*
|
|
* Notes:
|
|
* - Anthropic: Docs mention a "many images" rule (>20 images = 2000px max),
|
|
* but testing shows 100 x 8k images work fine. Anthropic may auto-resize
|
|
* internally. Total request size capped at 32MB. Explicit error at 101+.
|
|
* - OpenAI: * 100 x 8k succeeded, 200 x 8k failed with timeout. Actual limit
|
|
* likely between 100-200. Documented size limit is 20MB but ≥25MB works.
|
|
* - Gemini: ** Very permissive on count, hits rate limits before image limits.
|
|
* - Mistral: Very restrictive (8 images max). Explicit error at 9+.
|
|
* - xAI: * 100 x 8k succeeded, 150 x 8k timed out. 25MB size limit exact.
|
|
* - Groq: *** Most restrictive. 5 images max, 33177600 pixels max (≈5760x5760).
|
|
* 8k images (64M pixels) exceed limit, so 0 supported.
|
|
* - zAI: **** Context-window limited (65536 tokens). 400 x 8k succeeded,
|
|
* 500 x 8k exceeded token limit.
|
|
* - OpenRouter: **** Context-window limited (65536 tokens), not explicit
|
|
* image limit. 40 x 8k succeeded, 50 x 8k exceeded token limit.
|
|
*
|
|
* ============================================================================
|
|
* PRACTICAL RECOMMENDATIONS FOR CODING AGENTS:
|
|
* ============================================================================
|
|
*
|
|
* Conservative cross-provider safe limits:
|
|
* - Max 5 images per request (for Groq compatibility)
|
|
* - Max 5MB per image (for Anthropic/Groq)
|
|
* - Max 5760px dimension (for Groq pixel limit)
|
|
*
|
|
* If excluding Groq:
|
|
* - Max 8 images per request (for Mistral)
|
|
* - Max 5MB per image (for Anthropic)
|
|
* - Max 8000px dimension (common limit)
|
|
*
|
|
* For Anthropic-only (most common case):
|
|
* - Max 100 images per request
|
|
* - Max 5MB per image
|
|
* - Max 8000px dimension
|
|
* - Max 32MB total request size
|
|
*
|
|
* ============================================================================
|
|
*/
|
|
|
|
import { execSync } from "node:child_process";
|
|
import { mkdirSync, rmSync } from "node:fs";
|
|
import { dirname, join } from "node:path";
|
|
import { fileURLToPath } from "node:url";
|
|
import { afterAll, beforeAll, describe, expect, it } from "vitest";
|
|
import { getModel } from "../src/models.js";
|
|
import { complete } from "../src/stream.js";
|
|
import type { Api, Context, ImageContent, Model, OptionsForApi, UserMessage } from "../src/types.js";
|
|
|
|
const __filename = fileURLToPath(import.meta.url);
|
|
const __dirname = dirname(__filename);
|
|
|
|
// Temp directory for generated images
|
|
const TEMP_DIR = join(__dirname, ".temp-images");
|
|
|
|
/**
|
|
* Generate a valid PNG image of specified dimensions using ImageMagick
|
|
*/
|
|
function generateImage(width: number, height: number, filename: string): string {
|
|
const filepath = join(TEMP_DIR, filename);
|
|
execSync(`magick -size ${width}x${height} xc:red "${filepath}"`, { stdio: "ignore" });
|
|
const buffer = require("fs").readFileSync(filepath);
|
|
return buffer.toString("base64");
|
|
}
|
|
|
|
/**
|
|
* Generate a valid PNG image of approximately the specified size in bytes
|
|
*/
|
|
function generateImageWithSize(targetBytes: number, filename: string): string {
|
|
const filepath = join(TEMP_DIR, filename);
|
|
// Use uncompressed PNG to get predictable sizes
|
|
// Each pixel is 3 bytes (RGB), plus PNG overhead (~100 bytes)
|
|
// For a square image: side = sqrt(targetBytes / 3)
|
|
const side = Math.ceil(Math.sqrt(targetBytes / 3));
|
|
// Use noise pattern to prevent compression from shrinking the file
|
|
execSync(`magick -size ${side}x${side} xc: +noise Random -depth 8 PNG24:"${filepath}"`, { stdio: "ignore" });
|
|
|
|
// Check actual size and adjust if needed
|
|
const stats = require("fs").statSync(filepath);
|
|
if (stats.size < targetBytes * 0.8) {
|
|
// If too small, increase dimensions
|
|
const newSide = Math.ceil(side * Math.sqrt(targetBytes / stats.size));
|
|
execSync(`magick -size ${newSide}x${newSide} xc: +noise Random -depth 8 PNG24:"${filepath}"`, {
|
|
stdio: "ignore",
|
|
});
|
|
}
|
|
|
|
const buffer = require("fs").readFileSync(filepath);
|
|
return buffer.toString("base64");
|
|
}
|
|
|
|
/**
|
|
* Create a user message with multiple images
|
|
*/
|
|
function createMultiImageMessage(imageCount: number, imageBase64: string): UserMessage {
|
|
const content: (ImageContent | { type: "text"; text: string })[] = [
|
|
{ type: "text", text: `I am sending you ${imageCount} images. Just reply with "received ${imageCount}".` },
|
|
];
|
|
|
|
for (let i = 0; i < imageCount; i++) {
|
|
content.push({
|
|
type: "image",
|
|
data: imageBase64,
|
|
mimeType: "image/png",
|
|
});
|
|
}
|
|
|
|
return {
|
|
role: "user",
|
|
content,
|
|
timestamp: Date.now(),
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Test sending a specific number of images to a model
|
|
*/
|
|
async function testImageCount<TApi extends Api>(
|
|
model: Model<TApi>,
|
|
imageCount: number,
|
|
imageBase64: string,
|
|
options?: OptionsForApi<TApi>,
|
|
): Promise<{ success: boolean; error?: string }> {
|
|
const context: Context = {
|
|
messages: [createMultiImageMessage(imageCount, imageBase64)],
|
|
};
|
|
|
|
try {
|
|
const response = await complete(model, context, options);
|
|
if (response.stopReason === "error") {
|
|
return { success: false, error: response.errorMessage };
|
|
}
|
|
return { success: true };
|
|
} catch (e) {
|
|
return { success: false, error: e instanceof Error ? e.message : String(e) };
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Test sending an image of a specific size
|
|
*/
|
|
async function testImageSize<TApi extends Api>(
|
|
model: Model<TApi>,
|
|
imageBase64: string,
|
|
options?: OptionsForApi<TApi>,
|
|
): Promise<{ success: boolean; error?: string }> {
|
|
const context: Context = {
|
|
messages: [
|
|
{
|
|
role: "user",
|
|
content: [
|
|
{ type: "text", text: "I am sending you an image. Just reply with 'received'." },
|
|
{ type: "image", data: imageBase64, mimeType: "image/png" },
|
|
],
|
|
timestamp: Date.now(),
|
|
},
|
|
],
|
|
};
|
|
|
|
try {
|
|
const response = await complete(model, context, options);
|
|
if (response.stopReason === "error") {
|
|
return { success: false, error: response.errorMessage };
|
|
}
|
|
return { success: true };
|
|
} catch (e) {
|
|
return { success: false, error: e instanceof Error ? e.message : String(e) };
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Test sending an image with specific dimensions
|
|
*/
|
|
async function testImageDimensions<TApi extends Api>(
|
|
model: Model<TApi>,
|
|
imageBase64: string,
|
|
options?: OptionsForApi<TApi>,
|
|
): Promise<{ success: boolean; error?: string }> {
|
|
const context: Context = {
|
|
messages: [
|
|
{
|
|
role: "user",
|
|
content: [
|
|
{ type: "text", text: "I am sending you an image. Just reply with 'received'." },
|
|
{ type: "image", data: imageBase64, mimeType: "image/png" },
|
|
],
|
|
timestamp: Date.now(),
|
|
},
|
|
],
|
|
};
|
|
|
|
try {
|
|
const response = await complete(model, context, options);
|
|
if (response.stopReason === "error") {
|
|
return { success: false, error: response.errorMessage };
|
|
}
|
|
return { success: true };
|
|
} catch (e) {
|
|
return { success: false, error: e instanceof Error ? e.message : String(e) };
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Find the maximum value that succeeds using linear search
|
|
*/
|
|
async function findLimit(
|
|
testFn: (value: number) => Promise<{ success: boolean; error?: string }>,
|
|
min: number,
|
|
max: number,
|
|
step: number,
|
|
): Promise<{ limit: number; lastError?: string }> {
|
|
let lastSuccess = min;
|
|
let lastError: string | undefined;
|
|
|
|
for (let value = min; value <= max; value += step) {
|
|
console.log(` Testing value: ${value}...`);
|
|
const result = await testFn(value);
|
|
if (result.success) {
|
|
lastSuccess = value;
|
|
console.log(` SUCCESS`);
|
|
} else {
|
|
lastError = result.error;
|
|
console.log(` FAILED: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
return { limit: lastSuccess, lastError };
|
|
}
|
|
|
|
// =============================================================================
|
|
// Provider-specific test suites
|
|
// =============================================================================
|
|
|
|
describe("Image Limits E2E Tests", () => {
|
|
let smallImage: string; // 100x100 for count tests
|
|
|
|
beforeAll(() => {
|
|
// Create temp directory
|
|
mkdirSync(TEMP_DIR, { recursive: true });
|
|
|
|
// Generate small test image for count tests
|
|
smallImage = generateImage(100, 100, "small.png");
|
|
});
|
|
|
|
afterAll(() => {
|
|
// Clean up temp directory
|
|
rmSync(TEMP_DIR, { recursive: true, force: true });
|
|
});
|
|
|
|
// -------------------------------------------------------------------------
|
|
// Anthropic (claude-3-5-haiku-20241022)
|
|
// Limits: 100 images, 5MB per image, 8000px max dimension
|
|
// -------------------------------------------------------------------------
|
|
describe.skipIf(!process.env.ANTHROPIC_API_KEY)("Anthropic (claude-3-5-haiku-20241022)", () => {
|
|
const model = getModel("anthropic", "claude-3-5-haiku-20241022");
|
|
|
|
it("should accept a small number of images (5)", async () => {
|
|
const result = await testImageCount(model, 5, smallImage);
|
|
expect(result.success, result.error).toBe(true);
|
|
});
|
|
|
|
it("should find maximum image count limit", { timeout: 600000 }, async () => {
|
|
// Known limit: 100 images
|
|
const { limit, lastError } = await findLimit((count) => testImageCount(model, count, smallImage), 20, 120, 20);
|
|
console.log(`\n Anthropic max images: ~${limit} (last error: ${lastError})`);
|
|
expect(limit).toBeGreaterThanOrEqual(80);
|
|
expect(limit).toBeLessThanOrEqual(100);
|
|
});
|
|
|
|
it("should find maximum image size limit", { timeout: 600000 }, async () => {
|
|
const MB = 1024 * 1024;
|
|
// Known limit: 5MB per image
|
|
const sizes = [1, 2, 3, 4, 5, 6];
|
|
|
|
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: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Anthropic max image size: ~${lastSuccess}MB (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
});
|
|
|
|
it("should find maximum image dimension limit", { timeout: 600000 }, async () => {
|
|
// Known limit: 8000px
|
|
const dimensions = [1000, 2000, 4000, 6000, 8000, 10000];
|
|
|
|
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: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Anthropic max dimension: ~${lastSuccess}px (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(6000);
|
|
expect(lastSuccess).toBeLessThanOrEqual(8000);
|
|
});
|
|
});
|
|
|
|
// -------------------------------------------------------------------------
|
|
// OpenAI (gpt-4o-mini via openai-completions)
|
|
// Limits: 500 images, ~20MB per image (documented)
|
|
// -------------------------------------------------------------------------
|
|
describe.skipIf(!process.env.OPENAI_API_KEY)("OpenAI (gpt-4o-mini)", () => {
|
|
const model: Model<"openai-completions"> = { ...getModel("openai", "gpt-4o-mini"), api: "openai-completions" };
|
|
|
|
it("should accept a small number of images (5)", async () => {
|
|
const result = await testImageCount(model, 5, smallImage);
|
|
expect(result.success, result.error).toBe(true);
|
|
});
|
|
|
|
it("should find maximum image count limit", { timeout: 600000 }, async () => {
|
|
// Known limit: 500 images
|
|
const { limit, lastError } = await findLimit(
|
|
(count) => testImageCount(model, count, smallImage),
|
|
100,
|
|
600,
|
|
100,
|
|
);
|
|
console.log(`\n OpenAI max images: ~${limit} (last error: ${lastError})`);
|
|
expect(limit).toBeGreaterThanOrEqual(400);
|
|
expect(limit).toBeLessThanOrEqual(500);
|
|
});
|
|
|
|
it("should find maximum image size limit", { timeout: 600000 }, async () => {
|
|
const MB = 1024 * 1024;
|
|
// Documented limit: 20MB
|
|
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: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n OpenAI max image size: ~${lastSuccess}MB (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(15);
|
|
});
|
|
|
|
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: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n OpenAI max dimension: ~${lastSuccess}px (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(2000);
|
|
});
|
|
});
|
|
|
|
// -------------------------------------------------------------------------
|
|
// Google Gemini (gemini-2.5-flash)
|
|
// Limits: Very high (~2500 images), large size support
|
|
// -------------------------------------------------------------------------
|
|
describe.skipIf(!process.env.GEMINI_API_KEY)("Gemini (gemini-2.5-flash)", () => {
|
|
const model = getModel("google", "gemini-2.5-flash");
|
|
|
|
it("should accept a small number of images (5)", async () => {
|
|
const result = await testImageCount(model, 5, smallImage);
|
|
expect(result.success, result.error).toBe(true);
|
|
});
|
|
|
|
it("should find maximum image count limit", { timeout: 900000 }, async () => {
|
|
// Known to work up to ~2500, hits errors around 3000
|
|
const { limit, lastError } = await findLimit(
|
|
(count) => testImageCount(model, count, smallImage),
|
|
500,
|
|
3000,
|
|
500,
|
|
);
|
|
console.log(`\n Gemini max images: ~${limit} (last error: ${lastError})`);
|
|
expect(limit).toBeGreaterThanOrEqual(500);
|
|
});
|
|
|
|
it("should find maximum image size limit", { timeout: 600000 }, async () => {
|
|
const MB = 1024 * 1024;
|
|
// Very permissive, tested up to 60MB successfully
|
|
const sizes = [10, 20, 30, 40];
|
|
|
|
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: ${result.error?.substring(0, 100)}`);
|
|
break;
|
|
}
|
|
}
|
|
|
|
console.log(`\n Gemini max image size: ~${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 8K IMAGES TEST
|
|
// =========================================================================
|
|
// Tests how many 8000x8000 images each provider can handle.
|
|
// This is important for:
|
|
// 1. Reproducing Anthropic's "many images" rule (>20 images = 2000px max)
|
|
// 2. Finding practical limits for prompt caching optimization
|
|
// =========================================================================
|
|
|
|
describe("Max 8K Images (large image stress test)", () => {
|
|
// Generate a single 8k image to reuse
|
|
// Note: solid color compresses well but still has 8000x8000 pixel dimensions
|
|
let image8k: string;
|
|
|
|
beforeAll(() => {
|
|
console.log("Generating 8000x8000 test image...");
|
|
image8k = generateImage(8000, 8000, "stress-8k.png");
|
|
const sizeBytes = Buffer.from(image8k, "base64").length;
|
|
console.log(
|
|
` 8k image size: ${(sizeBytes / 1024 / 1024).toFixed(2)}MB (compressed, but still 8000x8000 dimensions)`,
|
|
);
|
|
});
|
|
|
|
// Anthropic - known 100 image limit, testing if 8k dimensions change this
|
|
it.skipIf(!process.env.ANTHROPIC_API_KEY)(
|
|
"Anthropic: max 8k images before rejection",
|
|
{ timeout: 900000 },
|
|
async () => {
|
|
const model = getModel("anthropic", "claude-3-5-haiku-20241022");
|
|
// Known limit is 100 images - test around that boundary
|
|
const counts = [10, 20, 50, 80, 100, 110, 120];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x 8k images...`);
|
|
const result = await testImageCount(model, count, image8k);
|
|
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 8k images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(5);
|
|
},
|
|
);
|
|
|
|
// OpenAI - known 500 image limit
|
|
it.skipIf(!process.env.OPENAI_API_KEY)(
|
|
"OpenAI: max 8k images before rejection",
|
|
{ timeout: 1800000 },
|
|
async () => {
|
|
const model = getModel("openai", "gpt-4o-mini");
|
|
// Known limit is 500 images - test around that boundary
|
|
const counts = [50, 100, 200, 300, 400, 500, 550];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x 8k images...`);
|
|
const result = await testImageCount(model, count, image8k);
|
|
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 8k images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(5);
|
|
},
|
|
);
|
|
|
|
// Gemini - known to be very permissive (~2000+ small images), but 8k may differ
|
|
it.skipIf(!process.env.GOOGLE_API_KEY)(
|
|
"Gemini: max 8k images before rejection",
|
|
{ timeout: 1800000 },
|
|
async () => {
|
|
const model = getModel("google", "gemini-2.5-flash");
|
|
// Test progressively - 8k images are large so limit may be lower
|
|
const counts = [10, 50, 100, 200, 500, 1000];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x 8k images...`);
|
|
const result = await testImageCount(model, count, image8k);
|
|
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 8k images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(5);
|
|
},
|
|
);
|
|
|
|
// Mistral - known 8 image limit
|
|
it.skipIf(!process.env.MISTRAL_API_KEY)(
|
|
"Mistral: max 8k images before rejection",
|
|
{ timeout: 600000 },
|
|
async () => {
|
|
const model = getModel("mistral", "pixtral-12b");
|
|
// Known limit is 8 images - test around that boundary
|
|
const counts = [1, 2, 4, 6, 8, 9, 10];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x 8k images...`);
|
|
const result = await testImageCount(model, count, image8k);
|
|
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 8k images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
},
|
|
);
|
|
|
|
// xAI - tested up to 100 small images successfully
|
|
it.skipIf(!process.env.XAI_API_KEY)("xAI: max 8k images before rejection", { timeout: 1200000 }, async () => {
|
|
const model = getModel("xai", "grok-2-vision");
|
|
// Test around the expected boundary
|
|
const counts = [10, 50, 100, 150, 200];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x 8k images...`);
|
|
const result = await testImageCount(model, count, image8k);
|
|
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 8k images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(5);
|
|
});
|
|
|
|
// Groq - very limited (5 images, ~5760px max)
|
|
it.skipIf(!process.env.GROQ_API_KEY)(
|
|
"Groq: max 8k images before rejection (expect 0 - exceeds pixel limit)",
|
|
{ timeout: 600000 },
|
|
async () => {
|
|
const model = getModel("groq", "meta-llama/llama-4-scout-17b-16e-instruct");
|
|
// 8k images exceed Groq's 33177600 pixel limit, so even 1 should fail
|
|
const counts = [1, 2, 3];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x 8k images...`);
|
|
const result = await testImageCount(model, count, image8k);
|
|
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 8k images: ${lastSuccess} (last error: ${lastError})`);
|
|
// Groq should fail even with 1 image at 8k (64M pixels > 33M limit)
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(0);
|
|
},
|
|
);
|
|
|
|
// zAI - tested up to 100 small images successfully, very permissive
|
|
it.skipIf(!process.env.ZAI_API_KEY)("zAI: max 8k images before rejection", { timeout: 1800000 }, async () => {
|
|
const model = getModel("zai", "glm-4.5v");
|
|
// Very permissive - extend to find actual limit
|
|
const counts = [50, 100, 200, 300, 400, 500];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x 8k images...`);
|
|
const result = await testImageCount(model, count, image8k);
|
|
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 8k images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(5);
|
|
});
|
|
|
|
// OpenRouter - context-window limited (~40 small images), 8k will be fewer
|
|
it.skipIf(!process.env.OPENROUTER_API_KEY)(
|
|
"OpenRouter: max 8k images before rejection",
|
|
{ timeout: 900000 },
|
|
async () => {
|
|
const model = getModel("openrouter", "z-ai/glm-4.5v");
|
|
// 8k images consume more tokens, so limit will be lower than 40
|
|
const counts = [1, 2, 5, 10, 20, 30, 40, 50];
|
|
|
|
let lastSuccess = 0;
|
|
let lastError: string | undefined;
|
|
|
|
for (const count of counts) {
|
|
console.log(` Testing ${count} x 8k images...`);
|
|
const result = await testImageCount(model, count, image8k);
|
|
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 8k images: ${lastSuccess} (last error: ${lastError})`);
|
|
expect(lastSuccess).toBeGreaterThanOrEqual(1);
|
|
},
|
|
);
|
|
});
|
|
});
|