LLM Quality Benchmark 🧪

Test any OpenAI-compatible LLM endpoint with 25 automated quality checks — reasoning, coding, multilingual, structured output, tool calling and more.

Start Testing
LLM TestBench
by Trooper.AI — 25 Quality Tests (5 parallel)
Connection
Use a different model to evaluate responses. Leave empty to self-judge.
Tests use the same model for generation and judging (LLM-as-Judge). Results are indicative, not absolute. API must be OpenAI-compatible and allow CORS.
# Test Category Score St
◇
Configure your endpoint and run tests
25 tests covering reasoning, coding, tools, multilingual, and more

What Is an LLM Quality Benchmark?

An LLM quality benchmark is a standardised set of tests designed to evaluate how well a large language model (LLM) performs across diverse real-world tasks. Rather than relying on a single metric like perplexity, a quality benchmark probes multiple dimensions — reasoning, instruction following, coding ability, multilingual fluency, structured output, and tool use — to produce a holistic performance profile.

Our free LLM TestBench runs 25 parallel tests directly in your browser against any OpenAI-compatible API endpoint. The model itself acts as judge (LLM-as-Judge paradigm), scoring each response on a 0–10 scale. This makes it easy to compare different models, providers, or quantisation levels side by side — without any server-side setup.

Why Benchmark Your LLM?

Choosing the right AI model for your workload is crucial. Running a benchmark helps you:

  • Compare models objectively — see how GPT-4, Llama 3, Mistral, Qwen, or any other model ranks on the same tests.
  • Validate inference providers — verify that your hosted endpoint delivers the same quality as the original model weights.
  • Detect regressions — re-run the benchmark after model updates to catch quality drops early.
  • Evaluate quantisation trade-offs — understand how GPTQ, AWQ, or GGUF quantisation affects output quality.
  • Test before production — make data-driven decisions before deploying a model into a customer-facing application.

The 25 Tests Explained

The benchmark covers 7 categories that reflect real production demands:

Text

Basic Q&A, summarization, and creative writing assess fluency, conciseness, and format adherence.

Instructions

ALL-CAPS formatting, character persona adherence, and edge-case honesty test how strictly the model follows system-level constraints.

Multilingual

German, French, and translation tests measure linguistic correctness and cultural awareness across languages.

Structured Output

JSON generation and markdown tables check whether the model can produce machine-parseable output reliably.

Reasoning

From syllogisms and trick questions to the Birthday Paradox and arithmetic, these tests cover easy, medium, hard, and multi-step reasoning.

Coding

Python iteration, JavaScript closures, and bug detection evaluate code generation and review capabilities.

Tool Calling

A function-call test with a weather tool verifies that the model can format structured tool-use requests as expected by modern agent frameworks.

How It Works

  1. Enter your API credentials — endpoint URL, model name, and API key. Your key stays in the browser and is never sent to our servers.
  2. Click "Run All Tests" — the benchmark sends each test prompt to the model, collects the response, then uses the same model to judge the answer.
  3. Review scores — expand any row to see the prompt, expected answer, model response, and the judge's reasoning.

The entire benchmark typically completes in 2–5 minutes depending on model speed. All traffic goes directly from your browser to the API endpoint — nothing passes through Trooper.AI servers.

Run Your LLM on Trooper.AI GPU Servers

Need a fast, EU-hosted GPU to serve your own model? Rent a GPU server from Trooper.AI and deploy any open-source LLM in minutes. All servers are GDPR-compliant, come with root access, and support popular inference frameworks like vLLM, TGI, and Ollama out of the box.

After deployment, point this benchmark at your server's endpoint and verify quality instantly — it's the fastest way to validate that your self-hosted LLM meets production standards.

Frequently Asked Questions

Yes, the benchmark is completely free. The only cost is the API usage on your endpoint — each run consumes roughly 50 API calls (25 generate + 25 judge).

Your API key never leaves your browser. All requests are made directly from the client to your API endpoint via HTTPS. We do not store, log, or transmit your key.

Any API that implements the /v1/chat/completions endpoint with standard OpenAI request/response format. This includes OpenAI, Trooper.AI Router, vLLM, TGI, Ollama (with OpenAI compatibility layer), Together AI, Groq, and many more. The endpoint must allow CORS from your browser.

Using the same model as judge (LLM-as-Judge) keeps the benchmark simple and self-contained — no additional API keys or external services required. While self-judging can introduce bias, research shows it correlates well with human evaluation for most tasks. For higher-stakes evaluations, consider using a stronger judge model.

A score of 8+/10 on average indicates strong overall quality. Scores between 5–7 suggest the model handles most tasks but struggles with harder reasoning or strict instruction following. Below 5, the model may not be suitable for production use. Top-tier models like GPT-4o or Claude 3.5 Sonnet typically score 8.5+ across all categories.

Rent a GPU Server GPU Benchmarks