Best LLM Endpoint for MMLU
Compare hosted LLM endpoints on MMLU using public Benchscope runs. MMLU measures broad academic and professional knowledge across 57 subjects and 14,042 multiple-choice questions. Use this page to see which endpoint has the best MMLU score, which is fastest, and which offers the best quality-latency tradeoff for benchmark-driven endpoint selection.
Current Recommendation
Based on completed canonical-prompt public runs. Partial and custom-prompt runs are excluded from winner claims.
Top MMLU Scores — Canonical Prompt, Public Runs
| # | Endpoint | Provider | Model | Score | p50 Latency | Samples |
|---|---|---|---|---|---|---|
| 1 | 4router.net | 4router.net | Custom | 100.0% | 2748 ms | 1 |
| 2 | Cerebras / Qwen 3 235B A22B Instruct | Cerebras | Qwen 3 235B A22B Instruct | 90.0% | 199 ms | 20 |
| 3 | Groq / Llama 3.3 70B | Groq | Llama 3.3 70B | 85.0% | 120 ms | 100 |
| 4 | Together / Llama 3.3 70B | Together AI | Llama 3.3 70B | 85.0% | 885 ms | 100 |
| 5 | Groq / GPT-OSS 120B | Groq | GPT-OSS 120B | 70.0% | 379 ms | 20 |
| 6 | Groq / Qwen3 32B | Groq | Qwen3 32B | 70.0% | 1285 ms | 20 |
| 7 | Groq / Llama 3.1 8B | Groq | Llama 3.1 8B | 45.0% | 103 ms | 20 |
| 8 | 4router.net (partial) | 4router.net | Custom | 92.5% | 3192 ms | 14,042 |
| 9 | Cerebras / Qwen 3 235B A22B Instruct (partial) | Cerebras | Qwen 3 235B A22B Instruct | 88.7% | 3872 ms | 100 |
Canonical-prompt completed public runs, sorted by score. Explore all MMLU runs →
What MMLU Measures
MMLU (Massive Multitask Language Understanding) presents 14,042 four-choice questions across 57 subjects spanning STEM, humanities, social sciences, and professional domains. Benchscope extracts the first letter from the model output and scores it as correct or incorrect. This comparison uses canonical-prompt runs only — results are directly comparable across providers and model families. MMLU is best interpreted as a broad knowledge and exam-style benchmark. It is useful for detecting whether a hosted endpoint preserves the underlying model family's general knowledge performance. It should not be used alone to choose a model for instruction following, coding, tool use, or long-form generation.
How to Interpret MMLU Results
Frontier models cluster above 85%, limiting MMLU's ability to differentiate top-tier endpoints. MMLU is most informative when comparing mid-tier models or confirming that a provider's quantization has not degraded factual knowledge. When the same model family is hosted by different providers, score differences reflect infrastructure — not model capability. Benchscope compares endpoints, not just model names. The same model family can behave differently across providers because of serving stack, quantization, rate limits, and inference settings.
Caveats
MMLU has known contamination risk — questions may have appeared in model pretraining data, so high scores partly reflect memorization. MMLU does not test generation quality, instruction following, or compositional reasoning. For reasoning-intensive comparisons, see the MATH and IFEval comparison pages. Use MMLU to check broad knowledge and provider-hosting effects, then pair it with instruction-following, reasoning, and latency-sensitive benchmarks before choosing an endpoint.
MMLU Benchmark Latency
Search demand for MMLU endpoint selection often includes latency. Benchscope reports p50 latency next to score so you can identify the best LLM endpoint for MMLU by both accuracy and speed. This matters for high-volume classification, routing, grading, and evaluation workloads where small latency differences compound across many requests.
Best Accuracy, Best Latency, and Best Tradeoff
Do not choose an endpoint from score alone. Use the ranked public runs to identify the highest MMLU score, then compare latency and sample count. When two endpoints are close in score, the lower-latency endpoint is usually the better production candidate unless your use case is accuracy-critical.
How to Choose an Endpoint for MMLU
- Choose the highest-scoring endpoint if you care about broad academic knowledge coverage.
- Choose the fastest endpoint if you need low-latency MMLU benchmark, classification, or routing workloads.
- Choose the lowest-cost endpoint if your task uses many short prompts.
- When comparing the same model family across providers, prioritize canonical-prompt runs with the same sample scope.
- Pair MMLU with MATH or IFEval before choosing an endpoint — MMLU alone is not sufficient for frontier models.
Frequently Asked Questions
What is the best LLM endpoint for MMLU?
The best MMLU endpoint changes as providers update their deployments. See the rankings table above for the current top-scoring canonical-prompt run on Benchscope. Completed runs sorted by score are the most reliable indicator.
What does MMLU measure?
MMLU (Massive Multitask Language Understanding) tests broad academic and professional knowledge across 57 subjects and 14,042 multiple-choice questions. It measures factual knowledge recall, not generation quality, instruction following, or reasoning. See all MMLU runs on Benchscope.
Why can the same model score differently on Groq, Together, Fireworks, or Cerebras?
Provider-hosted endpoints differ in quantization level, serving infrastructure, batching strategy, and configuration. These differences can affect accuracy. See the Llama 3.3 70B Groq vs Together AI comparison for a worked example.
Is MMLU still useful for frontier models?
Frontier models cluster tightly above 85% on MMLU, which limits how much it can differentiate top-tier endpoints. MMLU is most informative for mid-tier model comparisons or for checking whether a provider's hosting has degraded general knowledge. For harder comparisons, see MATH.
Are Benchscope MMLU runs comparable?
Canonical-prompt runs with matching sample sizes are directly comparable. Custom-prompt runs and partial runs require caution. Each run is labeled with prompt mode, sample size, and status. Read the methodology for full comparability rules.
How does Benchscope score MMLU?
Benchscope extracts the first letter from the model output and checks it against the correct answer (A, B, C, or D). The primary score is the fraction of questions answered correctly. Read the full scoring methodology.
Related
- MMLU benchmark results — full table with all providers and filters
- Public MMLU canonical runs on Benchscope
- Best LLM endpoint for MATH — competition-level reasoning
- Best LLM endpoint for GSM8K — grade school math reasoning
- Best LLM endpoint for IFEval — instruction-following compliance
- Best LLM endpoint for MuSR — multi-step compositional reasoning
- Llama 3.3 70B on Groq vs Together AI
- Groq benchmark results
- Together AI benchmark results
- Cerebras benchmark results
- All model families on Benchscope
- How MMLU scoring and comparability work
Evaluate Your Endpoint
Benchscope is free to use. Run MMLU on your own hosted endpoint and make your results public to appear in this comparison.
Run this benchmark on your endpoint → · Explore public MMLU runs
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