LLM Leaderboard & AI Benchmarks (2026)
Frontier models compared on the benchmarks that matter — plus the practical AI-QA lens to read them with.
| Model | Vendor | MMLU | GSM8K | HumanEval | Notes |
|---|---|---|---|---|---|
| GPT-4o | OpenAI | 88.7 | 95.8 | 90.2 | Strong all-rounder; fastest of the top tier. |
| Claude 3.5 Sonnet | Anthropic | 88.7 | 96.4 | 92.0 | Top code & reasoning, excellent agent behavior. |
| Gemini 1.5 Pro | 85.9 | 91.7 | 84.1 | 1M+ token context — best for long-document tasks. | |
| Llama 3.1 405B | Meta | 88.6 | 96.8 | 89.0 | Best open weights; self-host viable. |
| Mistral Large 2 | Mistral | 84.0 | 93.0 | 92.0 | Strong code; favorable license for EU stacks. |
Scores aggregated from public vendor reports and the HELM / Open LLM Leaderboard projects. Always re-verify on your own task before picking a model.
How to read these benchmarks
MMLU
57-subject knowledge & reasoning
Watch out: Saturated — top models cluster within 3 points; small gaps are noise.
GSM8K
Grade-school math word problems
Watch out: Mostly solved by frontier models; HumanEval / MATH discriminate better.
HumanEval
Python code generation from docstrings
Watch out: Doesn't reflect real codebases — pair with SWE-bench for production signal.
MT-Bench
Multi-turn instruction following, judged by GPT-4
Watch out: LLM-judge bias; useful relative, not absolute.
SWE-bench
Real GitHub issues fixed in real repos
Watch out: Best current proxy for engineering ability.
Practical AI-QA take
A 1–2 point lead on MMLU is meaningless. For production decisions, build a 50–200 example eval set on your task and re-run it on every candidate model. See the LLM Evaluation pillar guide.