Mixtral Turbo Instruct on HumanEval: score, rank and what it means
Mixtral Turbo Instruct scores 74.2 on HumanEval, placing it #355 of 600 models in the index. The benchmark average is 74.0 and the leader is GPT-5.2 Pro at 96.8.
Score distribution
The chart shows the top 10 models on HumanEval, with Mixtral Turbo Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 2 | Gemini 3 Pro Pro | Google DeepMind | 200000 | $19.80 | 87.3 |
| 3 | o4 Max | OpenAI | 2000000 | $19.51 | 86.2 |
| 4 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 5 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 6 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 7 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 8 | Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| 9 | GPT-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 10 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
What this score means
On HumanEval, Mixtral Turbo Instruct sits at #355 of 600. The leader, GPT-5.2 Pro, scores 96.8, so the gap to the top is 22.6 points. The median model in this index scores 77.4, which puts Mixtral Turbo Instruct below the midpoint.
Together AI models average 68.4 on this benchmark. Mixtral Turbo Instruct is 5.8 points above that provider line, so it is a stronger result than the average Together AI model.
Among open-weight models, the average is 72.4. Mixtral Turbo Instruct is 1.8 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 74.2 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.
Price context
At $3.16 per million input tokens, Mixtral Turbo Instruct is below the median input price of $4.00 across the 600 models scored on HumanEval. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Molmo Instruct | Allen Institute for AI | 32768 | $2.53 | 68.3 |
| Fusion | OpenRouter | 16384 | $8.41 | 71.1 |
| Molmo | Allen Institute for AI | 32768 | $2.49 | 68.5 |
| Llama 3 Hyperbolic Base | Hyperbolic | 32768 | $3.02 | 71.1 |
| Mixtral Turbo Instruct | Together AI | 32768 | $3.16 | 71.9 |
| Arctic Instruct | Snowflake | 65536 | $2.83 | 70.6 |
| Reka Core Mini | Reka AI | 8192 | $7.48 | 70.1 |
Other benchmarks for Mixtral Turbo Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 72.9 | 369 | 39th | |
| GPQA Diamond | 69.7 | 276 | 54th | |
| SWE-bench Verified | 60.8 | 309 | 49th | |
| MATH-500 | 75.9 | 303 | 50th | |
| AIME 2025 | 67.5 | 304 | 49th | |
| LiveCodeBench | 61.4 | 358 | 40th | |
| IFEval | 82.1 | 275 | 54th | |
| TruthfulQA | 77.2 | 276 | 54th | |
| HellaSwag | 82.5 | 336 | 44th | |
| MMMU | 66.6 | 365 | 39th | |
| Arena Elo | 1356.6 | 351 | 42th |
FAQ
What score does Mixtral Turbo Instruct get on HumanEval?
Mixtral Turbo Instruct scores 74.2 on HumanEval in this index. The benchmark average is 74.0 and the top score is 96.8.
How does Mixtral Turbo Instruct rank on HumanEval?
It ranks #355 of 600 models, in the 41st percentile. The leader is GPT-5.2 Pro at 96.8.
Is Mixtral Turbo Instruct good at HumanEval?
It is below the median for this benchmark, so test it carefully before relying on it for this capability. The result should be read alongside its price and context window.
How is the HumanEval score calculated?
HumanEval is a coding benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.
What does Mixtral Turbo Instruct cost?
Mixtral Turbo Instruct costs $3.16 per million input tokens and $13.04 per million output tokens.