Mixtral Turbo Instruct on AIME 2025: score, rank and what it means
Mixtral Turbo Instruct scores 67.5 on AIME 2025, placing it #304 of 600 models in the index. The benchmark average is 63.6 and the leader is Claude Opus 4.5 Pro at 91.4.
Score distribution
The chart shows the top 10 models on AIME 2025, with Mixtral Turbo Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 2 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 3 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 4 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 5 | o4 Reasoning | OpenAI | 1000000 | $19.69 | 87.5 |
| 6 | o4 Pro | OpenAI | 1000000 | $19.37 | 87.3 |
| 7 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 8 | Gemini 3 Pro Max | Google DeepMind | 2000000 | $20.57 | 87.9 |
| 9 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 10 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
What this score means
On AIME 2025, Mixtral Turbo Instruct sits at #304 of 600. The leader, Claude Opus 4.5 Pro, scores 91.4, so the gap to the top is 23.9 points. The median model in this index scores 67.6, which puts Mixtral Turbo Instruct below the midpoint.
Together AI models average 56.2 on this benchmark. Mixtral Turbo Instruct is 11.3 points above that provider line, so it is a stronger result than the average Together AI model.
Among open-weight models, the average is 61.8. Mixtral Turbo Instruct is 5.8 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 67.5 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 AIME 2025. 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 |
|---|---|---|---|---|
| Llama 3.1 Chat | Meta AI | 32768 | $3.82 | 75.3 |
| DBRX Instruct | Databricks | 8192 | $3.21 | 72.4 |
| Llama 3 DeepInfra Base | DeepInfra | 16384 | $2.95 | 71.8 |
| Mixtral Groq | Groq | 16384 | $3.37 | 73.1 |
| Mixtral Turbo Instruct | Together AI | 32768 | $3.16 | 71.9 |
| DBRX Chat | Databricks | 65536 | $3.07 | 71.7 |
| Gemma 3 Instruct | Google DeepMind | 128000 | $3.72 | 74.1 |
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 | |
| HumanEval | 74.2 | 355 | 41th | |
| MATH-500 | 75.9 | 303 | 50th | |
| 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 AIME 2025?
Mixtral Turbo Instruct scores 67.5 on AIME 2025 in this index. The benchmark average is 63.6 and the top score is 91.4.
How does Mixtral Turbo Instruct rank on AIME 2025?
It ranks #304 of 600 models, in the 49th percentile. The leader is Claude Opus 4.5 Pro at 91.4.
Is Mixtral Turbo Instruct good at AIME 2025?
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 AIME 2025 score calculated?
AIME 2025 is a mathematics 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.