Mixtral Turbo Instruct on GPQA Diamond: score, rank and what it means
Mixtral Turbo Instruct scores 69.7 on GPQA Diamond, placing it #276 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.
Score distribution
The chart shows the top 10 models on GPQA Diamond, with Mixtral Turbo Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Reasoning | OpenAI | 1000000 | $20.98 | 87.5 |
| 2 | GPT-5.1 Max | OpenAI | 2000000 | $20.50 | 87.9 |
| 3 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 4 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 5 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 6 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 7 | o4 Turbo | OpenAI | 2000000 | $19.34 | 86.6 |
| 8 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 9 | Grok 4 Turbo | xAI | 1000000 | $19.21 | 86.7 |
| 10 | Claude Sonnet 4.5 Max | Anthropic | 1000000 | $19.90 | 87.2 |
What this score means
On GPQA Diamond, Mixtral Turbo Instruct sits at #276 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 17.3 points. The median model in this index scores 68.8, which puts Mixtral Turbo Instruct above the midpoint.
Together AI models average 60.8 on this benchmark. Mixtral Turbo Instruct is 8.9 points above that provider line, so it is a stronger result than the average Together AI model.
Among open-weight models, the average is 63.6. Mixtral Turbo Instruct is 6.1 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 69.7 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 GPQA Diamond. 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 | Meta AI | 200000 | $4.16 | 76.3 |
| Nova Lite Max | Amazon | 200000 | $9.79 | 73.4 |
| Yi-Lightning Chat | 01.AI | 32768 | $3.79 | 75.5 |
| Llama 3.1 Cerebras Chat | Cerebras | 32768 | $4.07 | 76.0 |
| Mixtral Turbo Instruct | Together AI | 32768 | $3.16 | 71.9 |
| Seed-OSS Chat | ByteDance | 65536 | $3.49 | 74.4 |
| Claude Sonnet 4.5 Mini | Anthropic | 32768 | $11.43 | 76.5 |
Other benchmarks for Mixtral Turbo Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 72.9 | 369 | 39th | |
| SWE-bench Verified | 60.8 | 309 | 49th | |
| HumanEval | 74.2 | 355 | 41th | |
| 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 GPQA Diamond?
Mixtral Turbo Instruct scores 69.7 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.
How does Mixtral Turbo Instruct rank on GPQA Diamond?
It ranks #276 of 600 models, in the 54th percentile. The leader is GPT-5.2 Reasoning at 87.0.
Is Mixtral Turbo Instruct good at GPQA Diamond?
Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.
How is the GPQA Diamond score calculated?
GPQA Diamond is a reasoning 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.