Mixtral Turbo Instruct on HellaSwag: score, rank and what it means
Mixtral Turbo Instruct scores 82.5 on HellaSwag, placing it #336 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.
Score distribution
The chart shows the top 10 models on HellaSwag, with Mixtral Turbo Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 3 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 4 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 5 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
| 8 | Gemini 3 Pro Turbo | Google DeepMind | 400000 | $19.62 | 86.4 |
| 9 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 10 | o4 Pro | OpenAI | 1000000 | $19.37 | 87.3 |
What this score means
On HellaSwag, Mixtral Turbo Instruct sits at #336 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 13.6 points. The median model in this index scores 83.6, which puts Mixtral Turbo Instruct below the midpoint.
Together AI models average 78.2 on this benchmark. Mixtral Turbo Instruct is 4.3 points above that provider line, so it is a stronger result than the average Together AI model.
Among open-weight models, the average is 80.2. Mixtral Turbo Instruct is 2.3 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 82.5 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 HellaSwag. 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 Cerebras Base | Cerebras | 65536 | $3.36 | 72.7 |
| Mistral Small 3 Chat | Mistral AI | 200000 | $3.54 | 73.7 |
| GPT-4.1 Nano | OpenAI | 32768 | $7.42 | 70.5 |
| Mixtral Turbo | Together AI | 65536 | $3.20 | 72.4 |
| Mixtral Turbo Instruct | Together AI | 32768 | $3.16 | 71.9 |
| OLMo 2 | Allen Institute for AI | 65536 | $2.99 | 71.0 |
| Firesearch | Fireworks AI | 32768 | $7.03 | 68.5 |
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 | |
| AIME 2025 | 67.5 | 304 | 49th | |
| LiveCodeBench | 61.4 | 358 | 40th | |
| IFEval | 82.1 | 275 | 54th | |
| TruthfulQA | 77.2 | 276 | 54th | |
| MMMU | 66.6 | 365 | 39th | |
| Arena Elo | 1356.6 | 351 | 42th |
FAQ
What score does Mixtral Turbo Instruct get on HellaSwag?
Mixtral Turbo Instruct scores 82.5 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.
How does Mixtral Turbo Instruct rank on HellaSwag?
It ranks #336 of 600 models, in the 44th percentile. The leader is GPT-5.2 Max at 96.1.
Is Mixtral Turbo Instruct good at HellaSwag?
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 HellaSwag score calculated?
HellaSwag is a commonsense 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.