Mistral Large 3 1.5B on GPQA Diamond: score, rank and what it means
Mistral Large 3 1.5B scores 52.3 on GPQA Diamond, placing it #497 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 Mistral Large 3 1.5B 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, Mistral Large 3 1.5B sits at #497 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 34.7 points. The median model in this index scores 68.8, which puts Mistral Large 3 1.5B below the midpoint.
Mistral AI models average 63.3 on this benchmark. Mistral Large 3 1.5B is 11.0 points below that provider line, so it is a weaker result than the average Mistral AI model.
Among small open-weight models, the average is 49.3. Mistral Large 3 1.5B is 3.0 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 52.3 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 $0.82 per million input tokens, Mistral Large 3 1.5B 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 |
|---|---|---|---|---|
| Firesearch Nano | Fireworks AI | 8192 | $2.49 | 55.6 |
| Firefunction Nano | Fireworks AI | 16384 | $3.07 | 58.1 |
| Llama 3 Lambda 7B | Lambda | 8192 | $0.84 | 55.4 |
| Embed 4 Mini | Cohere | 4096 | $2.48 | 55.8 |
| Mistral Large 3 1.5B | Mistral AI | 32768 | $0.82 | 55.4 |
| Mixtral 8B | Mistral AI | 8192 | $0.91 | 55.4 |
| Yi-Large 8B | 01.AI | 32768 | $0.88 | 56.1 |
Other benchmarks for Mistral Large 3 1.5B
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 62.2 | 499 | 17th | |
| SWE-bench Verified | 38.2 | 537 | 11th | |
| HumanEval | 56.6 | 534 | 11th | |
| MATH-500 | 56.6 | 500 | 17th | |
| AIME 2025 | 47.6 | 472 | 21th | |
| LiveCodeBench | 43.3 | 513 | 15th | |
| IFEval | 67.1 | 503 | 16th | |
| TruthfulQA | 60.1 | 528 | 12th | |
| HellaSwag | 71.9 | 502 | 16th | |
| MMMU | 53.8 | 511 | 15th | |
| Arena Elo | 1243.2 | 502 | 16th |
FAQ
What score does Mistral Large 3 1.5B get on GPQA Diamond?
Mistral Large 3 1.5B scores 52.3 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.
How does Mistral Large 3 1.5B rank on GPQA Diamond?
It ranks #497 of 600 models, in the 17th percentile. The leader is GPT-5.2 Reasoning at 87.0.
Is Mistral Large 3 1.5B good at GPQA Diamond?
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 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 Mistral Large 3 1.5B cost?
Mistral Large 3 1.5B costs $0.82 per million input tokens and $3.17 per million output tokens.