DeepSeek-V3.2 Chat on GPQA Diamond: score, rank and what it means
DeepSeek-V3.2 Chat scores 78.0 on GPQA Diamond, placing it #85 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 DeepSeek-V3.2 Chat 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, DeepSeek-V3.2 Chat sits at #85 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 9.0 points. The median model in this index scores 68.8, which puts DeepSeek-V3.2 Chat above the midpoint.
DeepSeek models average 67.5 on this benchmark. DeepSeek-V3.2 Chat is 10.5 points above that provider line, so it is a stronger result than the average DeepSeek model.
Among multimodal models, the average is 73.5. DeepSeek-V3.2 Chat is 4.5 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 78.0 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 $5.80 per million input tokens, DeepSeek-V3.2 Chat is above 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 |
|---|---|---|---|---|
| GPT-4.1 Pro | OpenAI | 400000 | $17.56 | 85.2 |
| Doubao 1.5 Pro Reasoning | ByteDance | 128000 | $14.56 | 81.6 |
| Kimi K1.5 Chat | Moonshot AI | 128000 | $5.11 | 80.2 |
| Command A Turbo | Cohere | 32768 | $13.53 | 79.8 |
| DeepSeek-V3.2 Chat | DeepSeek | 1000000 | $5.80 | 82.5 |
| Gemini 2.5 Pro Vision | Google DeepMind | 200000 | $14.91 | 82.0 |
| Reka Core Pro | Reka AI | 65536 | $13.44 | 79.9 |
Other benchmarks for DeepSeek-V3.2 Chat
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 82.5 | 114 | 81th | |
| SWE-bench Verified | 69.6 | 120 | 80th | |
| HumanEval | 87.9 | 74 | 88th | |
| MATH-500 | 90.2 | 46 | 92th | |
| AIME 2025 | 81.0 | 75 | 88th | |
| LiveCodeBench | 77.9 | 65 | 89th | |
| IFEval | 88.6 | 91 | 85th | |
| TruthfulQA | 86.3 | 45 | 93th | |
| HellaSwag | 89.2 | 107 | 82th | |
| MMMU | 76.0 | 140 | 77th | |
| Arena Elo | 1462.2 | 48 | 92th |
FAQ
What score does DeepSeek-V3.2 Chat get on GPQA Diamond?
DeepSeek-V3.2 Chat scores 78.0 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.
How does DeepSeek-V3.2 Chat rank on GPQA Diamond?
It ranks #85 of 600 models, in the 86th percentile. The leader is GPT-5.2 Reasoning at 87.0.
Is DeepSeek-V3.2 Chat 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 DeepSeek-V3.2 Chat cost?
DeepSeek-V3.2 Chat costs $5.80 per million input tokens and $26.43 per million output tokens.