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A head-to-head benchmark comparison of DeepSeek-V3.1-Terminus vs Qwen3-30B-A3B across 47 evaluations. DeepSeek-V3.1-Terminus leads on the most benchmarks (29).
What’s improved? Language consistency: fewer CN/EN mix-ups & no more random chars. Agent upgrades: stronger Code Agent & Search Agent performance. DeepSeek-V.
Full DeepSeek-V3.1-Terminus specs →Today, we are excited to announce the release of Qwen3, the latest addition to the Qwen family of large language models. Our flagship model, Qwen3-235B-A22B, ac.
Full Qwen3-30B-A3B specs →| Benchmark | DeepSeek-V3.1-Terminus | Qwen3-30B-A3B |
|---|---|---|
| code | ||
| Codeforces Elo | 2,073 | 1,974 |
| FullStackBench | 55.48% | — |
| LiveCodeBench | 48.02% | — |
| LiveCodeBench v5 | — | 62.6% |
| LiveCodeBench v6 | 75.33% | — |
| MBPP | 90.69% | — |
| SciCode | — | 28.5% |
| coding | ||
| Aider Benchmark | 92.86 | — |
| MultiPL-E | 77.68 | — |
| general | ||
| AlignBench v1.1 | — | 8.7 |
| ArenaHard | — | 91% |
| ArenaHard v2 | 63.24 | — |
| C-Eval | 91.76% | 86.6% |
| Creative Writing v3 | — | 79.1 |
| IFEval | 89.09% | 86.5% |
| LiveBench | — | 74.3 |
| MMLU-Pro | 85% | — |
| MMLU-Redux | 92.37% | 89.5% |
| MMMLU | — | 78.4% |
| Multi-IF | — | 72.2 |
| MultiChallenge | 45.79% | — |
| WritingBench | — | 7.7 |
| knowledge | ||
| AGIEval | 89.83 | — |
| CMMLU | 89.2 | — |
| INCLUDE | — | 71.9 |
| TriviaQA | 82.77 | — |
| math | ||
| AIME 2024 | 71.67% | 80.4% |
| AIME 2025 | 89.06% | 70.9% |
| CNMO 2024 | 85.42% | — |
| HMMT February 2025 | 86.1% | 23.54% |
| MATH-500 | — | 98% |
| MT-AIME 2024 | — | 73.9 |
| Omni-MATH | 81.93 | — |
| Omni-MATH-HARD | — | 23.87% |
| PolyMath-en | — | 46.1 |
| UGMathBench | 77.19 | — |
| reasoning | ||
| ARC-AGI-1 | 40.62 | — |
| AutoLogi | — | 88.7 |
| BBEH | 61.04 | — |
| FinanceReasoning | 87.76 | — |
| GPQA Diamond | 81% | 65.8% |
| Humanity's Last Exam | 17.82% | 6.14% |
| MLogiQA | — | 70.1 |
| ZebraLogic | 96.33% | 89.5% |
| safety | ||
| HealthBench | 50.19% | — |
| science | ||
| PhyBench | 47.91 | — |
| tool-use | ||
| BFCL v3 | 62.01% | 69.1% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
Across the 47 benchmarks compared here, DeepSeek-V3.1-Terminus leads on the most (29). The right choice still depends on your task — check the per-benchmark table above for coding, reasoning and math.
DeepSeek-V3.1-Terminus has the strongest result on Codeforces Elo among the models compared here. See the code-category rows in the table for the full picture.
This comparison covers 47 benchmarks on which at least one of the selected models has a published score.
Scores are aggregated from official model cards, technical reports and standard public evaluations, and link back to each benchmark's source. They are updated as new results are published.
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