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DeepSeek-V3.1-Terminus
About DeepSeek-V3.1-Terminus
DeepSeek-V3.1-Terminus is an AI model developed by DeepSeek, in the language category, released in 2025, made available as an open-weights model with 671B params.
On this page you'll find DeepSeek-V3.1-Terminus's full specifications, including 50 benchmark results. Review provider pricing and benchmark scores below, or compare DeepSeek-V3.1-Terminus head-to-head with other language models.
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Run DeepSeek-V3.1-Terminus locally — what it takes
DeepSeek-V3.1-Terminus (671B) is too large to run on a single consumer or Apple machine. At Q4 its weights alone are about 376 GB — it needs a multi-GPU / datacenter setup:
- ≈ 2× AMD Instinct MI325X
- ≈ 3× NVIDIA B200
Lower quantization (Q3/Q2) or CPU + system-RAM offload (e.g. ktransformers) reduces the requirement at the cost of speed.
Software support
✓ measured · · compatible · — not supported. Informational only — speed is hardware-based.
Community
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DeepSeek-V3.1-Terminus benchmark scores▶
| Benchmark | Category | Score | Variant |
|---|---|---|---|
| Codeforces Elo | code | 2,073 | thinking-rating-cited-ring-1t |
| Codeforces Elo | code | 1,582 | non-thinking-rating-cited-ling-1t |
| FullStackBench | code | 55.48% | non-thinking-cited-ling-1t |
| LiveCodeBench | code | 48.02% | non-thinking-cited-ling-1t |
| LiveCodeBench v6 | code | 75.33% | thinking-cited-ring-1t |
| MBPP | code | 90.69% | sanitized-non-thinking-cited-ling-1t |
| Aider Benchmark | coding | 92.86 | thinking-cited-ring-1t |
| Aider Benchmark | coding | 88.16 | non-thinking-edit-cited-ling-1t |
| MultiPL-E | coding | 77.68 | non-thinking-cited-ling-1t |
| ArenaHard v2 | general | 63.24 | non-thinking-win-rate-cited-ling-1t |
| ArenaHard v2 | general | 60.27 | thinking-win-rate-cited-ring-1t |
| C-Eval | general | 91.76% | non-thinking-cited-ling-1t |
| C-Eval | general | 91.22% | thinking-cited-ring-1t |
| IFEval | general | 89.09% | thinking-cited-ring-1t |
| IFEval | general | 86.32% | non-thinking-prompt-strict-cited-ling-1t |
| MMLU-Pro | general | 85% | thinking-cited-ring-1t |
| MMLU-Pro | general | 83.25% | non-thinking-cited-ling-1t |
| MMLU-Redux | general | 92.37% | non-thinking-cited-ling-1t |
| MultiChallenge | general | 45.79% | thinking-cited-ring-1t |
| MultiChallenge | general | 42.49% | non-thinking-cited-ling-1t |
| AGIEval | knowledge | 89.83 | thinking-cited-ring-1t |
| CMMLU | knowledge | 89.2 | thinking-cited-ring-1t |
| TriviaQA | knowledge | 82.77 | thinking-cited-ring-1t |
| AIME 2024 | math | 71.67% | non-thinking-cited-ling-1t |
| AIME 2025 | math | 89.06% | thinking-cited-ring-1t |
| AIME 2025 | math | 55.21% | non-thinking-cited-ling-1t |
| CNMO 2024 | math | 85.42% | thinking-cited-ring-1t |
| CNMO 2024 | math | 73.78% | non-thinking-cited-ling-1t |
| HMMT February 2025 | math | 86.1% | thinking-cited-ring-1t |
| HMMT February 2025 | math | 41.25% | non-thinking-cited-ling-1t |
| Omni-MATH | math | 81.93 | thinking-cited-ring-1t |
| Omni-MATH | math | 64.77 | non-thinking-cited-ling-1t |
| UGMathBench | math | 77.19 | thinking-cited-ring-1t |
| UGMathBench | math | 72.7 | non-thinking-cited-ling-1t |
| ARC-AGI-1 | reasoning | 40.62 | thinking-cited-ring-1t |
| ARC-AGI-1 | reasoning | 14.69 | non-thinking-cited-ling-1t |
| BBEH | reasoning | 61.04 | thinking-cited-ring-1t |
| BBEH | reasoning | 42.86 | non-thinking-cited-ling-1t |
| FinanceReasoning | reasoning | 87.76 | thinking-cited-ring-1t |
| FinanceReasoning | reasoning | 86.44 | non-thinking-cited-ling-1t |
| GPQA Diamond | reasoning | 81% | thinking-cited-ring-1t |
| GPQA Diamond | reasoning | 76.23% | non-thinking-cited-ling-1t |
| Humanity's Last Exam | reasoning | 17.82% | thinking-cited-ring-1t |
| Humanity's Last Exam | reasoning | 10.38% | non-thinking-cited-ling-1t |
| ZebraLogic | reasoning | 96.33% | thinking-cited-ring-1t |
| ZebraLogic | reasoning | 81.6% | non-thinking-cited-ling-1t |
| HealthBench | safety | 50.19% | thinking-cited-ring-1t |
| PhyBench | science | 47.91 | thinking-cited-ring-1t |
| BFCL v3 | tool-use | 62.01% | thinking-cited-ring-1t |
| BFCL v3 | tool-use | 52.67% | non-thinking-cited-ling-1t |
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Frequently asked questions
What is DeepSeek-V3.1-Terminus? ▶
DeepSeek-V3.1-Terminus is an AI model developed by DeepSeek, in the language category, released in 2025. It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.
Who created DeepSeek-V3.1-Terminus? ▶
DeepSeek-V3.1-Terminus was developed by DeepSeek and released in 2025.
Is DeepSeek-V3.1-Terminus open source or proprietary? ▶
DeepSeek-V3.1-Terminus ships as an open-weights model: you can download and self-host the weights, though the licence may place some restrictions on use.
How much does DeepSeek-V3.1-Terminus cost? ▶
Pricing for DeepSeek-V3.1-Terminus depends on the provider. See the providers table on this page for the latest API rates.
How does DeepSeek-V3.1-Terminus perform on benchmarks? ▶
DeepSeek-V3.1-Terminus is benchmarked across 50 evaluations on GenAIList, including Codeforces Elo (2,073). See the full benchmark table below and compare it with other models.
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