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DeepSeek-V3
About DeepSeek-V3
DeepSeek-V3 is an AI model developed by DeepSeek, in the language category, released in 2024, made available as an open-weights model with 671B params.
On this page you'll find DeepSeek-V3's full specifications, including 45 benchmark results. Review provider pricing and benchmark scores below, or compare DeepSeek-V3 head-to-head with other language models.
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Run DeepSeek-V3 locally — what it takes
DeepSeek-V3 (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 benchmark scores▶
| Benchmark | Category | Score | Variant |
|---|---|---|---|
| Aider Polyglot | code | 49.6% | cited-magistral |
| HumanEval | code | 94.6% | pass@10-cited-tulu3 |
| HumanEval | code | 92.1% | cited-minimax-text |
| HumanEval | code | 91.5% | pass@1 |
| HumanEval+ | code | 91.6% | pass@10-cited-tulu3 |
| LiveCodeBench | code | 42.7% | pass@1 |
| LiveCodeBench v5 | code | 36.2% | cited-magistral |
| MBPP+ | code | 78.8% | plus-cited-minimax-text |
| MBPP+ | code | 75.7% | pass@1 |
| AlpacaEval 2 | general | 66.5% | |
| AlpacaEval 2 | general | 53.5% | lc-win-cited-tulu3 |
| ArenaHard | general | 92.1% | |
| ArenaHard | general | 91.4% | cited-minimax-text |
| C-Eval | general | 86.3% | |
| CLUEWSC | general | 93.5% | |
| IFEval | general | 88% | cited-tulu3 |
| IFEval | general | 87.3% | avg-cited-minimax-text |
| IFEval | general | 86.1% | |
| MMLU | general | 88.5% | cited-minimax-text |
| MMLU | general | 87.9% | EM |
| MMLU | general | 82.1% | cited-tulu3 |
| MMLU-Pro | general | 76% | EM |
| MMLU-Pro | general | 75.9% | cited-minimax-text |
| MMLU-Redux | general | 88.8% | EM |
| AIME 2024 | math | 39.2% | cited-magistral |
| AIME 2024 | math | 34% | pass@1 |
| AIME 2025 | math | 28.8% | cited-magistral |
| CNMO 2024 | math | 33.9% | pass@1 |
| GSM8K | math | 96.7% | cited-minimax-text |
| GSM8K | math | 94.1% | cited-tulu3 |
| MATH | math | 89.7% | EM |
| MATH | math | 84.6% | cited-minimax-text |
| MATH | math | 72.5% | cited-tulu3 |
| MATH-500 | math | 90.2% | cited-magistral |
| MATH-500 | math | 88.9% | pass@1 |
| BIG-Bench Hard | reasoning | 89.5% | cited-tulu3 |
| DROP | reasoning | 91.8% | F1 |
| DROP | reasoning | 91% | f1-cited-minimax-text |
| GPQA Diamond | reasoning | 59.1% | cited-magistral |
| GPQA Diamond | reasoning | 59.1% | diamond-cited-minimax-text |
| GPQA Diamond | reasoning | 56.1% | pass@1 |
| C-SimpleQA | safety | 68.9% | |
| C-SimpleQA | safety | 64.8% | cited-minimax-text |
| SimpleQA | safety | 24.9% | cited-minimax-text |
| SimpleQA | safety | 24.6% |
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Frequently asked questions
What is DeepSeek-V3? ▶
DeepSeek-V3 is an AI model developed by DeepSeek, in the language category, released in 2024. It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.
Who created DeepSeek-V3? ▶
DeepSeek-V3 was developed by DeepSeek and released in 2024.
Is DeepSeek-V3 open source or proprietary? ▶
DeepSeek-V3 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 cost? ▶
Pricing for DeepSeek-V3 depends on the provider. See the providers table on this page for the latest API rates.
How does DeepSeek-V3 perform on benchmarks? ▶
DeepSeek-V3 is benchmarked across 45 evaluations on GenAIList, including Aider Polyglot (49.6%). See the full benchmark table below and compare it with other models.
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