DeepSeek-V3.1
Zero-eval
#1BrowseComp-zh
#2SimpleQA
#2SWE-bench Multilingual
by DeepSeek
+
+
+
+
About
DeepSeek-V3.1 is a language model developed by DeepSeek. The model shows competitive results across 16 benchmarks. It excels particularly in SimpleQA (93.4%), MMLU-Redux (91.8%), MMLU-Pro (83.7%). It supports a 328K token context window for handling large documents. The model is available through 2 API providers. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents DeepSeek's latest advancement in AI technology.
+
+
+
+
Pricing Range
Input (per 1M)$0.27 -$0.27
Output (per 1M)$1.00 -$1.00
Providers2
+
+
+
+
Timeline
AnnouncedJan 10, 2025
ReleasedJan 10, 2025
+
+
+
+
License & Family
License
MIT
Base ModelDeepSeek-V3
Performance Overview
Performance metrics and category breakdown
Overall Performance
16 benchmarks
Average Score
58.4%
Best Score
93.4%
High Performers (80%+)
3Performance Metrics
Max Context Window
327.7K+
+
+
+
All Benchmark Results for DeepSeek-V3.1
Complete list of benchmark scores with detailed information
| SimpleQA | text | 0.93 | 93.4% | Self-reported | |
| MMLU-Redux | text | 0.92 | 91.8% | Self-reported | |
| MMLU-Pro | text | 0.84 | 83.7% | Self-reported | |
| GPQA | text | 0.75 | 74.9% | Self-reported | |
| CodeForces | text | 0.70 | 69.7% | Self-reported | |
| Aider-Polyglot | text | 0.68 | 68.4% | Self-reported | |
| AIME 2024 | text | 0.66 | 66.3% | Self-reported | |
| SWE-Bench Verified | text | 0.66 | 66.0% | Self-reported | |
| LiveCodeBench | text | 0.56 | 56.4% | Self-reported | |
| SWE-bench Multilingual | text | 0.55 | 54.5% | Self-reported |
Showing 1 to 10 of 16 benchmarks