DeepSeek V3.2 leads on the shared-benchmark average
Ahead on 8 of 9 shared benchmarks, averaging 11.9 points higher
Summarised only from the 9 percentage-scale benchmarks scored by every selected model; details are below. A further 1 historical, reverse-direction or differently scaled metrics are excluded; indexes, cost and time are not added to accuracy percentages.
“Best available” takes each model’s best recorded non-parallel result in the metric’s stated direction per benchmark, so it may combine modes into a virtual configuration that does not exist. Read it with the mode breakdown.

DeepSeek V3.2
DeepSeek-AI
Benchmark-by-benchmark comparison. Changing the thinking mode or tool filters updates the chart and table below.
“Best available” picks the best non-parallel result in the metric’s stated direction separately for each benchmark. The resulting series can combine several reasoning levels and is not one reproducible runtime configuration. Choose a mode filter to compare like-for-like runs.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Each axis is the mean percentage score of one benchmark domain. It is an average, not a capability rating.
Relative edge: Agent能力评测 +29.1 / Relative gap: none clear
Relative edge: none clear / Relative gap: Agent能力评测 -29.1
Method: for each model and benchmark, all scores in the current mode scope are averaged (not the best score), then those benchmark scores are averaged within each domain. Only benchmarks scored on a 0-100 scale by at least two of the selected models count — Elo and rating-scale benchmarks such as Codeforces or Arena are excluded, because averaging a 1500 rating with an 85% accuracy produces a meaningless number. Missing values are not counted as zero, and the averages are unweighted, so domains with harder benchmarks read lower.
Every model and runtime mode, benchmark by benchmark. Values are comparable along a row, not between different benchmarks.
11 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | DeepSeek V3.2 | DeepSeek-V3.1 |
|---|---|---|
2.90Thinking Enabled | 2.00Thinking Enabled | |
25.10Thinking Enabled | 15.90Thinking Enabled | |
82.40Thinking Enabled | 80.10Thinking Enabled | |
83.30Thinking Enabled | 74.80Thinking Enabled | |
73.10Thinking Enabled | Tools | 66.00Standard Mode | |
93.10Thinking Enabled | 88.40Thinking Enabled | |
1511.20Standard Mode | 1433.20Standard Mode | |
35.60Thinking Enabled | Tools | 25.00Thinking Enabled | Tools | |
90.60Thinking Enabled | Tools | 37.40Thinking Enabled | Tools | |
60.70Thinking Enabled | 41.50Thinking Enabled | |
45.70Standard Mode | 47.00Standard Mode |
Official list prices per model API, split by input and output. Unit: USD per 1M tokens.
Architecture, licensing and API modalities. "Not provided" means the field is missing from our database.
| Features & specs | DeepSeek V3.2DeepSeek-AI | DeepSeek-V3.1DeepSeek-AI |
|---|---|---|
Core specsRelease | 2025-12-01 | 2025-08-20 |
Context length | 128K | 128K |
Total parameters | 671B | 671B |
Active parameters | 37B | 37B |
Max output length | 8,192 tokens | 8,192 tokens |
Architecture | MoE (mixture of experts) | MoE (mixture of experts) |
Runtime modes | 关闭开启 | 关闭开启 |
Availability & licensingCode availability | Available · DEEPSEEK LICENSE AGREEMENT | Available · MIT License |
Weight availability | Available · DEEPSEEK LICENSE AGREEMENT | Available · MIT License |
Use & commercial terms | 免费商用授权 | 免费商用授权 |
Local deploymentWeight size | 1.34TB | 1340GB |
VRAM for weights | ≈ 1340 GB (weights only, excludes KV cache) | ≈ 1340 GB (weights only, excludes KV cache) |
Weights | Hugging Face | Hugging Face |
Source repo | GitHub | Not provided |
API modality supportText Input/Output | Input:YesOutput:Yes | Input:YesOutput:Yes |
ResourcesPaper / report | DeepSeek-V3.2 正式版发布与说明 | DeepSeek-V3.1 Release |
DataLearner blog | 复杂问题推理能力大幅提升,DeepSeekAI发布DeepSeek V3.2正式版本以及一个评测结果可以媲美Gemini 3.0 Pro的将开源模型推到极限性能的DeepSeek-V3.2-Speciale模型 | DeepSeek V4没有等到,但是DeepSeekAI把DeepSeek V3升级到DeepSeek V3.1了,小幅更新,但核心架构和参数不变 |

DeepSeek-V3.1
DeepSeek-AI