DeepSeek-V4-Pro leads on the shared-benchmark average
Ahead on 1 of 1 shared benchmarks, averaging 11.5 points higher
Summarised only from the 1 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-V4-Pro
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: AI Agent - 工具使用 +4.6 / Relative gap: 数学推理 -7.3
Relative edge: 数学推理 +7.3 / Relative gap: AI Agent - 工具使用 -4.6
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.
23 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | DeepSeek-V4-Pro | Kimi K2.6 |
|---|---|---|
12.90Thinking Level · High | 8.00Thinking Enabled | |
48.20Thinking Level · Extra High | Tools | 54.00Thinking Enabled | Tools | |
71.57Standard Mode | 70.54Thinking Enabled | |
90.50Thinking Level · High | 90.50Thinking Enabled | |
93.50Thinking Level · High | 89.60Thinking Enabled | |
50.80Thinking Level · High | 51.50Thinking Enabled | |
76.20Thinking Level · Extra High | Tools | 76.70Thinking Enabled | Tools | |
55.40Thinking Level · Extra High | Tools | 58.60Thinking Enabled | Tools | |
80.60Thinking Level · Extra High | Tools | 80.20Thinking Enabled | Tools | |
1552.10Standard Mode | 1721.30Standard Mode | |
46.20Thinking Level · High | Tools | 43.90Thinking Enabled | Tools | |
96.20Thinking Level · High | Tools | 95.90Thinking Enabled | Tools | |
30.10Thinking Level · High | Tools | 23.30Thinking Enabled | Tools | |
76.50Thinking Level · High | 76.00Thinking Enabled | |
83.40Thinking Level · Extra High | Tools | 83.20Thinking Enabled | Tools | |
67.90Thinking Level · Extra High | Tools | 66.70Thinking Enabled | Tools | |
87.90Thinking Level · Extra High | Tools | 65.90Thinking Enabled | Tools | |
76.09Thinking Enabled | 64.63Thinking Enabled | |
96.67Thinking Level · High | Tools | 96.40Thinking Enabled | |
89.80Thinking Level · High | 86.00Thinking Enabled | |
74.70Thinking Level · High | 69.70Standard Mode | |
84.40Thinking Level · High | Tools | 84.12Thinking Enabled | Tools | |
13.40Thinking Level · High | 13.00Thinking Enabled |
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-V4-ProDeepSeek-AI | Kimi K2.6Moonshot AI |
|---|---|---|
Core specsRelease | 2026-08-13 | 2026-04-20 |
Context length | 1M | 256K |
Total parameters | 1.6T | 1T |
Active parameters | 49B | 32B |
Max output length | 384,000 tokens | Not provided |
Architecture | MoE (mixture of experts) | MoE (mixture of experts) |
Runtime modes | 关闭高最高 | 关闭开启 |
Availability & licensingCode availability | Available · MIT License | Available · Modified MIT |
Weight availability | Available · MIT License | Available · Modified MIT |
Use & commercial terms | 免费商用授权 | 免费商用授权 |
Local deploymentWeights | Hugging Face | Not provided |
API modality supportText Input/Output | Input:YesOutput:Yes | Input:YesOutput:Yes |
Image Input/Output | Input:NoOutput:No | Input:YesOutput:No |
Video Input/Output | Input:NoOutput:No | Input:YesOutput:No |
ResourcesPaper / report | DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence | Kimi K2.6: Advancing Open-Source Coding |

Kimi K2.6
Moonshot AI