DeepSeek-V4.1-Flash leads on the shared-benchmark average
Ahead on 13 of 15 shared benchmarks, averaging 6.4 points higher
Summarised only from the 15 percentage-scale benchmarks scored by every selected model; details are below.
“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-V4.1-Flash
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: 多模态理解 +10.6 / Relative gap: 综合评估 -5.1
Relative edge: 综合评估 +5.1 / Relative gap: 多模态理解 -10.6
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.
15 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | DeepSeek-V4.1-Flash | Kimi K3 |
|---|---|---|
63.90Thinking Level · High | Tools | 59.80Thinking Level · High | Tools | |
90.90Thinking Level · High | 92.90Thinking Level · High | |
74.20Thinking Level · High | Tools | 67.50Thinking Level · High | Tools | |
65.40Thinking Level · High | Tools | 58.00Thinking Level · High | Tools | |
20.30Thinking Level · High | Tools | 17.50Thinking Level · High | Tools | |
88.10Thinking Level · High | Tools | 80.00Thinking Level · High | Tools | |
90.60Thinking Level · High | Tools | 88.30Thinking Level · High | Tools | |
30.00Thinking Level · High | Tools | 17.70Thinking Level · High | Tools | |
31.20Thinking Level · High | Tools | 12.60Thinking Level · High | Tools | |
31.80Thinking Level · High | Tools | 27.60Thinking Level · High | Tools | |
54.80Thinking Level · High | Tools | 46.70Thinking Level · High | Tools | |
89.60Thinking Level · High | Tools | 85.70Thinking Level · High | Tools | |
78.90Thinking Level · High | Tools | 68.10Thinking Level · High | Tools | |
49.00Thinking Level · High | Tools | 41.00Thinking Level · High | Tools | |
65.60Thinking Level · High | 65.60Thinking Level · High |
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-V4.1-FlashDeepSeek-AI | Kimi K3Moonshot AI |
|---|---|---|
Core specsRelease | 2026-09-10 | 2026-07-16 |
Context length | 1M | 1M |
Total parameters | 552B | 2.8T |
Active parameters | 16B | 104B |
Max output length | 384,000 tokens | 1,048,576 tokens |
Architecture | MoE (mixture of experts) | MoE (mixture of experts) |
Runtime modes | 关闭低高最高 | 低高最高 |
Availability & licensingCode availability | Available · MIT License | Available · Kimi K3 License |
Weight availability | Available · MIT License | Available · Kimi K3 License |
Use & commercial terms | 免费商用授权 | 有条件免费商用授权 |
Local deploymentWeight size | 约 510.3 GB(48 个 safetensors 分片,约 475.3 GiB) | 约 1.42 TiB(MXFP4,96 个 Safetensors 分片) |
VRAM for weights | ≈ 510 GB (weights only, excludes KV cache) | ≈ 1454 GB (weights only, excludes KV cache) |
Weights | Hugging Face | Hugging Face |
Source repo | Not provided | GitHub |
API modality supportText Input/Output | Input:YesOutput:Yes | Input:YesOutput:Yes |
Image Input/Output | Input:YesOutput:No | Input:YesOutput:No |
Video Input/Output | Input:NoOutput:No | Input:YesOutput:No |
ResourcesPaper / report | DeepSeek V4.1 Technical Report | Kimi K3 技术报告 |

Kimi K3
Moonshot AI