DeepSeek-V4.1-Flash-1017 vs Kimi K3.1 Pro
How DeepSeek-V4.1-Flash-1017 and Kimi K3.1 Pro stack up across benchmarks, pricing, speed, and the workloads that matter.
DeepSeek-V4.1-Flash-1017
DeepSeek · Open weights
Kimi K3.1 Pro
Moonshot AI · Proprietary
DeepSeek-V4.1-Flash-1017 is the cheaper of the two at $0.40 per million blended tokens, about 7.22x less than Kimi K3.1 Pro at $2.89. On reasoning, Kimi K3.1 Pro leads by about 24 points in our normalized benchmark average.
The table below breaks down every workload axis where both models have published results. Each score is a normalized 0 to 1 average across the benchmarks tagged with that category. A gap of a few points is noise; a gap of ten or more is a real difference in capability. Where one model has not reported a result, the cell shows n/a and the missing benchmark does not drag its average down.
Benchmark scores by workload
| Workload | DeepSeek-V4.1-Flash-1017 | Kimi K3.1 Pro | Edge |
|---|---|---|---|
| Reasoning | 50% | 74% | +24.1 pts |
| Coding | 54% | 61% | +6.4 pts |
| Agents & tool use | 49% | 56% | +7.3 pts |
| Math | 43% | 61% | +17.2 pts |
| Vision & multimodal | 71% | 74% | +3 pts |
| Writing | n/a | 56% | n/a |
| Knowledge & factuality | n/a | 45% | n/a |
The headline benchmarks below are the most widely cited individual tests. GPQA Diamond measures graduate-level reasoning in physics, chemistry, and biology. SWE-Bench Verified tests whether a model can fix real GitHub issues. MMMU-Pro covers college-level multimodal understanding across six disciplines. AIME and FrontierMath push competitive and research math. BrowseComp measures web research ability.
Headline benchmarks
| Benchmark | DeepSeek-V4.1-Flash-1017 | Kimi K3.1 Pro |
|---|---|---|
| GPQA | n/a | 94% |
| MMMU-Pro | n/a | 84% |
| BrowseComp | n/a | 92% |
Benchmarks tell you what a model can do in a controlled setting. They do not tell you whether it will work on your specific task, with your specific data, at your specific scale. The recommendations below map each common workload to whichever of these two models scores higher on the relevant axis. Use them as a starting point, not a final answer.
Which should you choose?
Hard reasoning
Multi-step analysis, research, and problems that need sustained thought.
Kimi K3.1 ProCoding & software
Writing, reviewing, and debugging code across a real codebase.
Kimi K3.1 ProAgents & tool use
Long-running agents that call tools, browse, and act on their own.
Kimi K3.1 ProMath & science
Formal math, competitive problems, and quantitative science.
Kimi K3.1 ProVision & documents
Reading images, screenshots, charts, and dense documents.
Kimi K3.1 ProWriting & drafting
Copy, emails, and long-form drafting where tone matters.
Kimi K3.1 ProHigh-volume & cost-sensitive
Cheap, repetitive calls where the blended token price dominates.
DeepSeek-V4.1-Flash-1017Scores are normalized from public benchmarks published by llm-stats.com and averaged per workload. Pricing is the blended input/output cost per million tokens at an 8:1 mix. Refreshes daily. How this works.