by datastudy.nl

Head-to-head comparisons of frontier AI models, rebuilt daily from public benchmark data

Head to head

DeepSeek-V4.1-Flash vs Claude Sonnet 5.5

How DeepSeek-V4.1-Flash and Claude Sonnet 5.5 stack up across benchmarks, pricing, speed, and the workloads that matter.

DeepSeek-V4.1-Flash

DeepSeek · Open weights

Blended price$0.27per 1M tokens
Context1.04Mtokens
Speedn/atok/s
Benchmarks20results
VS

Claude Sonnet 5.5

Anthropic · Proprietary

Blended price$2.89per 1M tokens
Context1Mtokens
Speed42tok/s
Benchmarks47results

DeepSeek-V4.1-Flash is the cheaper of the two at $0.27 per million blended tokens, about 10.74x less than Claude Sonnet 5.5 at $2.89. On coding, Claude Sonnet 5.5 leads by about 17 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

WorkloadDeepSeek-V4.1-FlashClaude Sonnet 5.5Edge
Reasoning 76% 70% +6 pts
Coding 53% 70% +17.2 pts
Agents & tool use 49% 63% +13.7 pts
Math 61% 76% +14.8 pts
Vision & multimodal 68% 66% +1.7 pts
Writing n/a 60% n/a
Knowledge & factuality 91% 71% +19.5 pts

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

BenchmarkDeepSeek-V4.1-FlashClaude Sonnet 5.5
GPQA 91% n/a

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.

DeepSeek-V4.1-Flash

Coding & software

Writing, reviewing, and debugging code across a real codebase.

Claude Sonnet 5.5

Agents & tool use

Long-running agents that call tools, browse, and act on their own.

Claude Sonnet 5.5

Math & science

Formal math, competitive problems, and quantitative science.

Claude Sonnet 5.5

Vision & documents

Reading images, screenshots, charts, and dense documents.

DeepSeek-V4.1-Flash

Writing & drafting

Copy, emails, and long-form drafting where tone matters.

Claude Sonnet 5.5

High-volume & cost-sensitive

Cheap, repetitive calls where the blended token price dominates.

DeepSeek-V4.1-Flash

Scores 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.