by datastudy.nl

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

Head to head

DeepSeek-V4-Flash-0731 vs Qwen3.8 Max

How DeepSeek-V4-Flash-0731 and Qwen3.8 Max stack up across benchmarks, pricing, speed, and the workloads that matter.

DeepSeek-V4-Flash-0731

DeepSeek · Open weights

Blended price$0.10per 1M tokens
Context1.05Mtokens
Speed6tok/s
Benchmarks9results
VS

Qwen3.8 Max

Alibaba Cloud / Qwen Team · Proprietary

Blended pricen/aper 1M tokens
Contextn/atokens
Speedn/atok/s
Benchmarks26results

On agents & tool use, Qwen3.8 Max leads by about 25 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-Flash-0731Qwen3.8 MaxEdge
Reasoning 51% 74% +22.9 pts
Coding 66% 68% +2.1 pts
Agents & tool use 54% 79% +25.4 pts
Math n/a 50% n/a
Vision & multimodal n/a 80% n/a
Long context n/a 74% n/a
Writing n/a 75% n/a
Knowledge & factuality n/a 82% 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

BenchmarkDeepSeek-V4-Flash-0731Qwen3.8 Max
GPQA n/a 93%
MMMU-Pro n/a 82%

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.

Qwen3.8 Max

Coding & software

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

Qwen3.8 Max

Agents & tool use

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

Qwen3.8 Max

Math & science

Formal math, competitive problems, and quantitative science.

Qwen3.8 Max

Vision & documents

Reading images, screenshots, charts, and dense documents.

Qwen3.8 Max

Long documents

Whole codebases, books, and transcripts that fill the context window.

Qwen3.8 Max

Writing & drafting

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

Qwen3.8 Max

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.