by datastudy.nl

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

Head to head

Claude Opus 5 vs DeepSeek-V4-Flash-0731

How Claude Opus 5 and DeepSeek-V4-Flash-0731 stack up across benchmarks, pricing, speed, and the workloads that matter.

Claude Opus 5

Anthropic · Proprietary

Blended price$7.22per 1M tokens
Context1Mtokens
Speed3.5tok/s
Benchmarks12results
VS

DeepSeek-V4-Flash-0731

DeepSeek · Open weights

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

Claude Opus 5 costs $7.22 per million blended tokens, about 72.22x the price of DeepSeek-V4-Flash-0731. On agents & tool use, DeepSeek-V4-Flash-0731 leads by about 15 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

WorkloadClaude Opus 5DeepSeek-V4-Flash-0731Edge
Reasoning 64% 51% +13.6 pts
Coding 55% 66% +11.3 pts
Agents & tool use 39% 54% +15 pts
Math 65% n/a n/a
Vision & multimodal 69% n/a n/a
Knowledge & factuality 50% n/a 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

BenchmarkClaude Opus 5DeepSeek-V4-Flash-0731
ARC-AGI-3 30% n/a
BrowseComp 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.

Claude Opus 5

Coding & software

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

DeepSeek-V4-Flash-0731

Agents & tool use

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

DeepSeek-V4-Flash-0731

Math & science

Formal math, competitive problems, and quantitative science.

Claude Opus 5

Vision & documents

Reading images, screenshots, charts, and dense documents.

Claude Opus 5

High-volume & cost-sensitive

Cheap, repetitive calls where the blended token price dominates.

DeepSeek-V4-Flash-0731

Low latency & interactive

Chat, autocomplete, and real-time experiences that need snappy responses.

DeepSeek-V4-Flash-0731

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.