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DeepSeek

DeepSeek-V4-Flash-Max: benchmarks, price and rank

DeepSeek-V4-Flash-Max from DeepSeek is 72nd of 123 rated AI models on Spezia's leaderboard. It costs $0.09 in, $0.18 out per million tokens.

  • 72nd of 123 rated models
  • Rating 1,117
  • Rated on 7 benchmarks
  • Open weights
  • Released Apr 23, 2026
  • 1 million tokens of context

Where it is strongest

Not in the top tenth on any benchmark in use.

Where it falls behind

  • 41st of 50 on SWE-Bench Pro
  • 23rd of 37 on Toolathlon
  • 20th of 33 on MCP Atlas

Price: $0.09 in, $0.18 out per million tokens.

Its headline results

The benchmarks that count most in the rating, with its place among the models measured the same way, and where each number came from.

  • BrowseComp73.2%

    Questions whose answers are hidden deep on the web and take many searches and pages to track down.

    30th of 56. The maker's own number, from DeepSeek, via LLM Stats.

  • MCP Atlas69%

    Multi-step tasks where the model has to pick and combine many real tools, connected through MCP, the standard way agents plug into apps and data.

    20th of 33. The maker's own number, from DeepSeek, via LLM Stats.

  • SWE-Bench Pro52.6%

    Long changes to real codebases in several languages, often across many files, including code from private and copyleft projects that models are unlikely to have seen.

    41st of 50. The maker's own number, from DeepSeek, via LLM Stats.

  • Humanity's Last Exam45.1%

    About 2,500 questions written by experts in more than a hundred fields to be as hard as they could make them, many needing a diagram or image.

    23rd of 74. The maker's own number, from DeepSeek, via LLM Stats.

  • Toolathlon47.8%

    Long tasks across many real apps and their tools, such as email, calendars, spreadsheets, code repositories and online stores.

    23rd of 37. The maker's own number, from DeepSeek, via LLM Stats.

  • IMO-AnswerBench88.4%

    Problems from the International Mathematical Olympiad, the hardest contest for school students, where the model has to reach the right final answer.

    5th of 18. The maker's own number, from DeepSeek, via LLM Stats.

  • SWE-bench Verified79%

    Real bug reports and feature requests from popular open-source Python projects. The model has to change the code so the project's hidden tests pass.

    7th of 56. The maker's own number, from DeepSeek, via LLM Stats.

  • GPQA Diamond88.1%

    Graduate-level questions in biology, physics and chemistry, written by PhDs so that searching the web does not give the answer away.

    The maker's own number, from DeepSeek, via LLM Stats.

  • MMLU-Pro86.2%

    Twelve thousand multiple-choice questions across fourteen subjects, from law and medicine to engineering, with ten options each.

    11th of 56. The maker's own number, from DeepSeek, via LLM Stats.

Its full profile is in Spezia.

Every result with its source, what it costs per task, what jumped with its release, side by side with any other model, and answers to your questions about when to use it.

Models ranked near it

How the rating works, and every benchmark it uses: the model leaderboard.