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DeepSeekDeepSeek V4 ProVSMistral AIMistral Large 3

Analysis by:the whichllmmodel Editorial Team|Updated: June 2026

Our Take

We recommend Mistral Large 3 for practical local execution on standard developer hardware, or DeepSeek V4 Pro if you need peak reasoning and have the VRAM (or a cloud API) to host it. While DeepSeek V4 Pro offers frontier capability, its massive parameter size makes it extremely difficult to host locally. Choose Mistral Large 3 for standard local setups, or DeepSeek V4 Pro for peak reasoning.
WHY?
Benchmark Calculations & Evidence:
  • Model Size: DeepSeek V4 Pro is a massive 1600B parameter model requiring heavy GPU infrastructure, while Mistral Large 3 is a 675B parameter model.
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    Model Specs

    DeepSeek V4 Pro

    Open SourceAPI Available

    Benchmarks & Scores

    Coding (swe-bench-pro)
    52.1%

    complex codebases, multi-file repositories, and architectural planning

    Reasoning (gpqa-diamond)Winner (+2.5%)
    88%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)
    $2.17Input: $1.74 | Output: $3.48
    Context WindowLarger
    1.05M tokens
    Model Specs

    Mistral Large 3

    Open SourceAPI Available

    Benchmarks & Scores

    Coding (live-code-bench)
    34.4%

    scripting single-file apps or clearly defined functions

    Reasoning (gpqa-diamond)
    85.5%

    graduate-level science QA

    Cost & Context

    Cost (per 1M tokens)2.9x cheaper
    $0.75Input: $0.50 | Output: $1.50
    Context Window
    262.14k tokens

    Frequently Asked Questions about DeepSeek V4 Pro vs Mistral Large 3

    Mistral Large 3 is cheaper than DeepSeek V4 Pro. Mistral Large 3 has a blended cost of $0.75/1M tokens, which is about 2.9x cheaper than DeepSeek V4 Pro at $2.17/1M tokens.

    For coding tasks, DeepSeek V4 Pro scores 52.1% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while Mistral Large 3 scores 34.4% on live-code-bench (scripting single-file apps or clearly defined functions).

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