Model Comparison

DeepSeek-V3.2 (Thinking) vs Mistral Small 3.1 24B InstructWhich is better in 2026?

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Verdict: DeepSeek-V3.2 (Thinking) vs Mistral Small 3.1 24B Instruct — which is better?

DeepSeek-V3.2 (Thinking) (by DeepSeek) and Mistral Small 3.1 24B Instruct (by Mistral AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

DeepSeek-V3.2 (Thinking) outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Mistral Small 3.1 24B Instruct is better at 0 benchmarks. DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Choose DeepSeek-V3.2 (Thinking) if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • you want the most recent training data — it shipped Dec 2025

Choose Mistral Small 3.1 24B Instruct if…

  • you are already invested in the Mistral AI ecosystem

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V3.2 (Thinking) outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Mistral Small 3.1 24B Instruct is better at 0 benchmarks.

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Wed Jul 22 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

661.0B diff

DeepSeek-V3.2 (Thinking) has 661.0B more parameters than Mistral Small 3.1 24B Instruct, making it 2754.2% larger.

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
Mistral AI
Mistral Small 3.1 24B Instruct
24.0Bparameters
685.0B
DeepSeek-V3.2 (Thinking)
24.0B
Mistral Small 3.1 24B Instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2 (Thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Thinking) specifies output context (65,536 tokens).

DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Mistral AI
Mistral Small 3.1 24B Instruct
Input- tokens
Output- tokens
Wed Jul 22 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Mistral Small 3.1 24B Instruct supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) does not.

Mistral Small 3.1 24B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.2 (Thinking)

Text
Images
Audio
Video

Mistral Small 3.1 24B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2 (Thinking) is licensed under MIT, while Mistral Small 3.1 24B Instruct uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Mistral Small 3.1 24B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Mistral Small 3.1 24B Instruct was released on 2025-03-17.

DeepSeek-V3.2 (Thinking) is 9 months newer than Mistral Small 3.1 24B Instruct.

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

7 months ago

8mo newer
Mistral Small 3.1 24B Instruct

Mar 17, 2025

1.3 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher GPQA score (82.4% vs 46.0%)
Higher MMLU-Pro score (85.0% vs 66.8%)
Mistral AI
Supports multimodal inputs

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against DeepSeek-V3.2 (Thinking) and Mistral Small 3.1 24B Instruct side-by-side, then vote on the output you prefer.

DeepSeek-V3.2 (Thinking)
✓ Preferred
Mistral Small 3.1 24B Instruct
Open in Playground
AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Thinking)
Mistral AI
Mistral Small 3.1 24B Instruct

FAQ

Common questions about DeepSeek-V3.2 (Thinking) vs Mistral Small 3.1 24B Instruct.

Which is better, DeepSeek-V3.2 (Thinking) or Mistral Small 3.1 24B Instruct?

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is made by DeepSeek and Mistral Small 3.1 24B Instruct is made by Mistral AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.2 (Thinking) compare to Mistral Small 3.1 24B Instruct in benchmarks?

DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%. Mistral Small 3.1 24B Instruct scores HumanEval: 88.4%, MMLU: 80.6%, TriviaQA: 80.5%, MBPP: 74.7%, MATH: 69.3%.

What are the context window sizes for DeepSeek-V3.2 (Thinking) and Mistral Small 3.1 24B Instruct?

DeepSeek-V3.2 (Thinking) supports 131K tokens and Mistral Small 3.1 24B Instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.2 (Thinking) and Mistral Small 3.1 24B Instruct?

Key differences include multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Thinking) and Mistral Small 3.1 24B Instruct?

DeepSeek-V3.2 (Thinking) is developed by DeepSeek and Mistral Small 3.1 24B Instruct is developed by Mistral AI.

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