Model Comparison

DeepSeek VL2 vs Mistral Small 3.1 24B Instruct

Mistral Small 3.1 24B Instruct significantly outperforms across most benchmarks.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek VL2 outperforms in 0 benchmarks, while Mistral Small 3.1 24B Instruct is better at 1 benchmark (MMMU).

Mistral Small 3.1 24B Instruct significantly outperforms across most benchmarks.

Sat May 02 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Sat May 02 2026 • llm-stats.com
DeepSeek
DeepSeek VL2
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Mistral AI
Mistral Small 3.1 24B Instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

3.0B diff

DeepSeek VL2 has 3.0B more parameters than Mistral Small 3.1 24B Instruct, making it 12.5% larger.

DeepSeek
DeepSeek VL2
27.0Bparameters
Mistral AI
Mistral Small 3.1 24B Instruct
24.0Bparameters
27.0B
DeepSeek VL2
24.0B
Mistral Small 3.1 24B Instruct

Context Window

Maximum input and output token capacity

Only DeepSeek VL2 specifies input context (129,280 tokens). Only DeepSeek VL2 specifies output context (129,280 tokens).

DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Mistral AI
Mistral Small 3.1 24B Instruct
Input- tokens
Output- tokens
Sat May 02 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both DeepSeek VL2 and Mistral Small 3.1 24B Instruct support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

DeepSeek VL2

Text
Images
Audio
Video

Mistral Small 3.1 24B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 is licensed under deepseek, 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 VL2

deepseek

Open weights

Mistral Small 3.1 24B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek VL2 was released on 2024-12-13, while Mistral Small 3.1 24B Instruct was released on 2025-03-17.

Mistral Small 3.1 24B Instruct is 3 months newer than DeepSeek VL2.

DeepSeek VL2

Dec 13, 2024

1.4 years ago

Mistral Small 3.1 24B Instruct

Mar 17, 2025

1.1 years ago

3mo newer

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 (129,280 tokens)
Mistral AI
Higher MMMU score (59.3% vs 51.1%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek VL2
Mistral AI
Mistral Small 3.1 24B Instruct

FAQ

Common questions about DeepSeek VL2 vs Mistral Small 3.1 24B Instruct

Mistral Small 3.1 24B Instruct significantly outperforms across most benchmarks. DeepSeek VL2 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.
DeepSeek VL2 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%. Mistral Small 3.1 24B Instruct scores HumanEval: 88.4%, MMLU: 80.6%, TriviaQA: 80.5%, MBPP: 74.7%, MATH: 69.3%.
DeepSeek VL2 supports 129K 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.
Key differences include licensing (deepseek vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
DeepSeek VL2 is developed by DeepSeek and Mistral Small 3.1 24B Instruct is developed by Mistral AI.