Model Comparison

DeepSeek VL2 vs Pixtral-12B

DeepSeek VL2 shows notably better performance in the majority of benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek VL2 outperforms in 3 benchmarks (ChartQA, DocVQA, MathVista), while Pixtral-12B is better at 1 benchmark (MMMU).

DeepSeek VL2 shows notably better performance in the majority of benchmarks.

Fri Apr 17 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
Fri Apr 17 2026 • llm-stats.com
DeepSeek
DeepSeek VL2
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Mistral AI
Pixtral-12B
Input tokens$0.15
Output tokens$0.15
Best providerMistral
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

14.6B diff

DeepSeek VL2 has 14.6B more parameters than Pixtral-12B, making it 117.7% larger.

DeepSeek
DeepSeek VL2
27.0Bparameters
Mistral AI
Pixtral-12B
12.4Bparameters
27.0B
DeepSeek VL2
12.4B
Pixtral-12B

Context Window

Maximum input and output token capacity

DeepSeek VL2 accepts 129,280 input tokens compared to Pixtral-12B's 128,000 tokens. DeepSeek VL2 can generate longer responses up to 129,280 tokens, while Pixtral-12B is limited to 8,192 tokens.

DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Mistral AI
Pixtral-12B
Input128,000 tokens
Output8,192 tokens
Fri Apr 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both DeepSeek VL2 and Pixtral-12B support multimodal inputs.

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

DeepSeek VL2

Text
Images
Audio
Video

Pixtral-12B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 is licensed under deepseek, while Pixtral-12B 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

Pixtral-12B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek VL2 was released on 2024-12-13, while Pixtral-12B was released on 2024-09-17.

DeepSeek VL2 is 3 months newer than Pixtral-12B.

DeepSeek VL2

Dec 13, 2024

1.3 years ago

2mo newer
Pixtral-12B

Sep 17, 2024

1.6 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

Provider Availability

DeepSeek VL2 is available from Replicate. Pixtral-12B is available from Mistral AI.

DeepSeek VL2

replicate logo
Replicate

Pixtral-12B

mistral logo
Mistral
Input Price:Input: $0.15/1MOutput Price:Output: $0.15/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (129,280 tokens)
Higher ChartQA score (86.0% vs 81.8%)
Higher DocVQA score (93.3% vs 90.7%)
Higher MathVista score (62.8% vs 58.0%)
Mistral AI
Higher MMMU score (52.5% vs 51.1%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek VL2
Mistral AI
Pixtral-12B

FAQ

Common questions about DeepSeek VL2 vs Pixtral-12B

DeepSeek VL2 shows notably better performance in the majority of benchmarks. DeepSeek VL2 is made by DeepSeek and Pixtral-12B 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%. Pixtral-12B scores DocVQA: 90.7%, ChartQA: 81.8%, VQAv2: 78.6%, MT-Bench: 76.8%, HumanEval: 72.0%.
DeepSeek VL2 supports 129K tokens and Pixtral-12B supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (129K vs 128K), licensing (deepseek vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
DeepSeek VL2 is developed by DeepSeek and Pixtral-12B is developed by Mistral AI.