Model Comparison

DeepSeek R1 Zero vs Pixtral-12B

Comparing DeepSeek R1 Zero and Pixtral-12B across benchmarks, pricing, and capabilities.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek R1 Zero and Pixtral-12B don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Mon Apr 06 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Zero
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

658.6B diff

DeepSeek R1 Zero has 658.6B more parameters than Pixtral-12B, making it 5311.3% larger.

DeepSeek
DeepSeek R1 Zero
671.0Bparameters
Mistral AI
Pixtral-12B
12.4Bparameters
671.0B
DeepSeek R1 Zero
12.4B
Pixtral-12B

Context Window

Maximum input and output token capacity

Only Pixtral-12B specifies input context (128,000 tokens). Only Pixtral-12B specifies output context (8,192 tokens).

DeepSeek
DeepSeek R1 Zero
Input- tokens
Output- tokens
Mistral AI
Pixtral-12B
Input128,000 tokens
Output8,192 tokens
Mon Apr 06 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Pixtral-12B supports multimodal inputs, whereas DeepSeek R1 Zero does not.

Pixtral-12B can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek R1 Zero

Text
Images
Audio
Video

Pixtral-12B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Zero is licensed under MIT, while Pixtral-12B uses Apache 2.0.

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

DeepSeek R1 Zero

MIT

Open weights

Pixtral-12B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while Pixtral-12B was released on 2024-09-17.

DeepSeek R1 Zero is 4 months newer than Pixtral-12B.

DeepSeek R1 Zero

Jan 20, 2025

1.2 years ago

4mo 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

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Mistral AI
Larger context window (128,000 tokens)
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Zero
Mistral AI
Pixtral-12B

FAQ

Common questions about DeepSeek R1 Zero vs Pixtral-12B

DeepSeek R1 Zero (DeepSeek) and Pixtral-12B (Mistral AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
DeepSeek R1 Zero scores MATH-500: 95.9%, AIME 2024: 86.7%, GPQA: 73.3%, LiveCodeBench: 50.0%. Pixtral-12B scores DocVQA: 90.7%, ChartQA: 81.8%, VQAv2: 78.6%, MT-Bench: 76.8%, HumanEval: 72.0%.
DeepSeek R1 Zero supports an unknown number of 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 multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
DeepSeek R1 Zero is developed by DeepSeek and Pixtral-12B is developed by Mistral AI.