Model Comparison

DeepSeek R1 Zero vs Pixtral Large

Comparing DeepSeek R1 Zero and Pixtral Large across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek R1 Zero and Pixtral Large 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
Tue Mar 31 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Zero
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Mistral AI
Pixtral Large
Input tokens$2.00
Output tokens$6.00
Best providerMistral
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Model Size

Parameter count comparison

547.0B diff

DeepSeek R1 Zero has 547.0B more parameters than Pixtral Large, making it 441.1% larger.

DeepSeek
DeepSeek R1 Zero
671.0Bparameters
Mistral AI
Pixtral Large
124.0Bparameters
671.0B
DeepSeek R1 Zero
124.0B
Pixtral Large

Context Window

Maximum input and output token capacity

Only Pixtral Large specifies input context (128,000 tokens). Only Pixtral Large specifies output context (128,000 tokens).

DeepSeek
DeepSeek R1 Zero
Input- tokens
Output- tokens
Mistral AI
Pixtral Large
Input128,000 tokens
Output128,000 tokens
Tue Mar 31 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Pixtral Large supports multimodal inputs, whereas DeepSeek R1 Zero does not.

Pixtral Large 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 Large

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Zero is licensed under MIT, while Pixtral Large uses Mistral Research License (MRL) for research; Mistral Commercial License for commercial use.

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

DeepSeek R1 Zero

MIT

Open weights

Pixtral Large

Mistral Research License (MRL) for research; Mistral Commercial License for commercial use

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Zero was released on 2025-01-20, while Pixtral Large was released on 2024-11-18.

DeepSeek R1 Zero is 2 months newer than Pixtral Large.

DeepSeek R1 Zero

Jan 20, 2025

1.2 years ago

2mo newer
Pixtral Large

Nov 18, 2024

1.4 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

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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 Large

FAQ

Common questions about DeepSeek R1 Zero vs Pixtral Large

DeepSeek R1 Zero (DeepSeek) and Pixtral Large (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 Large scores AI2D: 93.8%, DocVQA: 93.3%, ChartQA: 88.1%, VQAv2: 80.9%, MM-MT-Bench: 74.0%.
DeepSeek R1 Zero supports an unknown number of tokens and Pixtral Large 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 Mistral Research License (MRL) for research; Mistral Commercial License for commercial use). See the full comparison above for benchmark-by-benchmark results.
DeepSeek R1 Zero is developed by DeepSeek and Pixtral Large is developed by Mistral AI.