DeepSeek-V4-Pro-0813 vs Pixtral Large
Comparing DeepSeek-V4-Pro-0813 and Pixtral Large across benchmarks, pricing, and capabilities.
DeepSeek · Mistral AI · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 and Pixtral Large trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-V4-Pro-0813 is roughly 5.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-0813 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- cost matters — it's about 5.5x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Pixtral Large
- you want predictable pricing at $2.00/M input and $6.00/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-0813 and Pixtral Largedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 4.6x cheaper than Pixtral Large ($2.00/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 6.9x cheaper than Pixtral Large ($6.00/1M tokens).
In conclusion, Pixtral Large is more expensive than DeepSeek-V4-Pro-0813.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 1476.0B more parameters than Pixtral Large, making it 1190.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to Pixtral Large's 128,000 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Pixtral Large is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Pixtral Large supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
Pixtral Large can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
Pixtral Large
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 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.
MIT
Open weights
Mistral Research License (MRL) for research; Mistral Commercial License for commercial use
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Pro-0813 was released on 2026-08-13, while Pixtral Large was released on 2024-11-18.
DeepSeek-V4-Pro-0813 is 21 months newer than Pixtral Large.
Aug 13, 2026
1 weeks ago
1.7yr newerNov 18, 2024
1.8 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Pixtral Large is available from Mistral AI.
DeepSeek-V4-Pro-0813
Pixtral Large
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Pixtral Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Pixtral Large.