DeepSeek-R1-0528 vs Pixtral Large
DeepSeek-R1-0528 leads the LLM Stats Score 24.4 to 11.8. DeepSeek-R1-0528 is 3.3x cheaper per token.
DeepSeek · Mistral AI · Updated for 2026
Which is better?
DeepSeek-R1-0528 leads the overall LLM Stats Score 24.4 to 11.8, ranking #155 overall.
On price, DeepSeek-R1-0528 is roughly 3.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-R1-0528 also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-R1-0528
- overall performance matters — it scores 24.4 and ranks #155 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 3.3x cheaper per token
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped May 2025
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.
Individual benchmarks
16 reported for DeepSeek-R1-0528 · 7 for Pixtral Large
DeepSeek-R1-0528 and Pixtral Largedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) is 4.0x cheaper than Pixtral Large ($2.00/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 2.8x cheaper than Pixtral Large ($6.00/1M tokens).
In conclusion, Pixtral Large is more expensive than DeepSeek-R1-0528.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1-0528 has 547.0B more parameters than Pixtral Large, making it 441.1% larger.
Context Window
Maximum input and output token capacity
DeepSeek-R1-0528 accepts 131,072 input tokens compared to Pixtral Large's 128,000 tokens. DeepSeek-R1-0528 can generate longer responses up to 131,072 tokens, while Pixtral Large is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Pixtral Large supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
Pixtral Large can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
Pixtral Large
License
Usage and distribution terms
DeepSeek-R1-0528 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-R1-0528 was released on 2025-05-28, while Pixtral Large was released on 2024-11-18.
DeepSeek-R1-0528 is 6 months newer than Pixtral Large.
May 28, 2025
1.3 years ago
6mo 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-R1-0528 is available from DeepInfra, DeepSeek, Novita. Pixtral Large is available from Mistral AI.
DeepSeek-R1-0528
Pixtral Large
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1-0528 and Pixtral Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs Pixtral Large.