Codestral-22B vs DeepSeek-R1
Comparing Codestral-22B and DeepSeek-R1 across benchmarks, pricing, and capabilities.
Mistral AI · DeepSeek · Updated for 2026
Which is better?
Codestral-22B and DeepSeek-R1 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Codestral-22B
- you are already invested in the Mistral AI ecosystem
Choose DeepSeek-R1
- you want the most recent training data — it shipped Jan 2025
At a glance
The differences that matter most.
Individual benchmarks
7 reported for Codestral-22B · 0 for DeepSeek-R1
Codestral-22B and DeepSeek-R1don'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
Model Size
Parameter count comparison
DeepSeek-R1 has 648.8B more parameters than Codestral-22B, making it 2922.5% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).
License
Usage and distribution terms
Codestral-22B is licensed under MNPL-0.1, while DeepSeek-R1 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
MNPL-0.1
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Codestral-22B was released on 2024-05-29, while DeepSeek-R1 was released on 2025-01-20.
DeepSeek-R1 is 8 months newer than Codestral-22B.
May 29, 2024
2.3 years ago
Jan 20, 2025
1.6 years ago
7mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against Codestral-22B and DeepSeek-R1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Codestral-22B vs DeepSeek-R1.