Model crosswalk
Side-by-side on price, capability and workload — three-way comparison.
Codestral 22B
vs
DeepSeek R1 Distill Llama 70B
vs
Qwen 2.5 Coder 32B Instruct
Codestral 22BA
Codestral 22B
22B params · 33K context · mistral-research
Cheapest provider—
$/1M input—
$/1M output—
DeepSeek R1 Distill Llama 70BB
DeepSeek R1 Distill Llama 70B
70B params · 131K context · mit
Cheapest provider—
$/1M input—
$/1M output—
Qwen 2.5 Coder 32B InstructC
Qwen 2.5 Coder 32B Instruct
32B params · 131K context · qwen
Cheapest provider—
$/1M input—
$/1M output—
Specs and cheapest providers
| Spec | Codestral 22B | DeepSeek R1 Distill Llama 70B | Qwen 2.5 Coder 32B Instruct |
|---|---|---|---|
| Parameters | 22B | 70B | 32B |
| Context window | 33K tokens | 131K tokens | 131K tokens |
| License | mistral-research | mit | qwen |
| Released | 2024-05-29 | 2025-01-20 | 2024-11-12 |
| Cheapest provider | |||
| Provider | — | — | — |
| Input / 1M tokens | — | — | — |
| Output / 1M tokens | — | — | — |
Benchmark comparison
No benchmark data available yet.
Editor's take
A research-licensed code specialist, a reasoning-distilled generalist, and a commercially permissive coding model — three distinct architectures for code-heavy workloads.
Codestral 22B was Mistral AI's first code-focused model, a 22 billion parameter dense transformer released May 2024. It covers 80-plus programming languages with a 32K context window. HumanEval performance competed with DeepSeek Coder V2 Lite at release. The Mistral Research License is the standing obstacle: commercial deployment without a direct Mistral agreement is prohibited. Teams consistently benchmark it favorably and then discover the licensing friction. For non-commercial research and internal tooling, it remains a reasonable evaluation choice.
DeepSeek R1 Distill Llama 70B, released January 2025, distills chain-of-thought supervision from the full 671B R1 model into a Llama 3.3 70B dense base. Independent evals show roughly 70–80 percent of full R1's AIME and MATH scores. For code generation, its approach is reasoning-based rather than completion-pattern-based — useful when problems benefit from explicit multi-step planning, but less targeted than specialist fine-tunes for autocomplete tasks. Groq hosts it with competitive latency for a 70B model. MIT license makes it fully commercial with no use restrictions.
Qwen 2.5 Coder 32B Instruct, from Alibaba's November 2024 release, offers 32 billion parameters with explicit code-specialist training, support for 92 programming languages, and a 131K context window that handles multi-file diffs cleanly. LiveCodeBench and MultiPL-E results put it alongside DeepSeek Coder V2 in the production-viable tier. The Qwen license permits commercial use, and the model is widely hosted across inference providers.
Pick Codestral 22B for non-commercial research. Pick DeepSeek R1 Distill 70B for reasoning-intensive code tasks and algorithmic problem-solving with MIT-licensed freedom. Pick Qwen 2.5 Coder 32B for production-scale code completion, CI pipelines, and multi-file agentic coding workflows.
Compare two at a time
Frequently asked questions
- How does Codestral 22B compare to DeepSeek R1 Distill Llama 70B and Qwen 2.5 Coder 32B Instruct on price?
- Use the table above to compare input and output prices per 1M tokens across the cheapest available providers for each model.
- Which model is best for coding: Codestral 22B, DeepSeek R1 Distill Llama 70B, or Qwen 2.5 Coder 32B Instruct?
- HumanEval and other code benchmarks are shown in the table. For production code tasks, also consider context window size and provider latency.
- What is the context window for Codestral 22B, DeepSeek R1 Distill Llama 70B, and Qwen 2.5 Coder 32B Instruct?
- Context window sizes are listed in the Specs row of the comparison table above.
Full model details