Codestral vs Mistral Large 3: Pricing Comparison

Compare pricing, capabilities, and costs for your LLM workloads.

Mistral

Codestral

Pricing (per 1M tokens)

Input$0.3000

Output$0.9000

Context & Output

Context Window256K tokens

Max Output32.8K tokens

Capabilities

Categorymid

Multimodaltext

Fine-tuningNo

StreamingYes

Mistral

Mistral Large 3

Pricing (per 1M tokens)

Input$0.5000

Output$1.50

Context & Output

Context Window262.1K tokens

Max Output32.8K tokens

Capabilities

Categoryflagship

Multimodaltext

Fine-tuningNo

StreamingYes

Quick Verdict

Cheaper Input Price

Codestral

40.0% cheaper

Cheaper Output Price

Codestral

40.0% cheaper

Larger Context Window

Mistral Large 3

+6.1K tokens

Cost Comparison

Sample workload: 1,000,000 input tokens + 1,000,000 output tokens

Codestral

$1.20

$0.3000/1M input + $0.9000/1M output

Mistral Large 3

$2.00

$0.5000/1M input + $1.50/1M output

Codestral is 40.0% cheaper for this workload.

Mistral Large 3 and Codestral serve different primary use cases despite coming from the same provider. Mistral Large 3 is Mistral AI's general-purpose flagship — strong across reasoning, instruction-following, and multilingual tasks. Codestral is Mistral's code-specialized model, purpose-built for code generation, completion, and technical tasks. Mistral Large 3 costs $0.50/M input tokens and $1.50/M output tokens. Codestral costs $0.30/M input tokens and $0.90/M output tokens — making it about 40% cheaper for code-heavy workloads where its specialization gives it an edge.

Which should you use?

Choose Mistral Large 3 if:

your workload spans multiple domains — reasoning, summarization, multilingual content, customer support, or general-purpose text generation. Mistral Large 3 is the right choice when you need consistent quality across task types and cannot afford to run separate models for different jobs. It also outperforms Codestral on non-code tasks where instruction-following and nuanced reasoning matter more than code-specific training.

Choose Codestral if:

your primary workload is code generation, completion, debugging, or technical documentation. Codestral is purpose-trained on code and outperforms Mistral Large 3 on standard code benchmarks at a fraction of the cost. For engineering teams running code-assist pipelines, automated testing, or code review at scale, Codestral is the clear cost-performance winner.

Frequently Asked Questions

Which is cheaper, Codestral or Mistral Large 3?

For input tokens, Codestral is cheaper at $0.3000 per 1M tokens. For output tokens, Codestral is cheaper at $0.9000 per 1M tokens. The overall cost depends on your workload's input/output ratio.

What is the context window size of Codestral vs Mistral Large 3?

Codestral has a context window of 256K tokens, while Mistral Large 3 has 262.1K tokens. Mistral Large 3 supports a larger context window of 262.1K tokens, which is beneficial for processing longer documents.

Is Codestral better than Mistral Large for code generation?

For code-specific tasks, yes — Codestral is purpose-trained on code and consistently outperforms Mistral Large 3 on code generation benchmarks including HumanEval and MBPP. Codestral also costs significantly less per token, making it the default choice for any workload where code generation, completion, or debugging is the primary task. Mistral Large 3 is the better choice when code is one component of a broader mixed workload requiring strong general reasoning.

How much cheaper is Codestral than Mistral Large 3?

Codestral costs $0.30 per million input tokens and $0.90 per million output tokens. Mistral Large 3 costs $0.50 per million input tokens and $1.50 per million output tokens — exactly 1.67× more expensive on both input and output. For a pure code generation workload of 10 million tokens per month with a 50/50 input/output split, Codestral saves approximately $4.00 compared to Mistral Large 3. Use the calculator above to enter your specific volume.

What languages does Codestral support?

Codestral supports over 80 programming languages including Python, JavaScript, TypeScript, Java, C++, Go, Rust, SQL, and Bash. It is trained on a large corpus of code from public repositories and performs well on both completion tasks (finishing partial code) and generation tasks (writing code from a natural language description). It supports a fill-in-the-middle (FIM) mode specifically designed for code completion use cases.

Can Mistral Large 3 handle code tasks?

Yes. Mistral Large 3 is a capable general-purpose model that handles code tasks reasonably well — it scores competitively on code benchmarks relative to other general models at its price tier. The practical question is not whether it can code, but whether you need a single model to handle both code and non-code tasks in the same pipeline. If yes, Mistral Large 3 is the right choice. If your pipeline is code-only or code-primary, Codestral delivers better quality at lower cost.

Does Codestral support an API for production use?

Yes. Codestral is available via the Mistral AI API and is designed for production use. It supports both standard completion and fill-in-the-middle (FIM) endpoints. FIM is particularly useful for code completion integrations where you need the model to complete code given both a prefix and suffix context — the standard pattern for IDE integrations and code assist tools.

Which Mistral model should I use for a RAG pipeline?

For a RAG pipeline where the LLM synthesizes retrieved documents into an answer, Mistral Large 3 is the better choice over Codestral. RAG synthesis requires strong instruction-following, coherent reasoning over multiple sources, and reliable citation behavior — areas where Mistral Large 3's general-purpose training outperforms Codestral's code-focused training. If your RAG pipeline retrieves code snippets and generates code-based answers, Codestral becomes competitive again.

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