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Codestral-22B vs Parse

Comparing Codestral-22B and Parse across benchmarks, pricing, and capabilities.

Mistral AI · Cohere · Updated for 2026

Which is better?

Codestral-22B and Parse 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 need open weights you can self-host or fine-tune

Choose Parse

  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Codestral-22B
Mistral AI
Parse
Cohere
Benchmark wins
—
—
Input price
— / M
— / M
Output price
— / M
— / M
Context window
—
8,192

Individual benchmarks

7 reported for Codestral-22B · 1 for Parse

No common benchmarks found

Codestral-22B and Parsedon'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

19.9B diff

Codestral-22B has 19.9B more parameters than Parse, making it 865.2% larger.

Mistral AI
Codestral-22B
22.2Bparameters
Cohere
Parse
2.3Bparameters
22.2B
Codestral-22B
2.3B
Parse

Context Window

Maximum input and output token capacity

Only Parse specifies input context (8,192 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Cohere
Parse
Input8,192 tokens
Output- tokens
Mon Aug 31 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Parse supports multimodal inputs, whereas Codestral-22B does not.

Parse can handle both text and other forms of data like images, making it suitable for multimodal applications.

Codestral-22B

Text
Images
Audio
Video

Parse

Text
Images
Audio
Video

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while Parse uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

Codestral-22B

MNPL-0.1

Open weights

Parse

Proprietary

Closed source

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while Parse was released on 2026-08-27.

Parse is 27 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.3 years ago

Parse

Aug 27, 2026

4 days ago

2.2yr newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against Codestral-22B and Parse side-by-side, then vote on the output you prefer.

Codestral-22B
✓ Preferred
Parse
Open in Playground

FAQ

Common questions about Codestral-22B vs Parse.

Which is better, Codestral-22B or Parse?

Codestral-22B (Mistral AI) and Parse (Cohere) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Codestral-22B compare to Parse in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. Parse scores ParseBench: 79.2%.

What are the context window sizes for Codestral-22B and Parse?

Codestral-22B supports an unknown number of tokens and Parse supports 8K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Codestral-22B and Parse?

Key differences include multimodal support (no vs yes), licensing (MNPL-0.1 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes Codestral-22B and Parse?

Codestral-22B is developed by Mistral AI and Parse is developed by Cohere.

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