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Codestral-22B vs DeepSeek-V4.1-Flash

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 0.0.

Mistral AI · DeepSeek · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 0.0, ranking #12 overall.

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-V4.1-Flash

  • overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

Codestral-22B
Mistral AI
DeepSeek-V4.1-Flash
DeepSeek
Core performance indexes
LLM Stats Score
0.0
#325
51.8
#12
Reasoning
-0.1
#317
48.9
#17
Coding
2.5
#224
44.4
#5
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.22 / M
Output price
— / M
$0.66 / M
Context window
—
1,040,000

Individual benchmarks

7 reported for Codestral-22B · 20 for DeepSeek-V4.1-Flash

No common benchmarks found

Codestral-22B and DeepSeek-V4.1-Flashdon'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

741.0B diff

DeepSeek-V4.1-Flash has 741.0B more parameters than Codestral-22B, making it 3337.9% larger.

Mistral AI
Codestral-22B
22.2Bparameters
DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
22.2B
Codestral-22B
763.2B
DeepSeek-V4.1-Flash

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
Sun Sep 13 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash supports multimodal inputs, whereas Codestral-22B does not.

DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

Codestral-22B

Text
Images
Audio
Video

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while DeepSeek-V4.1-Flash uses MIT.

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

Codestral-22B

MNPL-0.1

Open weights

DeepSeek-V4.1-Flash

MIT

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while DeepSeek-V4.1-Flash was released on 2026-09-10.

DeepSeek-V4.1-Flash is 28 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.3 years ago

DeepSeek-V4.1-Flash

Sep 10, 2026

3 days ago

2.3yr 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 DeepSeek-V4.1-Flash side-by-side, then vote on the output you prefer.

Codestral-22B
✓ Preferred
DeepSeek-V4.1-Flash
Open in Playground

FAQ

Common questions about Codestral-22B vs DeepSeek-V4.1-Flash.

Which is better, Codestral-22B or DeepSeek-V4.1-Flash?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 0.0. Codestral-22B is made by Mistral AI and DeepSeek-V4.1-Flash is made by DeepSeek. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Codestral-22B compare to DeepSeek-V4.1-Flash in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%.

What are the context window sizes for Codestral-22B and DeepSeek-V4.1-Flash?

Codestral-22B supports an unknown number of tokens and DeepSeek-V4.1-Flash supports 1.0M 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 DeepSeek-V4.1-Flash?

Key differences include LLM Stats Score (0.0 vs 51.8), multimodal support (no vs yes), licensing (MNPL-0.1 vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes Codestral-22B and DeepSeek-V4.1-Flash?

Codestral-22B is developed by Mistral AI and DeepSeek-V4.1-Flash is developed by DeepSeek.

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