The AI arena is free today

Open Superagent

Codestral-22B vs DeepSeek-V4.1-Flash-1017

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to -0.3.

Mistral AI · DeepSeek · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to -0.3, ranking #15 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-1017

  • overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

Codestral-22B
Mistral AI
DeepSeek-V4.1-Flash-1017
DeepSeek
Core performance indexes
LLM Stats Score
-0.3
#350
51.0
#15
Reasoning
-0.4
#343
48.6
#18
Coding
2.4
#248
39.1
#15
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.30 / M
Output price
— / M
$1.20 / M
Context window
—
1,048,576

Individual benchmarks

7 reported for Codestral-22B · 11 for DeepSeek-V4.1-Flash-1017

No common benchmarks found

Codestral-22B and DeepSeek-V4.1-Flash-1017don'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-1017 has 741.0B more parameters than Codestral-22B, making it 3337.9% larger.

Mistral AI
Codestral-22B
22.2Bparameters
DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
22.2B
Codestral-22B
763.2B
DeepSeek-V4.1-Flash-1017

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Tue Oct 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Codestral-22B does not.

DeepSeek-V4.1-Flash-1017 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-1017

Text
Images
Audio
Video

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while DeepSeek-V4.1-Flash-1017 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-1017

MIT

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while DeepSeek-V4.1-Flash-1017 was released on 2026-10-17.

DeepSeek-V4.1-Flash-1017 is 29 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.4 years ago

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

0 days ago

2.4yr 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?

Judge for yourself.

Run your own prompts against Codestral-22B and DeepSeek-V4.1-Flash-1017 side-by-side, then vote on the output you prefer.

Codestral-22B
✓ Preferred
DeepSeek-V4.1-Flash-1017
Open in Playground

FAQ

Common questions about Codestral-22B vs DeepSeek-V4.1-Flash-1017.

Which is better, Codestral-22B or DeepSeek-V4.1-Flash-1017?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to -0.3. Codestral-22B is made by Mistral AI and DeepSeek-V4.1-Flash-1017 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-1017 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-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%.

What are the context window sizes for Codestral-22B and DeepSeek-V4.1-Flash-1017?

Codestral-22B supports an unknown number of tokens and DeepSeek-V4.1-Flash-1017 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-1017?

Key differences include LLM Stats Score (-0.3 vs 51.0), 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-1017?

Codestral-22B is developed by Mistral AI and DeepSeek-V4.1-Flash-1017 is developed by DeepSeek.

Related comparisons