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Codestral-22B vs Gemini 1.5 Pro

Codestral-22B and Gemini 1.5 Pro are closely matched at 0.2 and 12.2 on the LLM Stats Score.

Mistral AI · Google · Updated for 2026

Which is better?

Codestral-22B and Gemini 1.5 Pro are closely matched on the overall LLM Stats Score at 0.2 and 12.2.

In the 1 individual benchmarks reported for both models, Gemini 1.5 Pro wins 1; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Codestral-22B

  • you want the most recent training data — it shipped May 2024
  • you need open weights you can self-host or fine-tune

Choose Gemini 1.5 Pro

  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results

At a glance

The differences that matter most.

Codestral-22B
Mistral AI
Gemini 1.5 Pro
Google
Core performance indexes
LLM Stats Score
0.2
#309
12.2
#239
Reasoning
0.1
#303
12.0
#233
Coding
2.5
#213
4.4
#197
Cost, coverage & limits
Benchmark wins
0 of 1
1 of 1
Input price
— / M
$2.50 / M
Output price
— / M
$10.00 / M
Context window
—
2,097,152

Individual benchmarks

7 reported for Codestral-22B · 23 for Gemini 1.5 Pro

1 shared

Codestral-22B outperforms in 0 benchmarks, while Gemini 1.5 Pro is better at 1 benchmark (HumanEval).

Gemini 1.5 Pro significantly outperforms across most benchmarks.

Mon Aug 31 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only Gemini 1.5 Pro specifies input context (2,097,152 tokens). Only Gemini 1.5 Pro specifies output context (8,192 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Google
Gemini 1.5 Pro
Input2,097,152 tokens
Output8,192 tokens
Mon Aug 31 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemini 1.5 Pro supports multimodal inputs, whereas Codestral-22B does not.

Gemini 1.5 Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.

Codestral-22B

Text
Images
Audio
Video

Gemini 1.5 Pro

Text
Images
Audio
Video

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while Gemini 1.5 Pro 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

Gemini 1.5 Pro

Proprietary

Closed source

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while Gemini 1.5 Pro was released on 2024-05-01.

Codestral-22B is 1 month newer than Gemini 1.5 Pro.

Codestral-22B

May 29, 2024

2.3 years ago

4w newer
Gemini 1.5 Pro

May 1, 2024

2.3 years ago

Knowledge Cutoff

When training data ends

Gemini 1.5 Pro has a documented knowledge cutoff of 2023-11-01, while Codestral-22B's cutoff date is not specified.

We can confirm Gemini 1.5 Pro's training data extends to 2023-11-01, but cannot make a direct comparison without Codestral-22B's cutoff date.

Codestral-22B

—

Gemini 1.5 Pro

Nov 2023

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against Codestral-22B and Gemini 1.5 Pro side-by-side, then vote on the output you prefer.

Codestral-22B
✓ Preferred
Gemini 1.5 Pro
Open in Playground

FAQ

Common questions about Codestral-22B vs Gemini 1.5 Pro.

Which is better, Codestral-22B or Gemini 1.5 Pro?

Codestral-22B and Gemini 1.5 Pro are closely matched on the LLM Stats Score at 0.2 and 12.2. Codestral-22B is made by Mistral AI and Gemini 1.5 Pro is made by Google. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Codestral-22B compare to Gemini 1.5 Pro in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. Gemini 1.5 Pro scores XSTest: 98.8%, FLEURS: 93.3%, HellaSwag: 93.3%, GSM8k: 90.8%, BIG-Bench Hard: 89.2%.

What are the context window sizes for Codestral-22B and Gemini 1.5 Pro?

Codestral-22B supports an unknown number of tokens and Gemini 1.5 Pro supports 2.1M 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 Gemini 1.5 Pro?

Key differences include LLM Stats Score (0.2 vs 12.2), 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 Gemini 1.5 Pro?

Codestral-22B is developed by Mistral AI and Gemini 1.5 Pro is developed by Google.

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