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Devstral Medium vs Gemini 1.5 Pro

Devstral Medium leads the LLM Stats Score 13.9 to 12.1. Devstral Medium is 5.5x cheaper per token.

Mistral AI · Google · Updated for 2026

Which is better?

Devstral Medium leads the overall LLM Stats Score 13.9 to 12.1, ranking #230 overall.

On price, Devstral Medium is roughly 5.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Gemini 1.5 Pro also accepts a larger context window (2,097,152 input tokens), making it the stronger choice for long documents and large codebases.

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

Choose Devstral Medium

  • overall performance matters — it scores 13.9 and ranks #230 on LLM Stats
  • cost matters — it's about 5.5x cheaper per token
  • you want the most recent training data — it shipped Jul 2025

Choose Gemini 1.5 Pro

  • you process long inputs — it offers a 2,097,152 token context window

At a glance

The differences that matter most.

Core performance indexes
13.9
#230
12.1
#244
13.8
#224
11.9
#237
5.7
#189
4.4
#201
Cost, coverage & limits
Benchmark wins
Input price
$0.40 / M
$2.50 / M
Output price
$2.00 / M
$10.00 / M
Context window
128,000
2,097,152

Individual benchmarks

1 reported for Devstral Medium · 23 for Gemini 1.5 Pro

No common benchmarks found

Devstral Medium and Gemini 1.5 Prodon'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

Pricing Analysis

Price comparison per million tokens

Devstral Medium costs less

For input processing, Devstral Medium ($0.40/1M tokens) is 6.3x cheaper than Gemini 1.5 Pro ($2.50/1M tokens).

For output processing, Devstral Medium ($2.00/1M tokens) is 5.0x cheaper than Gemini 1.5 Pro ($10.00/1M tokens).

In conclusion, Gemini 1.5 Pro is more expensive than Devstral Medium.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Sun Sep 06 2026 • llm-stats.com
Mistral AI
Devstral Medium
Input tokens$0.40
Output tokens$2.00
Best providerMistral
Google
Gemini 1.5 Pro
Input tokens$2.50
Output tokens$10.00
Best providerGoogle
Notice missing or incorrect data?Start an Issue

Context Window

Maximum input and output token capacity

Gemini 1.5 Pro accepts 2,097,152 input tokens compared to Devstral Medium's 128,000 tokens. Devstral Medium can generate longer responses up to 128,000 tokens, while Gemini 1.5 Pro is limited to 8,192 tokens.

Mistral AI
Devstral Medium
Input128,000 tokens
Output128,000 tokens
Google
Gemini 1.5 Pro
Input2,097,152 tokens
Output8,192 tokens
Sun Sep 06 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemini 1.5 Pro supports multimodal inputs, whereas Devstral Medium does not.

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

Devstral Medium

Text
Images
Audio
Video

Gemini 1.5 Pro

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

Devstral Medium

Proprietary

Closed source

Gemini 1.5 Pro

Proprietary

Closed source

Release Timeline

When each model was launched

Devstral Medium was released on 2025-07-10, while Gemini 1.5 Pro was released on 2024-05-01.

Devstral Medium is 15 months newer than Gemini 1.5 Pro.

Devstral Medium

Jul 10, 2025

1.2 years ago

1.2yr 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 Devstral Medium'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 Devstral Medium's cutoff date.

Devstral Medium

Gemini 1.5 Pro

Nov 2023

Provider Availability

Devstral Medium is available from Mistral AI. Gemini 1.5 Pro is available from Google.

Devstral Medium

mistral logo
Mistral
Input Price:Input: $0.40/1MOutput Price:Output: $2.00/1M

Gemini 1.5 Pro

google logo
Google
Input Price:Input: $2.50/1MOutput Price:Output: $10.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

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

Devstral Medium
✓ Preferred
Gemini 1.5 Pro
Open in Playground

FAQ

Common questions about Devstral Medium vs Gemini 1.5 Pro.

Which is better, Devstral Medium or Gemini 1.5 Pro?

Devstral Medium leads the LLM Stats Score 13.9 to 12.1. Devstral Medium 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 Devstral Medium compare to Gemini 1.5 Pro in benchmarks?

Devstral Medium scores SWE-Bench Verified: 61.6%. Gemini 1.5 Pro scores XSTest: 98.8%, FLEURS: 93.3%, HellaSwag: 93.3%, GSM8k: 90.8%, BIG-Bench Hard: 89.2%.

Is Devstral Medium cheaper than Gemini 1.5 Pro?

Devstral Medium is 6.3x cheaper for input tokens. Devstral Medium costs $0.40/M input and $2.00/M output via mistral. Gemini 1.5 Pro costs $2.50/M input and $10.00/M output via google.

What are the context window sizes for Devstral Medium and Gemini 1.5 Pro?

Devstral Medium supports 128K 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 Devstral Medium and Gemini 1.5 Pro?

Key differences include LLM Stats Score (13.9 vs 12.1), context window (128K vs 2.1M), input pricing ($0.40 vs $2.50/M), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

Who makes Devstral Medium and Gemini 1.5 Pro?

Devstral Medium is developed by Mistral AI and Gemini 1.5 Pro is developed by Google.