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MAI-Thinking-1 vs Mistral Large 4

Mistral Large 4 leads the LLM Stats Score 46.2 to 32.9.

Microsoft · Mistral AI · Updated for 2026

Which is better?

Mistral Large 4 leads the overall LLM Stats Score 46.2 to 32.9, ranking #34 overall.

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

Choose MAI-Thinking-1

  • you are already invested in the Microsoft ecosystem

Choose Mistral Large 4

  • overall performance matters — it scores 46.2 and ranks #34 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.

MAI-Thinking-1
Microsoft
Mistral Large 4
Mistral AI
Core performance indexes
LLM Stats Score
32.9
#116
46.2
#34
Reasoning
33.7
#105
44.0
#43
Coding
19.0
#120
35.8
#27
Agents
12.5
#113
34.3
#26
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.68 / M
Output price
— / M
$2.09 / M
Context window
—
1,000,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
MAI-Thinking-1
Mistral Large 4
Tool use
10.7#129
27.1#26
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

23 reported for MAI-Thinking-1 · 18 for Mistral Large 4

No common benchmarks found

MAI-Thinking-1 and Mistral Large 4don'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

50.0B diff

Mistral Large 4 has 50.0B more parameters than MAI-Thinking-1, making it 5.0% larger.

Microsoft
MAI-Thinking-1
1.0Tparameters
Mistral AI
Mistral Large 4
1.1Tparameters
1000.0B
MAI-Thinking-1
1050.0B
Mistral Large 4

Context Window

Maximum input and output token capacity

Only Mistral Large 4 specifies input context (1,000,000 tokens).

Microsoft
MAI-Thinking-1
Input- tokens
Output- tokens
Mistral AI
Mistral Large 4
Input1,000,000 tokens
Output- tokens
Thu Oct 08 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Mistral Large 4 supports multimodal inputs, whereas MAI-Thinking-1 does not.

Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.

MAI-Thinking-1

Text
Images
Audio
Video

Mistral Large 4

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.

MAI-Thinking-1

Proprietary

Closed source

Mistral Large 4

Proprietary

Closed source

Release Timeline

When each model was launched

MAI-Thinking-1 was released on 2026-06-02, while Mistral Large 4 was released on 2026-10-06.

Mistral Large 4 is 4 months newer than MAI-Thinking-1.

MAI-Thinking-1

Jun 2, 2026

4 months ago

Mistral Large 4

Oct 6, 2026

2 days ago

4mo 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 MAI-Thinking-1 and Mistral Large 4 side-by-side, then vote on the output you prefer.

MAI-Thinking-1
✓ Preferred
Mistral Large 4
Open in Playground

FAQ

Common questions about MAI-Thinking-1 vs Mistral Large 4.

Which is better, MAI-Thinking-1 or Mistral Large 4?

Mistral Large 4 leads the LLM Stats Score 46.2 to 32.9. MAI-Thinking-1 is made by Microsoft and Mistral Large 4 is made by Mistral AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does MAI-Thinking-1 compare to Mistral Large 4 in benchmarks?

MAI-Thinking-1 scores LongFact: 98.0%, AIME 2025: 97.0%, AIME 2026: 94.5%, GraphWalks: 90.0%, AIR-Bench: 88.0%. Mistral Large 4 scores B3 AI Security Benchmark: 93.3%, CyBench: 93.0%, SciCode: 91.8%, KORABench: 84.5%, CyberGym: 82.0%.

What are the context window sizes for MAI-Thinking-1 and Mistral Large 4?

MAI-Thinking-1 supports an unknown number of tokens and Mistral Large 4 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 MAI-Thinking-1 and Mistral Large 4?

Key differences include LLM Stats Score (32.9 vs 46.2), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

Who makes MAI-Thinking-1 and Mistral Large 4?

MAI-Thinking-1 is developed by Microsoft and Mistral Large 4 is developed by Mistral AI.

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