Model Comparison

Gemma 4 26B-A4B vs MAI-Thinking-1Which is better in 2026?

MAI-Thinking-1 significantly outperforms across most benchmarks.

Verdict: Gemma 4 26B-A4B vs MAI-Thinking-1 — which is better?

Gemma 4 26B-A4B (by Google) and MAI-Thinking-1 (by Microsoft) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Gemma 4 26B-A4B outperforms in 1 benchmarks (MedXpertQA), while MAI-Thinking-1 is better at 4 benchmarks (AIME 2026, GPQA, LiveCodeBench v6, MMLU-Pro). MAI-Thinking-1 significantly outperforms across most benchmarks.

Choose Gemma 4 26B-A4B if…

  • you need open weights you can self-host or fine-tune

Choose MAI-Thinking-1 if…

  • you want the strongest raw capability — it leads on 4 of 5 shared benchmarks
  • you want the most recent training data — it shipped Jun 2026

Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

Gemma 4 26B-A4B outperforms in 1 benchmarks (MedXpertQA), while MAI-Thinking-1 is better at 4 benchmarks (AIME 2026, GPQA, LiveCodeBench v6, MMLU-Pro).

MAI-Thinking-1 significantly outperforms across most benchmarks.

Fri Jul 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

974.8B diff

MAI-Thinking-1 has 974.8B more parameters than Gemma 4 26B-A4B, making it 3868.3% larger.

Google
Gemma 4 26B-A4B
25.2Bparameters
Microsoft
MAI-Thinking-1
1.0Tparameters
25.2B
Gemma 4 26B-A4B
1000.0B
MAI-Thinking-1

Context Window

Maximum input and output token capacity

Only Gemma 4 26B-A4B specifies input context (262,144 tokens). Only Gemma 4 26B-A4B specifies output context (131,072 tokens).

Google
Gemma 4 26B-A4B
Input262,144 tokens
Output131,072 tokens
Microsoft
MAI-Thinking-1
Input- tokens
Output- tokens
Fri Jul 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 4 26B-A4B supports multimodal inputs, whereas MAI-Thinking-1 does not.

Gemma 4 26B-A4B can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemma 4 26B-A4B

Text
Images
Audio
Video

MAI-Thinking-1

Text
Images
Audio
Video

License

Usage and distribution terms

Gemma 4 26B-A4B is licensed under Apache 2.0, while MAI-Thinking-1 uses a proprietary license.

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

Gemma 4 26B-A4B

Apache 2.0

Open weights

MAI-Thinking-1

Proprietary

Closed source

Release Timeline

When each model was launched

Gemma 4 26B-A4B was released on 2026-04-02, while MAI-Thinking-1 was released on 2026-06-02.

MAI-Thinking-1 is 2 months newer than Gemma 4 26B-A4B.

Gemma 4 26B-A4B

Apr 2, 2026

3 months ago

MAI-Thinking-1

Jun 2, 2026

1 months ago

2mo newer

Knowledge Cutoff

When training data ends

Gemma 4 26B-A4B has a documented knowledge cutoff of 2025-01-01, while MAI-Thinking-1's cutoff date is not specified.

We can confirm Gemma 4 26B-A4B's training data extends to 2025-01-01, but cannot make a direct comparison without MAI-Thinking-1's cutoff date.

Gemma 4 26B-A4B

Jan 2025

MAI-Thinking-1

—

Outputs Comparison

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Key Takeaways

Google
Larger context window (262,144 tokens)
Supports multimodal inputs
Has open weights
Higher MedXpertQA score (58.1% vs 43.0%)
Microsoft
Higher AIME 2026 score (94.5% vs 88.3%)
Higher GPQA score (84.2% vs 82.3%)
Higher LiveCodeBench v6 score (87.7% vs 77.1%)
Higher MMLU-Pro score (85.0% vs 82.6%)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Gemma 4 26B-A4B and MAI-Thinking-1 side-by-side, then vote on the output you prefer.

Gemma 4 26B-A4B
✓ Preferred
MAI-Thinking-1
Open in Playground
AI Model Comparison Table
Feature
Google
Gemma 4 26B-A4B
Microsoft
MAI-Thinking-1

FAQ

Common questions about Gemma 4 26B-A4B vs MAI-Thinking-1.

Which is better, Gemma 4 26B-A4B or MAI-Thinking-1?

MAI-Thinking-1 significantly outperforms across most benchmarks. Gemma 4 26B-A4B is made by Google and MAI-Thinking-1 is made by Microsoft. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemma 4 26B-A4B compare to MAI-Thinking-1 in benchmarks?

Gemma 4 26B-A4B scores AIME 2026: 88.3%, MMMLU: 86.3%, t2-bench: 85.5%, MMLU-Pro: 82.6%, MathVision: 82.4%. MAI-Thinking-1 scores LongFact: 98.0%, AIME 2025: 97.0%, AIME 2026: 94.5%, GraphWalks: 90.0%, AIR-Bench: 88.0%.

What are the context window sizes for Gemma 4 26B-A4B and MAI-Thinking-1?

Gemma 4 26B-A4B supports 262K tokens and MAI-Thinking-1 supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemma 4 26B-A4B and MAI-Thinking-1?

Key differences include multimodal support (yes vs no), licensing (Apache 2.0 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemma 4 26B-A4B and MAI-Thinking-1?

Gemma 4 26B-A4B is developed by Google and MAI-Thinking-1 is developed by Microsoft.

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