Model Comparison

Gemini Diffusion vs LongCat-Flash-Thinking

LongCat-Flash-Thinking significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

Gemini Diffusion outperforms in 0 benchmarks, while LongCat-Flash-Thinking is better at 4 benchmarks (AIME 2025, GPQA, LiveCodeBench, SWE-Bench Verified).

LongCat-Flash-Thinking significantly outperforms across most benchmarks.

Tue Apr 21 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Tue Apr 21 2026 • llm-stats.com
Google
Gemini Diffusion
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Meituan
LongCat-Flash-Thinking
Input tokens$0.30
Output tokens$1.20
Best providerMeituan
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Only LongCat-Flash-Thinking specifies input context (128,000 tokens). Only LongCat-Flash-Thinking specifies output context (128,000 tokens).

Google
Gemini Diffusion
Input- tokens
Output- tokens
Meituan
LongCat-Flash-Thinking
Input128,000 tokens
Output128,000 tokens
Tue Apr 21 2026 • llm-stats.com

License

Usage and distribution terms

Gemini Diffusion is licensed under a proprietary license, while LongCat-Flash-Thinking uses MIT.

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

Gemini Diffusion

Proprietary

Closed source

LongCat-Flash-Thinking

MIT

Open weights

Release Timeline

When each model was launched

Gemini Diffusion was released on 2025-05-20, while LongCat-Flash-Thinking was released on 2025-09-22.

LongCat-Flash-Thinking is 4 months newer than Gemini Diffusion.

Gemini Diffusion

May 20, 2025

11 months ago

LongCat-Flash-Thinking

Sep 22, 2025

7 months 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

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Key Takeaways

Google
Meituan
Larger context window (128,000 tokens)
Has open weights
Higher AIME 2025 score (90.6% vs 23.3%)
Higher GPQA score (81.5% vs 40.4%)
Higher LiveCodeBench score (79.4% vs 30.9%)
Higher SWE-Bench Verified score (59.4% vs 22.9%)

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini Diffusion
Meituan
LongCat-Flash-Thinking

FAQ

Common questions about Gemini Diffusion vs LongCat-Flash-Thinking

LongCat-Flash-Thinking significantly outperforms across most benchmarks. Gemini Diffusion is made by Google and LongCat-Flash-Thinking is made by Meituan. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Gemini Diffusion scores HumanEval: 89.6%, MBPP: 76.0%, Global-MMLU-Lite: 69.1%, LBPP (v2): 56.8%, BigCodeBench: 45.4%. LongCat-Flash-Thinking scores MATH-500: 99.2%, ZebraLogic: 95.5%, AIME 2024: 93.3%, AIME 2025: 90.6%, MMLU-Redux: 89.3%.
Gemini Diffusion supports an unknown number of tokens and LongCat-Flash-Thinking supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.
Gemini Diffusion is developed by Google and LongCat-Flash-Thinking is developed by Meituan.