Model Comparison

Kimi-k1.5 vs Qwen3-235B-A22B-Thinking-2507

Qwen3-235B-A22B-Thinking-2507 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

Kimi-k1.5 outperforms in 0 benchmarks, while Qwen3-235B-A22B-Thinking-2507 is better at 1 benchmark (IFEval).

Qwen3-235B-A22B-Thinking-2507 significantly outperforms across most benchmarks.

Thu Apr 30 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
Thu Apr 30 2026 • llm-stats.com
Moonshot AI
Kimi-k1.5
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input tokens$0.30
Output tokens$3.00
Best providerFireworks
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Only Qwen3-235B-A22B-Thinking-2507 specifies input context (262,144 tokens). Only Qwen3-235B-A22B-Thinking-2507 specifies output context (131,072 tokens).

Moonshot AI
Kimi-k1.5
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input262,144 tokens
Output131,072 tokens
Thu Apr 30 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Kimi-k1.5 supports multimodal inputs, whereas Qwen3-235B-A22B-Thinking-2507 does not.

Kimi-k1.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.

Kimi-k1.5

Text
Images
Audio
Video

Qwen3-235B-A22B-Thinking-2507

Text
Images
Audio
Video

License

Usage and distribution terms

Kimi-k1.5 is licensed under a proprietary license, while Qwen3-235B-A22B-Thinking-2507 uses Apache 2.0.

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

Kimi-k1.5

Proprietary

Closed source

Qwen3-235B-A22B-Thinking-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

Kimi-k1.5 was released on 2025-01-20, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.

Qwen3-235B-A22B-Thinking-2507 is 6 months newer than Kimi-k1.5.

Kimi-k1.5

Jan 20, 2025

1.3 years ago

Qwen3-235B-A22B-Thinking-2507

Jul 25, 2025

9 months ago

6mo 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

Moonshot AI
Supports multimodal inputs
Alibaba Cloud / Qwen Team
Larger context window (262,144 tokens)
Has open weights
Higher IFEval score (87.8% vs 87.2%)

Detailed Comparison

AI Model Comparison Table
Feature
Moonshot AI
Kimi-k1.5
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507

FAQ

Common questions about Kimi-k1.5 vs Qwen3-235B-A22B-Thinking-2507

Qwen3-235B-A22B-Thinking-2507 significantly outperforms across most benchmarks. Kimi-k1.5 is made by Moonshot AI and Qwen3-235B-A22B-Thinking-2507 is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Kimi-k1.5 scores MATH-500: 96.2%, CLUEWSC: 91.4%, C-Eval: 88.3%, MMLU: 87.4%, IFEval: 87.2%. Qwen3-235B-A22B-Thinking-2507 scores MMLU-Redux: 93.8%, AIME 2025: 92.3%, WritingBench: 88.3%, IFEval: 87.8%, Creative Writing v3: 86.1%.
Kimi-k1.5 supports an unknown number of tokens and Qwen3-235B-A22B-Thinking-2507 supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (yes vs no), licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
Kimi-k1.5 is developed by Moonshot AI and Qwen3-235B-A22B-Thinking-2507 is developed by Alibaba Cloud / Qwen Team.