Model Comparison

DeepSeek-V2.5 vs Kimi-k1.5

Kimi-k1.5 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek-V2.5 outperforms in 0 benchmarks, while Kimi-k1.5 is better at 1 benchmark (MMLU).

Kimi-k1.5 significantly outperforms across most benchmarks.

Fri Jun 05 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only DeepSeek-V2.5 specifies input context (8,192 tokens). Only DeepSeek-V2.5 specifies output context (8,192 tokens).

DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
Moonshot AI
Kimi-k1.5
Input- tokens
Output- tokens
Fri Jun 05 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Kimi-k1.5 supports multimodal inputs, whereas DeepSeek-V2.5 does not.

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

DeepSeek-V2.5

Text
Images
Audio
Video

Kimi-k1.5

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while Kimi-k1.5 uses a proprietary license.

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

DeepSeek-V2.5

deepseek

Open weights

Kimi-k1.5

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Kimi-k1.5 was released on 2025-01-20.

Kimi-k1.5 is 9 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

2.1 years ago

Kimi-k1.5

Jan 20, 2025

1.4 years ago

8mo 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?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (8,192 tokens)
Has open weights
Moonshot AI
Supports multimodal inputs
Higher MMLU score (87.4% vs 80.4%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Moonshot AI
Kimi-k1.5

FAQ

Common questions about DeepSeek-V2.5 vs Kimi-k1.5.

Which is better, DeepSeek-V2.5 or Kimi-k1.5?

Kimi-k1.5 significantly outperforms across most benchmarks. DeepSeek-V2.5 is made by DeepSeek and Kimi-k1.5 is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V2.5 compare to Kimi-k1.5 in benchmarks?

DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. Kimi-k1.5 scores MATH-500: 96.2%, CLUEWSC: 91.4%, C-Eval: 88.3%, MMLU: 87.4%, IFEval: 87.2%.

What are the context window sizes for DeepSeek-V2.5 and Kimi-k1.5?

DeepSeek-V2.5 supports 8K tokens and Kimi-k1.5 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 DeepSeek-V2.5 and Kimi-k1.5?

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

Who makes DeepSeek-V2.5 and Kimi-k1.5?

DeepSeek-V2.5 is developed by DeepSeek and Kimi-k1.5 is developed by Moonshot AI.