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.

Mon Apr 06 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
Mon Apr 06 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
Moonshot AI
Kimi-k1.5
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

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
Mon Apr 06 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

1.9 years ago

Kimi-k1.5

Jan 20, 2025

1.2 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

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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

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.
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%.
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.
Key differences include multimodal support (no vs yes), licensing (deepseek vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V2.5 is developed by DeepSeek and Kimi-k1.5 is developed by Moonshot AI.