Model Comparison

DeepSeek-V3 vs Kimi-k1.5

Kimi-k1.5 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

6 benchmarks

DeepSeek-V3 outperforms in 1 benchmarks (MMLU), while Kimi-k1.5 is better at 5 benchmarks (AIME 2024, C-Eval, CLUEWSC, IFEval, MATH-500).

Kimi-k1.5 significantly outperforms across most benchmarks.

Mon Mar 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
Mon Mar 30 2026 • llm-stats.com
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
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-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Moonshot AI
Kimi-k1.5
Input- tokens
Output- tokens
Mon Mar 30 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Kimi-k1.5 supports multimodal inputs, whereas DeepSeek-V3 does not.

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

DeepSeek-V3

Text
Images
Audio
Video

Kimi-k1.5

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), 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-V3

MIT + Model License (Commercial use allowed)

Open weights

Kimi-k1.5

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Kimi-k1.5 was released on 2025-01-20.

Kimi-k1.5 is 1 month newer than DeepSeek-V3.

DeepSeek-V3

Dec 25, 2024

1.3 years ago

Kimi-k1.5

Jan 20, 2025

1.2 years ago

3w 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 (131,072 tokens)
Has open weights
Higher MMLU score (88.5% vs 87.4%)
Moonshot AI
Supports multimodal inputs
Higher AIME 2024 score (77.5% vs 39.2%)
Higher C-Eval score (88.3% vs 86.5%)
Higher CLUEWSC score (91.4% vs 90.9%)
Higher IFEval score (87.2% vs 86.1%)
Higher MATH-500 score (96.2% vs 90.2%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3
Moonshot AI
Kimi-k1.5

FAQ

Common questions about DeepSeek-V3 vs Kimi-k1.5

Kimi-k1.5 significantly outperforms across most benchmarks. DeepSeek-V3 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-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Kimi-k1.5 scores MATH-500: 96.2%, CLUEWSC: 91.4%, C-Eval: 88.3%, MMLU: 87.4%, IFEval: 87.2%.
DeepSeek-V3 supports 131K 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 (MIT + Model License (Commercial use allowed) vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3 is developed by DeepSeek and Kimi-k1.5 is developed by Moonshot AI.