Model Comparison

DeepSeek VL2 vs Kimi-k1.5

Kimi-k1.5 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek VL2 outperforms in 0 benchmarks, while Kimi-k1.5 is better at 2 benchmarks (MathVista, MMMU).

Kimi-k1.5 significantly outperforms across most benchmarks.

Wed May 13 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only DeepSeek VL2 specifies input context (129,280 tokens). Only DeepSeek VL2 specifies output context (129,280 tokens).

DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Moonshot AI
Kimi-k1.5
Input- tokens
Output- tokens
Wed May 13 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both DeepSeek VL2 and Kimi-k1.5 support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

DeepSeek VL2

Text
Images
Audio
Video

Kimi-k1.5

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 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 VL2

deepseek

Open weights

Kimi-k1.5

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek VL2 was released on 2024-12-13, while Kimi-k1.5 was released on 2025-01-20.

Kimi-k1.5 is 1 month newer than DeepSeek VL2.

DeepSeek VL2

Dec 13, 2024

1.4 years ago

Kimi-k1.5

Jan 20, 2025

1.3 years ago

1mo 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 (129,280 tokens)
Has open weights
Moonshot AI
Higher MathVista score (74.9% vs 62.8%)
Higher MMMU score (70.0% vs 51.1%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek VL2
Moonshot AI
Kimi-k1.5

FAQ

Common questions about DeepSeek VL2 vs Kimi-k1.5.

Which is better, DeepSeek VL2 or Kimi-k1.5?

Kimi-k1.5 significantly outperforms across most benchmarks. DeepSeek VL2 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 VL2 compare to Kimi-k1.5 in benchmarks?

DeepSeek VL2 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%. 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 VL2 and Kimi-k1.5?

DeepSeek VL2 supports 129K 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 VL2 and Kimi-k1.5?

Key differences include licensing (deepseek vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek VL2 and Kimi-k1.5?

DeepSeek VL2 is developed by DeepSeek and Kimi-k1.5 is developed by Moonshot AI.