Model Comparison

DeepSeek-V3.1 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-V3.1 outperforms in 0 benchmarks, while Kimi-k1.5 is better at 1 benchmark (AIME 2024).

Kimi-k1.5 significantly outperforms across most benchmarks.

Wed Apr 22 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
Wed Apr 22 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.1
Input tokens$0.27
Output tokens$1.00
Best providerDeepinfra
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-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Moonshot AI
Kimi-k1.5
Input- tokens
Output- tokens
Wed Apr 22 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Kimi-k1.5 supports multimodal inputs, whereas DeepSeek-V3.1 does not.

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

DeepSeek-V3.1

Text
Images
Audio
Video

Kimi-k1.5

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, 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-V3.1

MIT

Open weights

Kimi-k1.5

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Kimi-k1.5 was released on 2025-01-20.

Kimi-k1.5 is 0 month newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

Kimi-k1.5

Jan 20, 2025

1.3 years ago

1w 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 (163,840 tokens)
Has open weights
Moonshot AI
Supports multimodal inputs
Higher AIME 2024 score (77.5% vs 66.3%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
Moonshot AI
Kimi-k1.5

FAQ

Common questions about DeepSeek-V3.1 vs Kimi-k1.5

Kimi-k1.5 significantly outperforms across most benchmarks. DeepSeek-V3.1 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-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Kimi-k1.5 scores MATH-500: 96.2%, CLUEWSC: 91.4%, C-Eval: 88.3%, MMLU: 87.4%, IFEval: 87.2%.
DeepSeek-V3.1 supports 164K 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 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3.1 is developed by DeepSeek and Kimi-k1.5 is developed by Moonshot AI.