Model Comparison

DeepSeek-R1 vs Kimi-k1.5Which is better in 2026?

Comparing DeepSeek-R1 and Kimi-k1.5 across benchmarks, pricing, and capabilities.

Verdict: DeepSeek-R1 vs Kimi-k1.5 — which is better?

DeepSeek-R1 (by DeepSeek) and Kimi-k1.5 (by Moonshot AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Choose DeepSeek-R1 if…

  • you need open weights you can self-host or fine-tune

Choose Kimi-k1.5 if…

  • you are already invested in the Moonshot AI ecosystem

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1 and Kimi-k1.5don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
Moonshot AI
Kimi-k1.5
Input- tokens
Output- tokens
Sun Jun 28 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Kimi-k1.5 supports multimodal inputs, whereas DeepSeek-R1 does not.

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

DeepSeek-R1

Text
Images
Audio
Video

Kimi-k1.5

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1 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-R1

MIT

Open weights

Kimi-k1.5

Proprietary

Closed source

Release Timeline

When each model was launched

Both models were released on 2025-01-20.

They likely represent similar generations of model development.

DeepSeek-R1

Jan 20, 2025

1.4 years ago

Kimi-k1.5

Jan 20, 2025

1.4 years ago

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 (131,072 tokens)
Has open weights
Moonshot AI
Supports multimodal inputs

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against DeepSeek-R1 and Kimi-k1.5 side-by-side, then vote on the output you prefer.

DeepSeek-R1
✓ Preferred
Kimi-k1.5
Open in Playground
AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1
Moonshot AI
Kimi-k1.5

FAQ

Common questions about DeepSeek-R1 vs Kimi-k1.5.

Which is better, DeepSeek-R1 or Kimi-k1.5?

DeepSeek-R1 (DeepSeek) and Kimi-k1.5 (Moonshot AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-R1 compare to Kimi-k1.5 in benchmarks?

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-R1 and Kimi-k1.5?

DeepSeek-R1 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.

What are the main differences between DeepSeek-R1 and Kimi-k1.5?

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

Who makes DeepSeek-R1 and Kimi-k1.5?

DeepSeek-R1 is developed by DeepSeek and Kimi-k1.5 is developed by Moonshot AI.

Related comparisons