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DeepSeek-R1 vs Kimi-k1.5

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

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-R1 and Kimi-k1.5 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose DeepSeek-R1

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

Choose Kimi-k1.5

  • you are already invested in the Moonshot AI ecosystem

At a glance

The differences that matter most.

DeepSeek-R1
DeepSeek
Kimi-k1.5
Moonshot AI
Benchmark wins
—
—
Input price
$0.55 / M
— / M
Output price
$2.19 / M
— / M
Context window
131,072
—
Released
Jan 2025
Jan 2025
License
MIT
Proprietary

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

Playground indexes and blind preference scores

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
Thu Aug 27 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

1.6 years ago

Kimi-k1.5

Jan 20, 2025

1.6 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→

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

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.

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