Model Comparison

DeepSeek-R1-0528 vs Kimi K2 Base

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-R1-0528 outperforms in 3 benchmarks (GPQA, MMLU-Pro, SimpleQA), while Kimi K2 Base is better at 0 benchmarks.

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

Tue May 26 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

329.0B diff

Kimi K2 Base has 329.0B more parameters than DeepSeek-R1-0528, making it 49.0% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
Moonshot AI
Kimi K2 Base
1.0Tparameters
671.0B
DeepSeek-R1-0528
1000.0B
Kimi K2 Base

Context Window

Maximum input and output token capacity

Only DeepSeek-R1-0528 specifies input context (131,072 tokens). Only DeepSeek-R1-0528 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
Moonshot AI
Kimi K2 Base
Input- tokens
Output- tokens
Tue May 26 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-R1-0528

MIT

Open weights

Kimi K2 Base

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while Kimi K2 Base was released on 2025-07-11.

Kimi K2 Base is 1 month newer than DeepSeek-R1-0528.

DeepSeek-R1-0528

May 28, 2025

12 months ago

Kimi K2 Base

Jul 11, 2025

10 months 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 (131,072 tokens)
Higher GPQA score (81.0% vs 48.1%)
Higher MMLU-Pro score (85.0% vs 69.2%)
Higher SimpleQA score (92.3% vs 35.3%)
Moonshot AI

No standout differentiators in the data we have for this pair.

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
Moonshot AI
Kimi K2 Base

FAQ

Common questions about DeepSeek-R1-0528 vs Kimi K2 Base.

Which is better, DeepSeek-R1-0528 or Kimi K2 Base?

DeepSeek-R1-0528 significantly outperforms across most benchmarks. DeepSeek-R1-0528 is made by DeepSeek and Kimi K2 Base is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-R1-0528 compare to Kimi K2 Base in benchmarks?

DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. Kimi K2 Base scores C-Eval: 92.5%, GSM8k: 92.1%, MMLU-redux-2.0: 90.2%, MMLU: 87.8%, TriviaQA: 85.1%.

What are the context window sizes for DeepSeek-R1-0528 and Kimi K2 Base?

DeepSeek-R1-0528 supports 131K tokens and Kimi K2 Base supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Who makes DeepSeek-R1-0528 and Kimi K2 Base?

DeepSeek-R1-0528 is developed by DeepSeek and Kimi K2 Base is developed by Moonshot AI.