Model Comparison

DeepSeek-V3 vs Kimi K2 Base

Both models are evenly matched across the benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

6 benchmarks

DeepSeek-V3 outperforms in 3 benchmarks (GPQA, MMLU, MMLU-Pro), while Kimi K2 Base is better at 3 benchmarks (C-Eval, CSimpleQA, SimpleQA).

Both models are evenly matched across the benchmarks.

Mon May 11 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-V3, making it 49.0% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Moonshot AI
Kimi K2 Base
1.0Tparameters
671.0B
DeepSeek-V3
1000.0B
Kimi K2 Base

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Moonshot AI
Kimi K2 Base
Input- tokens
Output- tokens
Mon May 11 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Kimi K2 Base uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Kimi K2 Base

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Kimi K2 Base was released on 2025-07-11.

Kimi K2 Base is 7 months newer than DeepSeek-V3.

DeepSeek-V3

Dec 25, 2024

1.4 years ago

Kimi K2 Base

Jul 11, 2025

10 months ago

6mo 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 (131,072 tokens)
Higher GPQA score (59.1% vs 48.1%)
Higher MMLU score (88.5% vs 87.8%)
Higher MMLU-Pro score (75.9% vs 69.2%)
Moonshot AI
Higher C-Eval score (92.5% vs 86.5%)
Higher CSimpleQA score (77.6% vs 64.8%)
Higher SimpleQA score (35.3% vs 24.9%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3
Moonshot AI
Kimi K2 Base

FAQ

Common questions about DeepSeek-V3 vs Kimi K2 Base.

Which is better, DeepSeek-V3 or Kimi K2 Base?

Both models are evenly matched across the benchmarks. DeepSeek-V3 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-V3 compare to Kimi K2 Base in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. 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-V3 and Kimi K2 Base?

DeepSeek-V3 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.

What are the main differences between DeepSeek-V3 and Kimi K2 Base?

Key differences include licensing (MIT + Model License (Commercial use allowed) vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and Kimi K2 Base?

DeepSeek-V3 is developed by DeepSeek and Kimi K2 Base is developed by Moonshot AI.