Model Comparison

DeepSeek-V2.5 vs Kimi K2 Base

DeepSeek-V2.5 shows notably better performance in the majority of benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V2.5 outperforms in 2 benchmarks (GSM8k, MATH), while Kimi K2 Base is better at 1 benchmark (MMLU).

DeepSeek-V2.5 shows notably better performance in the majority of benchmarks.

Sat May 30 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

764.0B diff

Kimi K2 Base has 764.0B more parameters than DeepSeek-V2.5, making it 323.7% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
Moonshot AI
Kimi K2 Base
1.0Tparameters
236.0B
DeepSeek-V2.5
1000.0B
Kimi K2 Base

Context Window

Maximum input and output token capacity

Only DeepSeek-V2.5 specifies input context (8,192 tokens). Only DeepSeek-V2.5 specifies output context (8,192 tokens).

DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
Moonshot AI
Kimi K2 Base
Input- tokens
Output- tokens
Sat May 30 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while Kimi K2 Base uses MIT.

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

DeepSeek-V2.5

deepseek

Open weights

Kimi K2 Base

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Kimi K2 Base was released on 2025-07-11.

Kimi K2 Base is 14 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

2.1 years ago

Kimi K2 Base

Jul 11, 2025

10 months ago

1.2yr 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 (8,192 tokens)
Higher GSM8k score (95.1% vs 92.1%)
Higher MATH score (74.7% vs 70.2%)
Moonshot AI
Higher MMLU score (87.8% vs 80.4%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Moonshot AI
Kimi K2 Base

FAQ

Common questions about DeepSeek-V2.5 vs Kimi K2 Base.

Which is better, DeepSeek-V2.5 or Kimi K2 Base?

DeepSeek-V2.5 shows notably better performance in the majority of benchmarks. DeepSeek-V2.5 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-V2.5 compare to Kimi K2 Base in benchmarks?

DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. 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-V2.5 and Kimi K2 Base?

DeepSeek-V2.5 supports 8K 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-V2.5 and Kimi K2 Base?

Key differences include licensing (deepseek vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V2.5 and Kimi K2 Base?

DeepSeek-V2.5 is developed by DeepSeek and Kimi K2 Base is developed by Moonshot AI.