Model Comparison

DeepSeek-V2.5 vs Kimi K2-Instruct-0905

Kimi K2-Instruct-0905 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V2.5 outperforms in 0 benchmarks, while Kimi K2-Instruct-0905 is better at 2 benchmarks (MMLU, SWE-Bench Verified).

Kimi K2-Instruct-0905 significantly outperforms across most benchmarks.

Mon Apr 20 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Mon Apr 20 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
Moonshot AI
Kimi K2-Instruct-0905
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

764.0B diff

Kimi K2-Instruct-0905 has 764.0B more parameters than DeepSeek-V2.5, making it 323.7% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
Moonshot AI
Kimi K2-Instruct-0905
1000.0Bparameters
236.0B
DeepSeek-V2.5
1000.0B
Kimi K2-Instruct-0905

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-Instruct-0905
Input- tokens
Output- tokens
Mon Apr 20 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while Kimi K2-Instruct-0905 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-Instruct-0905

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Kimi K2-Instruct-0905 was released on 2025-09-05.

Kimi K2-Instruct-0905 is 16 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

2.0 years ago

Kimi K2-Instruct-0905

Sep 5, 2025

7 months ago

1.3yr 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)
Moonshot AI
Higher MMLU score (89.5% vs 80.4%)
Higher SWE-Bench Verified score (65.8% vs 16.8%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Moonshot AI
Kimi K2-Instruct-0905

FAQ

Common questions about DeepSeek-V2.5 vs Kimi K2-Instruct-0905

Kimi K2-Instruct-0905 significantly outperforms across most benchmarks. DeepSeek-V2.5 is made by DeepSeek and Kimi K2-Instruct-0905 is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. Kimi K2-Instruct-0905 scores MATH-500: 97.4%, MMLU-Redux: 92.7%, IFEval: 89.8%, AutoLogi: 89.5%, MMLU: 89.5%.
DeepSeek-V2.5 supports 8K tokens and Kimi K2-Instruct-0905 supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (deepseek vs MIT). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V2.5 is developed by DeepSeek and Kimi K2-Instruct-0905 is developed by Moonshot AI.