Kimi K2.5 vs DeepSeek-V3.2 (Non-thinking) Comparison
Comparing Kimi K2.5 and DeepSeek-V3.2 (Non-thinking) across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
Kimi K2.5 and DeepSeek-V3.2 (Non-thinking) don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
Kimi K2.5 has 315.0B more parameters than DeepSeek-V3.2 (Non-thinking), making it 46.0% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2 (Non-thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Non-thinking) specifies output context (8,192 tokens).
Input Capabilities
Supported data types and modalities
Kimi K2.5 supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) does not.
Kimi K2.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Kimi K2.5
DeepSeek-V3.2 (Non-thinking)
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Kimi K2.5 was released on 2026-01-27, while DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01.
Kimi K2.5 is 2 months newer than DeepSeek-V3.2 (Non-thinking).
Jan 27, 2026
1 months ago
1mo newerDec 1, 2025
3 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
Kimi K2.5
View detailsMoonshot AI
Detailed Comparison
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