Kimi K2.5 vs GLM-4.7-Flash Comparison
Comparing Kimi K2.5 and GLM-4.7-Flash across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
Kimi K2.5 outperforms in 5 benchmarks (AIME 2025, BrowseComp, GPQA, Humanity's Last Exam, SWE-Bench Verified), while GLM-4.7-Flash is better at 0 benchmarks.
Kimi K2.5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
Kimi K2.5 has 970.0B more parameters than GLM-4.7-Flash, making it 3233.3% larger.
Context Window
Maximum input and output token capacity
Only GLM-4.7-Flash specifies input context (128,000 tokens). Only GLM-4.7-Flash specifies output context (16,384 tokens).
Input Capabilities
Supported data types and modalities
Kimi K2.5 supports multimodal inputs, whereas GLM-4.7-Flash 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
GLM-4.7-Flash
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 GLM-4.7-Flash was released on 2026-01-19.
Kimi K2.5 is 0 month newer than GLM-4.7-Flash.
Jan 27, 2026
1 months ago
1w newerJan 19, 2026
1 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
GLM-4.7-Flash
View detailsZhipu AI
Detailed Comparison
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