Kimi K2.5 vs GLM-4.7 Comparison
Performance Benchmarks
Comparative analysis across standard metrics
Kimi K2.5 outperforms in 9 benchmarks (AIME 2025, BrowseComp, GPQA, Humanity's Last Exam, LiveCodeBench v6, MMLU-Pro, SWE-bench Multilingual, SWE-Bench Verified, Terminal-Bench 2.0), while GLM-4.7 is better at 1 benchmark (IMO-AnswerBench).
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 642.0B more parameters than GLM-4.7, making it 179.3% larger.
Context Window
Maximum input and output token capacity
Only GLM-4.7 specifies input context (202,800 tokens). Only GLM-4.7 specifies output context (131,072 tokens).
Input Capabilities
Supported data types and modalities
Both Kimi K2.5 and GLM-4.7 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Kimi K2.5
GLM-4.7
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 was released on 2025-12-22.
Kimi K2.5 is 1 month newer than GLM-4.7.
Jan 27, 2026
1 months ago
1mo newerDec 22, 2025
2 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
View detailsZhipu AI