Model Comparison
Kimi K2.5 vs GLM-4.6Which is better in 2026?
Kimi K2.5 significantly outperforms across most benchmarks. GLM-4.6 is 1.3x cheaper per token.
Verdict: Kimi K2.5 vs GLM-4.6 — which is better?
Kimi K2.5 (by Moonshot AI) and GLM-4.6 (by Zhipu AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
Kimi K2.5 outperforms in 6 benchmarks (AIME 2025, BrowseComp, GPQA, Humanity's Last Exam, LiveCodeBench v6, SWE-Bench Verified), while GLM-4.6 is better at 0 benchmarks. Kimi K2.5 significantly outperforms across most benchmarks.
On price, GLM-4.6 is roughly 1.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K2.5 also accepts a larger context window (262,100 input tokens), making it the stronger choice for long documents and large codebases.
Choose Kimi K2.5 if…
- you want the strongest raw capability — it leads on 6 of 6 shared benchmarks
- you process long inputs — it offers a 262,100 token context window
- you want the most recent training data — it shipped Jan 2026
Choose GLM-4.6 if…
- cost matters — it's about 1.3x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
Kimi K2.5 outperforms in 6 benchmarks (AIME 2025, BrowseComp, GPQA, Humanity's Last Exam, LiveCodeBench v6, SWE-Bench Verified), while GLM-4.6 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
For input processing, Kimi K2.5 ($0.60/1M tokens) is 1.1x more expensive than GLM-4.6 ($0.55/1M tokens).
For output processing, Kimi K2.5 ($3.00/1M tokens) is 1.5x more expensive than GLM-4.6 ($2.00/1M tokens).
In conclusion, Kimi K2.5 is more expensive than GLM-4.6.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.5 has 643.0B more parameters than GLM-4.6, making it 180.1% larger.
Context Window
Maximum input and output token capacity
Kimi K2.5 accepts 262,100 input tokens compared to GLM-4.6's 131,072 tokens. Kimi K2.5 can generate longer responses up to 262,100 tokens, while GLM-4.6 is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Both Kimi K2.5 and GLM-4.6 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Kimi K2.5
GLM-4.6
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-4.6 was released on 2025-09-30.
Kimi K2.5 is 4 months newer than GLM-4.6.
Jan 27, 2026
5 months ago
3mo newerSep 30, 2025
9 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Kimi K2.5 is available from Fireworks, Moonshot AI. GLM-4.6 is available from Fireworks, DeepInfra.
Kimi K2.5
GLM-4.6
Outputs Comparison
Key Takeaways
Kimi K2.5
View detailsMoonshot AI
GLM-4.6
View detailsZhipu AI
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Kimi K2.5 and GLM-4.6 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2.5 vs GLM-4.6.