GLM-4.6 vs Kimi K2 0905
GLM-4.6 significantly outperforms across most benchmarks. GLM-4.6 is 1.2x cheaper per token.
Zhipu AI · Moonshot AI · Updated for 2026
Which is better?
GLM-4.6 outperforms in 1 benchmarks (GPQA), while Kimi K2 0905 is better at 0 benchmarks. GLM-4.6 significantly outperforms across most benchmarks.
On price, GLM-4.6 is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K2 0905 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose GLM-4.6
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- cost matters — it's about 1.2x cheaper per token
- you want the most recent training data — it shipped Sep 2025
- you need open weights you can self-host or fine-tune
Choose Kimi K2 0905
- you process long inputs — it offers a 262,144 token context window
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.6 outperforms in 1 benchmarks (GPQA), while Kimi K2 0905 is better at 0 benchmarks.
GLM-4.6 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.6 ($0.55/1M tokens) is 1.1x cheaper than Kimi K2 0905 ($0.60/1M tokens).
For output processing, GLM-4.6 ($2.00/1M tokens) is 1.3x cheaper than Kimi K2 0905 ($2.50/1M tokens).
In conclusion, Kimi K2 0905 is more expensive than GLM-4.6.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2 0905 has 643.0B more parameters than GLM-4.6, making it 180.1% larger.
Context Window
Maximum input and output token capacity
Kimi K2 0905 accepts 262,144 input tokens compared to GLM-4.6's 131,072 tokens. Kimi K2 0905 can generate longer responses up to 262,144 tokens, while GLM-4.6 is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
GLM-4.6 supports multimodal inputs, whereas Kimi K2 0905 does not.
GLM-4.6 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.6
Kimi K2 0905
License
Usage and distribution terms
GLM-4.6 is licensed under MIT, while Kimi K2 0905 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
GLM-4.6 was released on 2025-09-30, while Kimi K2 0905 was released on 2025-09-05.
GLM-4.6 is 1 month newer than Kimi K2 0905.
Sep 30, 2025
11 months ago
3w newerSep 5, 2025
11 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
GLM-4.6 is available from Fireworks, DeepInfra. Kimi K2 0905 is available from Novita.
GLM-4.6
Kimi K2 0905
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.6 and Kimi K2 0905 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.6 vs Kimi K2 0905.