GLM-4.6 vs Kimi K2 0905
GLM-4.6 leads the LLM Stats Score 28.7 to 21.4. GLM-4.6 is 1.2x cheaper per token.
Zhipu AI · Moonshot AI · Updated for 2026
Which is better?
GLM-4.6 leads the overall LLM Stats Score 28.7 to 21.4, ranking #154 overall.
In the 1 individual benchmarks reported for both models, GLM-4.6 wins 1; this is a narrower head-to-head signal than the composite indexes.
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 LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-4.6
- overall performance matters — it scores 28.7 and ranks #154 on LLM Stats
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- 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.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
7 reported for GLM-4.6 · 6 for Kimi K2 0905
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.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.6 ($0.50/1M tokens) is 1.2x 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 202,752 tokens. Kimi K2 0905 can generate longer responses up to 262,144 tokens, while GLM-4.6 is limited to 202,752 tokens.
Input capabilities
Documented input modalities across available providers
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
1.1 years ago
3w newerSep 5, 2025
1.1 years 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 DeepInfra, Fireworks. 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.