GLM-4.6 vs Kimi K2 Instruct
GLM-4.6 leads the LLM Stats Score 28.5 to 21.4. Kimi K2 Instruct is 1.8x cheaper per token.
Zhipu AI · Moonshot AI · Updated for 2026
Which is better?
GLM-4.6 leads the overall LLM Stats Score 28.5 to 21.4, ranking #156 overall.
In the 5 individual benchmarks reported for both models, GLM-4.6 wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, Kimi K2 Instruct is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-4.6 also accepts a larger context window (202,752 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 28.5 and ranks #156 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 5 of 5 exact shared results
- you process long inputs — it offers a 202,752 token context window
- you want the most recent training data — it shipped Sep 2025
Choose Kimi K2 Instruct
- cost matters — it's about 1.8x cheaper per token
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 · 38 for Kimi K2 Instruct
GLM-4.6 outperforms in 5 benchmarks (AIME 2025, GPQA, Humanity's Last Exam, LiveCodeBench v6, Terminal-Bench), while Kimi K2 Instruct 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) costs the same as Kimi K2 Instruct ($0.50/1M tokens).
For output processing, GLM-4.6 ($2.00/1M tokens) is 4.0x more expensive than Kimi K2 Instruct ($0.50/1M tokens).
In conclusion, GLM-4.6 is more expensive than Kimi K2 Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2 Instruct has 643.0B more parameters than GLM-4.6, making it 180.1% larger.
Context Window
Maximum input and output token capacity
GLM-4.6 accepts 202,752 input tokens compared to Kimi K2 Instruct's 200,000 tokens. GLM-4.6 can generate longer responses up to 202,752 tokens, while Kimi K2 Instruct is limited to 200,000 tokens.
Input capabilities
Documented input modalities across available providers
GLM-4.6 supports multimodal inputs, whereas Kimi K2 Instruct 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 Instruct
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
GLM-4.6 was released on 2025-09-30, while Kimi K2 Instruct was released on 2025-07-11.
GLM-4.6 is 3 months newer than Kimi K2 Instruct.
Sep 30, 2025
1.1 years ago
2mo newerJul 11, 2025
1.3 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 Instruct is available from Fireworks, Novita.
GLM-4.6
Kimi K2 Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.6 and Kimi K2 Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.6 vs Kimi K2 Instruct.