GLM-5.2 vs Kimi K2.7 Code
GLM-5.2 and Kimi K2.7 Code are closely matched at 46.5 and 39.6 on the LLM Stats Score. Kimi K2.7 Code is 1.0x cheaper per token.
Zhipu AI · Moonshot AI · Updated for 2026
Which is better?
GLM-5.2 and Kimi K2.7 Code are closely matched on the overall LLM Stats Score at 46.5 and 39.6.
In the 4 individual benchmarks reported for both models, GLM-5.2 wins 3; this is a narrower head-to-head signal than the composite indexes.
GLM-5.2 also accepts a larger context window (1,048,576 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-5.2
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 4 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jun 2026
Choose Kimi K2.7 Code
- you want predictable pricing at $0.74/M input and $3.50/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
19 reported for GLM-5.2 · 9 for Kimi K2.7 Code
GLM-5.2 outperforms in 3 benchmarks (DeepSWE 1.1, MCP Atlas, Program Bench), while Kimi K2.7 Code is better at 1 benchmark (FrontierCode 1.1).
GLM-5.2 shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.2 ($0.95/1M tokens) is 1.3x more expensive than Kimi K2.7 Code ($0.74/1M tokens).
For output processing, GLM-5.2 ($3.00/1M tokens) is 1.2x cheaper than Kimi K2.7 Code ($3.50/1M tokens).
In conclusion, GLM-5.2 is more expensive than Kimi K2.7 Code.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.7 Code has 247.0B more parameters than GLM-5.2, making it 32.8% larger.
Context Window
Maximum input and output token capacity
GLM-5.2 accepts 1,048,576 input tokens compared to Kimi K2.7 Code's 262,144 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Kimi K2.7 Code supports multimodal inputs, whereas GLM-5.2 does not.
Kimi K2.7 Code can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
Kimi K2.7 Code
License
Usage and distribution terms
GLM-5.2 is licensed under MIT, while Kimi K2.7 Code uses Modified MIT License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Modified MIT License
Open weights
Release Timeline
When each model was launched
GLM-5.2 was released on 2026-06-16, while Kimi K2.7 Code was released on 2026-06-12.
GLM-5.2 is 0 month newer than Kimi K2.7 Code.
Jun 16, 2026
2 months ago
4d newerJun 12, 2026
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.
Provider Availability
GLM-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI. Kimi K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.
GLM-5.2
Kimi K2.7 Code
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and Kimi K2.7 Code side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs Kimi K2.7 Code.