Model Comparison
Kimi K2.7 Code vs GLM-5.1Which is better in 2026?
Kimi K2.7 Code significantly outperforms across most benchmarks. Kimi K2.7 Code is 1.5x cheaper per token.
Verdict: Kimi K2.7 Code vs GLM-5.1 — which is better?
Kimi K2.7 Code (by Moonshot AI) and GLM-5.1 (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 K2.7 Code outperforms in 2 benchmarks (LiveBench, MCP Atlas), while GLM-5.1 is better at 0 benchmarks. Kimi K2.7 Code significantly outperforms across most benchmarks.
On price, Kimi K2.7 Code is roughly 1.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K2.7 Code also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose Kimi K2.7 Code if…
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- cost matters — it's about 1.5x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Jun 2026
Choose GLM-5.1 if…
- you want predictable pricing at $1.40/M input and $4.40/M output
Performance Benchmarks
Comparative analysis across standard metrics
Kimi K2.7 Code outperforms in 2 benchmarks (LiveBench, MCP Atlas), while GLM-5.1 is better at 0 benchmarks.
Kimi K2.7 Code significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Kimi K2.7 Code ($0.74/1M tokens) is 1.9x cheaper than GLM-5.1 ($1.40/1M tokens).
For output processing, Kimi K2.7 Code ($3.50/1M tokens) is 1.3x cheaper than GLM-5.1 ($4.40/1M tokens).
In conclusion, GLM-5.1 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 246.0B more parameters than GLM-5.1, making it 32.6% larger.
Context Window
Maximum input and output token capacity
Kimi K2.7 Code accepts 262,144 input tokens compared to GLM-5.1's 200,000 tokens. Kimi K2.7 Code can generate longer responses up to 131,072 tokens, while GLM-5.1 is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Kimi K2.7 Code supports multimodal inputs, whereas GLM-5.1 does not.
Kimi K2.7 Code can handle both text and other forms of data like images, making it suitable for multimodal applications.
Kimi K2.7 Code
GLM-5.1
License
Usage and distribution terms
Kimi K2.7 Code is licensed under Modified MIT License, while GLM-5.1 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Modified MIT License
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Kimi K2.7 Code was released on 2026-06-12, while GLM-5.1 was released on 2026-04-07.
Kimi K2.7 Code is 2 months newer than GLM-5.1.
Jun 12, 2026
1 months ago
2mo newerApr 7, 2026
3 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 K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together. GLM-5.1 is available from FriendliAI, ZAI.
Kimi K2.7 Code
GLM-5.1
Outputs Comparison
Key Takeaways
Kimi K2.7 Code
View detailsMoonshot AI
GLM-5.1
View detailsZhipu AI
No standout differentiators in the data we have for this pair.
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Kimi K2.7 Code and GLM-5.1 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about Kimi K2.7 Code vs GLM-5.1.