GLM-5.2 vs GPT-5.6 Luna
GLM-5.2 and GPT-5.6 Luna are closely matched at 45.6 and 45.1 on the LLM Stats Score. GPT-5.6 Luna is 2.6x cheaper per token.
Zhipu AI · OpenAI · Updated for 2026
Which is better?
GLM-5.2 and GPT-5.6 Luna are closely matched on the overall LLM Stats Score at 45.6 and 45.1.
In the 7 individual benchmarks reported for both models, GPT-5.6 Luna wins 7; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-5.6 Luna is roughly 2.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5.6 Luna also accepts a larger context window (1,050,000 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
- you need open weights you can self-host or fine-tune
Choose GPT-5.6 Luna
- you value its reported benchmark strengths — it wins 7 of 7 exact shared results
- cost matters — it's about 2.6x cheaper per token
- you process long inputs — it offers a 1,050,000 token context window
- you want the most recent training data — it shipped Jul 2026
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 · 45 for GPT-5.6 Luna
GLM-5.2 outperforms in 0 benchmarks, while GPT-5.6 Luna is better at 7 benchmarks (DeepSWE, DeepSWE 1.1, FrontierCode 1.1, GPQA, SWE-Bench Pro, Terminal-Bench 2.1, Toolathlon).
GPT-5.6 Luna 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-5.2 ($0.75/1M tokens) is 3.8x more expensive than GPT-5.6 Luna ($0.20/1M tokens).
For output processing, GLM-5.2 ($2.40/1M tokens) is 2.0x more expensive than GPT-5.6 Luna ($1.20/1M tokens).
In conclusion, GLM-5.2 is more expensive than GPT-5.6 Luna.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-5.6 Luna accepts 1,050,000 input tokens compared to GLM-5.2's 1,048,576 tokens. GLM-5.2 can generate longer responses up to 1,048,576 tokens, while GPT-5.6 Luna is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
GPT-5.6 Luna supports multimodal inputs, whereas GLM-5.2 does not.
GPT-5.6 Luna can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
GPT-5.6 Luna
License
Usage and distribution terms
GLM-5.2 is licensed under MIT, while GPT-5.6 Luna 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-5.2 was released on 2026-06-16, while GPT-5.6 Luna was released on 2026-07-09.
GPT-5.6 Luna is 1 month newer than GLM-5.2.
Jun 16, 2026
3 months ago
Jul 9, 2026
2 months ago
3w newerKnowledge Cutoff
When training data ends
GPT-5.6 Luna has a documented knowledge cutoff of 2026-02-16, while GLM-5.2's cutoff date is not specified.
We can confirm GPT-5.6 Luna's training data extends to 2026-02-16, but cannot make a direct comparison without GLM-5.2's cutoff date.
—
Feb 2026
Provider Availability
GLM-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI. GPT-5.6 Luna is available from OpenAI.
GLM-5.2
GPT-5.6 Luna
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and GPT-5.6 Luna side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs GPT-5.6 Luna.