GLM-5.3-Flash vs GPT-5.6 Luna
GLM-5.3-Flash leads the LLM Stats Score 51.6 to 46.5. GLM-5.3-Flash is 1.9x cheaper per token.
Zhipu AI · OpenAI · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 51.6 to 46.5, ranking #11 overall.
The models split the 6 individual benchmarks reported for both models evenly.
On price, GLM-5.3-Flash is roughly 1.9x 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-5.3-Flash
- overall performance matters — it scores 51.6 and ranks #11 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- cost matters — it's about 1.9x cheaper per token
- you want the most recent training data — it shipped Aug 2026
- you need open weights you can self-host or fine-tune
Choose GPT-5.6 Luna
- you process long inputs — it offers a 1,050,000 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for GLM-5.3-Flash · 44 for GPT-5.6 Luna
GLM-5.3-Flash outperforms in 3 benchmarks (Artificial Analysis, AutomationBench, Toolathlon), while GPT-5.6 Luna is better at 3 benchmarks (Agents' Last Exam, DeepSWE 1.1, Terminal-Bench 2.1).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.3-Flash ($0.15/1M tokens) is 1.3x cheaper than GPT-5.6 Luna ($0.20/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 2.4x cheaper than GPT-5.6 Luna ($1.20/1M tokens).
In conclusion, GPT-5.6 Luna is more expensive than GLM-5.3-Flash.*
* 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-5.3-Flash's 1,048,576 tokens. GLM-5.3-Flash can generate longer responses up to 131,072 tokens, while GPT-5.6 Luna is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both GLM-5.3-Flash and GPT-5.6 Luna support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
GPT-5.6 Luna
License
Usage and distribution terms
GLM-5.3-Flash 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-5.3-Flash was released on 2026-08-26, while GPT-5.6 Luna was released on 2026-07-09.
GLM-5.3-Flash is 2 months newer than GPT-5.6 Luna.
Aug 26, 2026
2 days ago
1mo newerJul 9, 2026
1 months ago
Knowledge Cutoff
When training data ends
GPT-5.6 Luna has a documented knowledge cutoff of 2026-02-16, while GLM-5.3-Flash'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-5.3-Flash's cutoff date.
—
Feb 2026
Provider Availability
GLM-5.3-Flash is available from DeepInfra, Novita, ZAI. GPT-5.6 Luna is available from OpenAI.
GLM-5.3-Flash
GPT-5.6 Luna
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and GPT-5.6 Luna side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs GPT-5.6 Luna.