GLM-4.7 vs GPT-5
GLM-4.7 and GPT-5 are closely matched at 34.2 and 33.7 on the LLM Stats Score. GLM-4.7 is 4.7x cheaper per token.
Zhipu AI · OpenAI · Updated for 2026
Which is better?
GLM-4.7 and GPT-5 are closely matched on the overall LLM Stats Score at 34.2 and 33.7.
In the 5 individual benchmarks reported for both models, GPT-5 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-4.7 is roughly 4.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-5 also accepts a larger context window (400,000 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-4.7
- cost matters — it's about 4.7x cheaper per token
- you want the most recent training data — it shipped Dec 2025
- you need open weights you can self-host or fine-tune
Choose GPT-5
- your work emphasizes coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 5 exact shared results
- you process long inputs — it offers a 400,000 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
13 reported for GLM-4.7 · 34 for GPT-5
GLM-4.7 outperforms in 2 benchmarks (AIME 2025, Humanity's Last Exam), while GPT-5 is better at 2 benchmarks (BrowseComp, SWE-Bench Verified).
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-4.7 ($0.40/1M tokens) is 3.1x cheaper than GPT-5 ($1.25/1M tokens).
For output processing, GLM-4.7 ($1.75/1M tokens) is 5.7x cheaper than GPT-5 ($10.00/1M tokens).
In conclusion, GPT-5 is more expensive than GLM-4.7.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-5 accepts 400,000 input tokens compared to GLM-4.7's 202,752 tokens. GLM-4.7 can generate longer responses up to 202,752 tokens, while GPT-5 is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both GLM-4.7 and GPT-5 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.7
GPT-5
License
Usage and distribution terms
GLM-4.7 is licensed under MIT, while GPT-5 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-4.7 was released on 2025-12-22, while GPT-5 was released on 2025-08-07.
GLM-4.7 is 5 months newer than GPT-5.
Dec 22, 2025
9 months ago
4mo newerAug 7, 2025
1.2 years ago
Knowledge Cutoff
When training data ends
GPT-5 has a documented knowledge cutoff of 2024-09-30, while GLM-4.7's cutoff date is not specified.
We can confirm GPT-5's training data extends to 2024-09-30, but cannot make a direct comparison without GLM-4.7's cutoff date.
—
Sep 2024
Provider Availability
GLM-4.7 is available from DeepInfra, Fireworks, Novita. GPT-5 is available from OpenAI.
GLM-4.7
GPT-5
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.7 and GPT-5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.7 vs GPT-5.