GLM-4.7-Flash vs o4-mini
GLM-4.7-Flash and o4-mini are closely matched at 23.8 and 27.7 on the LLM Stats Score. GLM-4.7-Flash is 12.6x cheaper per token.
Zhipu AI · OpenAI · Updated for 2026
Which is better?
GLM-4.7-Flash and o4-mini are closely matched on the overall LLM Stats Score at 23.8 and 27.7.
In the 5 individual benchmarks reported for both models, o4-mini wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-4.7-Flash is roughly 12.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
o4-mini also accepts a larger context window (200,000 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-Flash
- cost matters — it's about 12.6x cheaper per token
- you want the most recent training data — it shipped Jan 2026
- you need open weights you can self-host or fine-tune
Choose o4-mini
- you value its reported benchmark strengths — it wins 5 of 5 exact shared results
- you process long inputs — it offers a 200,000 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
6 reported for GLM-4.7-Flash · 14 for o4-mini
GLM-4.7-Flash outperforms in 0 benchmarks, while o4-mini is better at 5 benchmarks (AIME 2025, BrowseComp, GPQA, Humanity's Last Exam, SWE-Bench Verified).
o4-mini 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-4.7-Flash ($0.07/1M tokens) is 15.7x cheaper than o4-mini ($1.10/1M tokens).
For output processing, GLM-4.7-Flash ($0.40/1M tokens) is 11.0x cheaper than o4-mini ($4.40/1M tokens).
In conclusion, o4-mini is more expensive than GLM-4.7-Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
o4-mini accepts 200,000 input tokens compared to GLM-4.7-Flash's 128,000 tokens. o4-mini can generate longer responses up to 100,000 tokens, while GLM-4.7-Flash is limited to 16,384 tokens.
Input capabilities
Documented input modalities across available providers
o4-mini supports multimodal inputs, whereas GLM-4.7-Flash does not.
o4-mini can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.7-Flash
o4-mini
License
Usage and distribution terms
GLM-4.7-Flash is licensed under MIT, while o4-mini 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-Flash was released on 2026-01-19, while o4-mini was released on 2025-04-16.
GLM-4.7-Flash is 9 months newer than o4-mini.
Jan 19, 2026
7 months ago
9mo newerApr 16, 2025
1.4 years ago
Knowledge Cutoff
When training data ends
o4-mini has a documented knowledge cutoff of 2024-05-31, while GLM-4.7-Flash's cutoff date is not specified.
We can confirm o4-mini's training data extends to 2024-05-31, but cannot make a direct comparison without GLM-4.7-Flash's cutoff date.
—
May 2024
Provider Availability
GLM-4.7-Flash is available from ZAI. o4-mini is available from OpenAI.
GLM-4.7-Flash
o4-mini
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.7-Flash and o4-mini side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.7-Flash vs o4-mini.