GLM-5.3-Flash vs GPT-5.2 Codex
GLM-5.3-Flash leads the LLM Stats Score 51.1 to 34.6. GLM-5.3-Flash is 20.3x cheaper per token.
Zhipu AI · OpenAI · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 51.1 to 34.6, ranking #12 overall.
On price, GLM-5.3-Flash is roughly 20.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3-Flash also accepts a larger context window (1,048,576 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 51.1 and ranks #12 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- cost matters — it's about 20.3x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
- you need open weights you can self-host or fine-tune
Choose GPT-5.2 Codex
- you want predictable pricing at $1.75/M input and $14.00/M output
At a glance
The differences that matter most.
Individual benchmarks
15 reported for GLM-5.3-Flash · 3 for GPT-5.2 Codex
GLM-5.3-Flash and GPT-5.2 Codexdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
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 11.7x cheaper than GPT-5.2 Codex ($1.75/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 28.0x cheaper than GPT-5.2 Codex ($14.00/1M tokens).
In conclusion, GPT-5.2 Codex 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
GLM-5.3-Flash accepts 1,048,576 input tokens compared to GPT-5.2 Codex's 400,000 tokens. GLM-5.3-Flash can generate longer responses up to 131,072 tokens, while GPT-5.2 Codex is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both GLM-5.3-Flash and GPT-5.2 Codex support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
GPT-5.2 Codex
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while GPT-5.2 Codex 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-5.2 Codex was released on 2026-01-14.
GLM-5.3-Flash is 7 months newer than GPT-5.2 Codex.
Aug 26, 2026
6 days ago
7mo newerJan 14, 2026
7 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
GLM-5.3-Flash is available from DeepInfra, FriendliAI, Novita, ZAI. GPT-5.2 Codex is available from OpenAI.
GLM-5.3-Flash
GPT-5.2 Codex
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and GPT-5.2 Codex side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs GPT-5.2 Codex.