GLM 5.3
9226°
Run this model
Offers / Ads
Z.ai | 1,048,576 token context | $0.08/M input, $4.80/M output. GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves on GLM-5.2 in coding and in the balance between performance and token efficiency. Reasoning is always on and cannot be disabled. Reasoning efforts low, high, and max are supported; max is the default.
Guides for this model
Best local LLM by use case (October 2026): the right open model for every job
Ollama vs LM Studio (2026): which local AI app to use, and which top models each one runs
How much VRAM do I actually need for local AI?
What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)
Captain's notes
Notes from 2026-10-13
GLM-5.3 is Z.ai's open-weights reasoning model built around complex software engineering and long-horizon agent tasks, with a 1-million-token context window. Every improvement over GLM-5.2 comes from post-training on the same base, with the biggest gains in multi-step coding and, notably, offensive security. It suits developers and researchers who want a capable coding and agent model they can run locally.
Known for
- Open-weights SOTA on Terminal Bench 3.0 and Agents' Last Exam among open models.
- Roughly 50% coding improvement over GLM-5.2 on Z.ai's internal benchmark.
- Leads open-weights models on CyberGym vulnerability discovery and exploitation tasks.
- 1-million-token context window for long codebases or document-heavy agent pipelines.
- Reasoning always active; three effort levels (low, high, max) with max as default.
Watch out for
- Reasoning cannot be disabled, which means higher token costs on tasks that do not need it.
- Output is priced at $4.80/M tokens — budget accordingly for long agentic runs.
- Emergent cyber capability is flagged by the maker itself as faster-than-expected; consider that before deploying in unsupervised contexts.
Prompts to try
Debug a multi-file refactor
I have a Python service split across three files: main.py, db.py, and models.py. After refactoring the ORM layer to use async SQLAlchemy, I get a MissingGreenlet error at runtime. Walk through the likely causes in each file, explain the async session lifecycle issue, and show the corrected pattern for dependency injection in FastAPI.
Plan a long-horizon agent
Design a step-by-step execution plan for an autonomous agent that must: clone a GitHub repository, identify all API endpoints, write integration tests for each one, run them in a sandboxed environment, and open a pull request with a test coverage report. List the tools the agent needs, the failure modes at each step, and how it should recover.
Trace a security vulnerability
Given this Python Flask route that accepts a filename parameter and reads a file from disk, identify the vulnerability class, explain how an attacker would exploit it, and rewrite the function with the minimum changes needed to close the hole without breaking the existing API contract.
@app.route('/download')
def download():
name = request.args.get('file')
return open(f'/var/data/{name}').read()
Compare with
- GLM 5.3 FlashXAPI-only model1,048,576 token context · $0.37/M input, $1.25/M output
- GLM 5V TurboAPI-only model202,752 token context · $1.20/M input, $4.00/M output
- GLM 5 TurboAPI-only model202,752 token context · $1.20/M input, $4.00/M output
- GLM 5.3 PrimeAPI-only model1,000,000 token context · $2.80/M input, $8.80/M output
More text models from z-ai
Questions people ask
- Is GLM-5.3 open weights and can I run it locally?
- Yes, the weights are published on Hugging Face under zai-org/GLM-5.3, so you can download and run the model on your own hardware.
- What does GLM-5.3 cost and what is the context window?
- Via Z.ai's API it is $0.08 per million input tokens and $4.80 per million output tokens, with a 1,048,576-token context window.
- How does GLM-5.3 differ from GLM-5.2?
- Both share the same base model; GLM-5.3's gains are entirely post-training, with the largest improvements in complex coding tasks and offensive security benchmarks where it more than doubles GLM-5.2's scores.
Captain's verdictThe open-weights model to reach for on hard coding and agent tasks, with a cyber capability that warrants careful deployment.
Model card on Hugging Face
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
Made this model? Show this badge on your page.
Paste it into your Hugging Face card or Civitai description.
Also ranked: Gold · Hottest this month · #25 Hottest AI model · #10 Hottest Text / LLM model · #10 Hottest Text / LLM text model
Not this creator?
Get your own badge
GLM 5.3 Popularity Trend
Heat score since 18 Aug 2026
Heat score
9,226°
This week
▲
61.9%
9,162° then, 9,226° now
This month
▲
1,291%
8,880° then, 9,226° now
Past year
n/a
Wasn't released/tracked
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+1,852°
OpenRouter tokens
14,906,750,510,237 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 1,490,675 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
+374°
Adoption rate
About 98,104 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 9,226°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.1,490,675 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+1,852°8,852°running total
- 3Add the adoption rate.About 98,104 downloads a day right now, measured between two readings, at a quarter of the weight.+374°9,226°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.9,226°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
GLM 5.3 is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
Browse by Model Family
Text / LLM Models
434
Anima Models
528
Chroma Models
19
Fibo Models
2
Flux Models
1,496
Flux 2 / Klein Models
1,239
Flux 3 Models
3
Moss Models
3
Iris Models
1
MiniMax H3 Models
364
Hunyuan Models
204
Ideogram Models
20
Krea2 Models
717
Qwen Models
20
Qwen2 Models
47
Ltx2 Models
153