GLM 5.3 Flash
9421°
Run this model
Offers / Ads
Z.ai | 1,048,576 token context | $0.02/M input, $0.72/M output. GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while reducing compute overhead. Yes, the stealth model Ox Alpha was revealed to be ZAI's new model, GLM-5.3 Flash.
Guides for this model
How to choose an AI model in October 2026: the closed source news, then the open weights worth downloading
Best local LLM by use case (October 2026): the right open model for every job
How much VRAM do I actually need for local AI?
Open weights vs open source vs API-only: what you are actually allowed to do
How to Install Ollama and Run a Local AI (Windows, Mac, Linux)
Captain's notes
Notes from 2026-10-13
GLM-5.3-Flash is a native multimodal model from Z.ai with 320B total parameters but only 18B active, making it punch well above its running cost. A hybrid sparse-and-linear attention architecture lets it handle a full million-token context without the usual compute penalty. It suits developers who need serious coding and agent capability at a price closer to a lightweight model than a frontier one.
Known for
- One million token context with reduced serving cost via hybrid attention
- 18B active parameters from a 320B total — efficient at inference time
- Strong coding and long-horizon agent task performance
- Native multimodal: text, vision and video in one model
- Adjustable reasoning budget via a reasoning_effort parameter
Watch out for
- 320B total parameters means local deployment demands serious hardware despite the low active count.
- Output is priced at $0.90/M tokens — cheap by frontier standards, but watch agentic loops that generate heavily.
- The maker has not published a specific open-source licence; check the Hugging Face repository before commercial use.
Prompts to try
Refactor a messy codebase
Here is a Python module that handles user authentication. It works but it is hard to read and has no error handling. Refactor it for clarity, add typed function signatures, and raise specific exceptions rather than returning None on failure. Explain each change you make and why.
Plan a multi-step agent
Design a step-by-step agent plan for automatically triaging a GitHub repository's open issues: categorise each by type (bug, feature, question), assign a priority score based on comment count and age, and draft a one-sentence response template for each category. List the tools the agent would need and flag any steps where human review is essential.
Reason over a long document
I am going to paste a lengthy contract below. Read the entire document, then: identify every clause that limits liability, note any that conflict with each other, and summarise the net liability position for each party in plain language. Flag any clause that a non-lawyer might miss but that carries significant financial risk.
Compare with
- GLM 5.3 FlashXAPI-only model1,048,576 token context · $0.37/M input, $1.25/M output
- GLM Flash LatestAPI-only model1,048,576 token context · $0.02/M input, $0.72/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
More text models from z-ai
Questions people ask
- Can I run GLM-5.3-Flash locally?
- Yes, the weights are open and available on Hugging Face at zai-org/GLM-5.3-Flash. It supports SGLang, vLLM, Transformers, KTransformers and Unsloth, but the 320B total parameter count means you will need substantial hardware even though only 18B parameters are active at inference time.
- What does GLM-5.3-Flash cost and what is its context window?
- Via the Z.ai API it is priced at $0.03 per million input tokens and $0.90 per million output tokens, with a context window of 1,048,576 tokens.
- How does GLM-5.3-Flash differ from earlier GLM models?
- It is the first natively multimodal model in the GLM-5 series, built on a new base model with a hybrid sparse-and-linear attention architecture that earlier GLM releases did not use. Z.ai says it outperforms GLM-5.2 across benchmarks at one-tenth the price.
Captain's verdictA rare combination: frontier-class context and multimodal capability at a price that makes long agentic runs financially survivable.
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: #2 Hottest Text / LLM model · #2 Hottest Text / LLM text model
Not this creator?
Get your own badge
GLM 5.3 Flash Popularity Trend
Heat score since 26 Aug 2026
Heat score
9,421°
This week
▲
26.6%
9,390° then, 9,421° now
This month
▲
286%
9,242° then, 9,421° 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.
+2,026°
OpenRouter tokens
56,734,070,001,964 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, 5,673,407 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
+395°
Adoption rate
About 183,507 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 9,421°, 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.5,673,407 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.+2,026°9,026°running total
- 3Add the adoption rate.About 183,507 downloads a day right now, measured between two readings, at a quarter of the weight.+395°9,421°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.9,421°
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 Flash 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