GLM 5.3 FlashX
8281°
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Z.ai | 1,048,576 token context | $0.37/M input, $1.25/M output. GLM-5.3-FlashX is the high-speed variant of Z.ai's GLM-5.3-Flash, a native multimodal model delivering inference speeds of up to 200 tokens/s. Built on the same hybrid sparse and linear attention architecture (320B total parameters, 18B active), it is suited for efficient coding, visual understanding, and long-horizon agent tasks with a 1M-token context window.
Guides for this model
The 10 cheapest ChatGPT alternatives in 2026: free local models, free tiers and flat-rate plans
Open weights vs open source vs API-only: what you are actually allowed to do
Captain's notes
Notes from 2026-10-13
GLM-5.3-FlashX is Z.ai's speed-optimised variant of GLM-5.3-Flash, a native multimodal model built on a hybrid sparse and linear attention architecture with 320B total parameters and 18B active. It reaches up to 200 tokens per second and carries a 1M-token context window, making it one of the faster options at this price point for coding, visual understanding, and agent workflows.
Known for
- Up to 200 tokens/s inference speed, fast for a model of this class.
- 1M-token context window — genuine long-document and multi-turn agent use.
- Native multimodal: handles vision and video alongside text.
- Efficient MoE design: 320B total parameters, only 18B active per forward pass.
- Priced well below most frontier multimodal models.
Watch out for
- Weights are not public; you run this via Z.ai's API only, no local deployment.
- Output costs $1.25/M tokens — agent loops with long outputs add up quickly.
Prompts to try
Refactor a messy function
Here is a Python function that works but is hard to read and slow on large lists. Refactor it for clarity and performance, explain each change you make, and flag any edge cases the original code silently ignores.
def find_dupes(lst):
dupes = []
for i in range(len(lst)):
for j in range(len(lst)):
if i != j and lst[i] == lst[j] and lst[i] not in dupes:
dupes.append(lst[i])
return dupes
Plan a long-horizon agent
You are planning an autonomous agent that monitors a company's GitHub repository, triages new issues by severity, drafts a reply to the reporter, and opens a Jira ticket if the issue is critical. Outline the full task graph, identify where the agent needs human approval, and flag the failure modes most likely to cause silent errors.
Summarise a long document
I am pasting a lengthy technical specification below. Read the entire document, then produce: a three-sentence executive summary, a bulleted list of the five most important requirements, and a list of any ambiguities or contradictions you found. Do not pad the output.
[paste your document here]
Compare with
- GLM 5.3 FlashOpen-weights model1,048,576 token context · $0.02/M input, $0.72/M output
- GLM 5.3Open-weights model1,048,576 token context · $0.08/M input, $4.80/M output
- GLM 5.2Open-weights model1,048,576 token context · $0.04/M input, $9.00/M output
- GLM 4.6Open-weights model204,800 token context · $0.50/M input, $2.00/M output
More text models from z-ai
Questions people ask
- Can I run GLM-5.3-FlashX locally?
- No. Z.ai has not released the weights, so the model is API-only and cannot be run on your own hardware.
- What does it cost and how large is the context?
- Pricing is $0.37 per million input tokens and $1.25 per million output tokens, with a 1,048,576-token context window.
- How does FlashX differ from GLM-5.3-Flash?
- FlashX is the high-speed variant of GLM-5.3-Flash, targeting up to 200 tokens per second; both share the same underlying architecture and parameter count.
Captain's verdictA genuinely fast, cheap multimodal model with a real 1M context — worth a look for agent pipelines where speed and cost matter more than frontier accuracy.
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
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Also ranked: #369 Hottest AI model · #65 Hottest Text / LLM model · #65 Hottest Text / LLM text model
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GLM 5.3 FlashX Popularity Trend
Heat score since 18 Sep 2026
Heat score
8,281°
This week
▲
25.2%
8,251° then, 8,281° now
This month
n/a
Wasn't released/tracked
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.
+1,281°
OpenRouter tokens
186,521,041,354 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, 18,652 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 8,281°, 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.18,652 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.+1,281°8,281°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.8,281°
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 FlashX 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.
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