GLM 4.6
6486°
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Z.ai | 204,800 token context | $0.50/M input, $2.00/M output. Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks. Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages. Advanced reasoning: GLM-4.6 shows a clear improvement in…Z.ai | 204,800 token context | $0.50/M input, $2.00/M output. Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks. Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages. Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability. More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks. Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.
Guides for this model
Ollama vs LM Studio (2026): which local AI app to use, and which top models each one runs
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?
What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)
Captain's notes
Notes from 2026-10-13
GLM-4.6 is an open-weights model from Z.ai with a 200,000-token context window, built to handle coding, reasoning, and agentic workflows. It steps up from GLM-4.5 with stronger tool use during inference and better real-world performance in coding environments like Cline and Roo Code. At $0.50 per million input tokens it sits at a reasonable price point for a model claiming to compete with DeepSeek-V3.1-Terminus and Claude Sonnet 4.
Known for
- 200K context window suited to long agentic tasks
- Strong real-world coding in front-end and AI coding tools
- Tool use supported during inference, not just at call boundaries
- Improved search-based agent performance within agent frameworks
- Writing and roleplay tuned toward natural, readable output
Watch out for
- Open weights, but check the licence terms before any commercial or derivative use.
- Output tokens cost four times the input rate; long agentic loops can add up fast.
- No published model size; plan your VRAM before attempting local deployment.
Prompts to try
Front-end page from scratch
Build a single-page personal portfolio site in plain HTML and CSS. Include a sticky nav bar, a hero section with a short bio, a three-column project grid with hover effects, and a contact form. The design should look polished and modern without any external frameworks or libraries.
Multi-step reasoning plan
A company has three warehouses and five delivery routes. Each route can only be served by one warehouse. Warehouse A can handle at most two routes, B at most two, and C at most one. Routes 1 and 3 must be served by the same warehouse. Route 5 cannot be served by Warehouse C. List all valid assignments and explain your reasoning step by step.
Tool-use agent design
Design a minimal agent loop in Python that takes a user question, decides whether to call a web search tool or answer directly, calls the tool if needed, then synthesises a final answer. Show the tool schema, the decision logic, and a worked example with a question that requires a search call.
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
- Can I run GLM-4.6 locally?
- Yes, the weights are open and available on Hugging Face at zai-org/GLM-4.6, so you can self-host or run it locally, subject to the licence terms Z.ai publishes with the repository.
- What does GLM-4.6 cost and how long is its context?
- Via the Z.ai API it costs $0.50 per million input tokens and $2.00 per million output tokens, with a 200,000-token context window.
- How does GLM-4.6 differ from GLM-4.5?
- The main changes are a doubled context window (128K to 200K), higher coding benchmark scores with better front-end output, tool use during inference, stronger agent and search performance, and writing that Z.ai says aligns more naturally with human preferences.
Captain's verdictA credible open-weights option for coding agents and long-context work, priced sensibly enough to take seriously alongside the closed-source competition.
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
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Also ranked: #78 Hottest Text / LLM text model
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How this score adds up
This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.
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.
GLM 4.6 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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