- Type
- Open weightsMIT License
- Params
- 357B
- Context
- 205K
about 154K words of context
Our take
Written Aug 3, 2026GLM 4.6 is a large downloadable text model from Z.AI with a permissive MIT licence and a 204,800-token request limit. Its measured coding skill sits well above its general-chat level, though creative writing and web development are clear weak points.
Pick this for coding tasks where its measured score peaks, or for hard-prompt reasoning near that same level. Use it when you need a wide request limit with no multimodal requirement, or when a permissive licence matters for commercial redistribution. Skip it if you need image, audio or video input, if creative writing quality is critical, or if you want a clear price-to-speed winner among hosts.
The case for it
- Strongest measured skill is coding, with a 34-point lift over its general-chat score.
- Permissive MIT licence with no commercial restrictions.
- Wide request limit for a 357-billion-parameter downloadable model, at 204,800 tokens.
- Broad benchmark coverage across seven Arena categories.
The case against it
- Web development coding is a clear weak point, 120 points below its peak coding score.
- Creative writing lags all other measured domains, 22.9 points below its overall average.
- No disclosed active-parameter count, leaving efficiency unverified.
How good is it?
IntelligencePuzzles, maths, exam questions
Arena Text (overall)57th of 143 · 1425
CodingWriting and fixing code on its own
Arena Coding67th of 143 · 1459
AgenticPlanning, calling tools, staying on task
GLM 4.6 is not on Arena Agent (IPS), which is where the rating would come from, so there is no rating here. It is on SWE-bench Verified, in 13th of 39 with 68.2.
WritingWe do not rate this
Two boards come close and neither tests writing: Arena Creative Writing asks people which of two replies they prefer, and LiveBench Language tests whether a model understood a passage. So we show where GLM 4.6 placed and give it no mark out of five.
These tests check whether a model follows instructions — a precondition for all the work above, but not a measure of how well that work is done, which is why they get no rating.
Every published score for this model8 scoresEvery figure we hold, from 8 boards, with who ran it and a link to the source — including the boards no rating above is built on.
Can you run it yourself?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
GeForce RTX 4090 · 24 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Apple M1 Pro (16-core GPU) · 32 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Comfortable fit
Apple M3 Ultra (80-core GPU) · 512 GB
Room to spare. 150.3 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
Or rent it from someone else
Cheapest of 8 live listings. Picked at the widest standard context we hold, within one quantisation slice, so the numbers beside it are a price one host actually charges.
- per 1M tokens
- $0.50 in / $2.00 out
- Context served
- 205K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| Venice AIfp4 | $0.43 / $1.75 | 198K | 17 tok/s | No | No | Confirmed |
| DeepInfrafp4 | $0.50 / $2.00 | 203K | not measured | Unknown | Unknown | Unknown |
| DeepInfrafp4 | $0.50 / $2.00 | 203K | 23 tok/s | No | No | Confirmed |
| OpenRouter | $0.50 / $2.00 | 205K | not measured | Unknown | Unknown | Unknown |
| Z.AIfp4 | $0.60 / $2.20 | 203K | 36 tok/s | No | No | Confirmed |
| Novita AI | $0.55 / $2.20 | 205K | not measured | Unknown | Unknown | Unknown |
| Novita AIbf16 | $0.55 / $2.20 | 205K | 25 tok/s | No | No | Confirmed |
| AtlasCloudfp8 | $0.60 / $2.20 | 203K | 24 tok/s | No | Yesunknown period | Unknown |
DeepInfra
Novita AIAcross the 8 listings we hold: 5 say they do not train on prompts, 0 say they do and 3 do not say. 4 appear in the zero-retention registry we check; the rest are unknown to us rather than confirmed either way.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
- ✓
- Supported
- ✗
- Not supported
- Not published
- host gave no parameter list
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| Venice AIfp4 | ✓ | ✓ | ✓ |
| DeepInfrafp4 | |||
| DeepInfrafp4 | ✓ | ✓ | ✗ |
| OpenRouter | ✓ | ✓ | ✓ |
| Z.AIfp4 | ✓ | ✗ | ✗ |
| Novita AI | |||
| Novita AIbf16 | ✓ | ✓ | ✗ |
| AtlasCloudfp8 | ✓ | ✓ | ✓ |
Tool calling: 6 of 8 listings say yes, 2 publish no parameter list. JSON output: 5 of 8 listings say yes, 1 says no, 2 publish no parameter list. Strict schema: 3 of 8 listings say yes, 3 say no, 2 publish no parameter list.
Models people weigh against GLM 4.6
When we formed this view
Dates behind this page
Prices last checked 37h ago
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- 2 of 8 listings publish no parameter list, so what their API accepts is unknown to us.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 3 of 8 listings do not say whether they train on prompts.
Licence and identifiers
What the licence allowsMIT License, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
MIT License
Fully permissive: do anything with attribution. No patent grant, unlike Apache-2.0.
Identifiers
- Hugging Face
- zai-org/GLM-4.6
- Architecture
- Mixture of experts
- Modality record
- text->text
- Catalogue slug
- z-ai-glm-4-6