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Free AI Visibility AuditGLM 4.6 is a text-only large language model from Z.ai, the follow-on to GLM-4.5, built for long-context work at a low per-token cost.
01 / decision
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 232 models we track, not against an absolute standard.
Capability
55.4/100
#16 of 22 with published scores
Input / 1M tokens
$0.43
field median $0.37
Output / 1M tokens
$1.75
field median $1.99
Context
205K
field median 500K
Read the coverage before the score. That figure rests on 20% of the weighting: the rest of the categories have no published evidence for this model, and are not counted rather than counted as zero.
02 / overview
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
The job it was built for, and the job it is not for.
GLM 4.6 is a general-purpose text model, not a multimodal one: it reads and writes text, with no image, audio or video input. Z.ai positions it as an incremental upgrade over GLM-4.5, with the headline change being a larger context window for handling more complex inputs in a single pass. If your job needs vision or speech, this is the wrong family.
Where it sits in its line, and when a sibling is the better pick.
GLM 4.6 first appeared at the end of September 2025 as the current generation of Z.ai's GLM line, replacing GLM-4.5 as the default choice within that family. It is the right pick when you are already working with GLM models and want the longer context without changing providers, or when cost per token is the binding constraint. It is the wrong pick when you need multimodal input or very long generated outputs, since the output ceiling is modest.
The API, the apps it powers, and what its limits let you do.
GLM 4.6 is reached through Z.ai's API and through the aggregators that resell it, and it is text in, text out. The expanded context window means you can put a large document set, a long transcript or a sizeable codebase into one request, but the output limit keeps each response comparatively short, so long-form generation has to be chunked across calls.
What changes because this exists, or why it does not.
The practical change is that long-context work gets cheap. Feeding an entire contract, a quarter of support tickets or a full documentation set into a single prompt was previously something you rationed on cost, and GLM 4.6 makes it a routine call, which suits batch classification, extraction and retrieval-heavy pipelines more than it suits flagship chat.
Follows GLM 4.5V. Superseded by GLM 4.6V. See the whole line.
03 / evidence
Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
Published rows
1
of 19 benchmarks we track
Independently verified
1
0 from the provider
Weighting covered
20%
the rest is not measured, not zero
Agentic
0/3
Not measured
Coding
1/3
Verified
Reasoning
0/3
Not measured
Multimodal
0/3
Not measured
Knowledge
0/2
Not measured
Multilingual
0/2
Not measured
Instruction following
0/1
Not measured
Maths
0/2
Not measured
04 / ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
| Benchmark | Score | Best published | Weight | Source |
|---|---|---|---|---|
| SWE-bench VerifiedReal GitHub issues, resolved and tested. | 55.4% | MiniMax M2.575.8% · 20.4 behind | 40% | SWE-bench Verified · GLM 4.6 (T=1)Verified |
Benchmark data from SWE-bench. Every row links the published result it was read from.
05 / capability
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.
| Category | Score | Rank | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|
| Agentic | Not measured | Not ranked | 22% | 0 of 3 | Not measured |
| Coding | 55.4 | #16 of 2229th percentile | 20% | 1 of 3 | Verified |
| Reasoning | Not measured | Not ranked | 17% | 0 of 3 | Not measured |
| Multimodal | Not measured | Not ranked | 12% | 0 of 3 | Not measured |
| Knowledge | Not measured | Not ranked | 12% | 0 of 2 | Not measured |
| Multilingual | Not measured | Not ranked | 7% | 0 of 2 | Not measured |
| Instruction following | Not measured | Not ranked | 5% | 0 of 1 | Not measured |
| Maths | Not measured | Not ranked | 5% | 0 of 2 | Not measured |
Category weights are ours and they are a judgement: weighted for what a SaaS team buys a model to do. Agentic and coding lead because that is where the work is. Published here rather than hidden, and set out in full on the methodology page.
06 / cost
List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.
| Charge | Price | Unit | Read on | Source |
|---|---|---|---|---|
| Input | $0.43 | per 1M tokens | 2026-09-24 | Check |
| Output | $1.75 | per 1M tokens | 2026-09-24 | Check |
GLM 4.6 sits at the budget end of the market, cheap enough that large-context prompts stop being something you budget around. Against the frontier models from the larger US labs it costs a small fraction per call, which is the main reason teams pick it for high-volume, repetitive text work.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $17.35 |
| A busy support assistant | 200M tokens | 40M tokens | $156.00 |
| A document pipeline | 1000M tokens | 100M tokens | $605.00 |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
07 / specs
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 204,800 tokens |
|---|---|
| Maximum output | 16,384 tokens |
| Modalities | text |
| Released | 30/09/2025 |
| Status | Current |
| Catalogue identifier | z-ai/glm-4.6 |
08 / lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from Z.ai
09 / line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Came before
← GLM 4.5VCame after
GLM 4.6V →Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.
10 / notes
What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.
What it changes for you
Models like GLM 4.6 mean your content is being read and summarised at a price point where nobody thinks twice about ingesting your entire site in one go. That rewards source material that holds up when read whole and in context, consistent product naming, clear pricing pages, documentation that answers questions directly, rather than pages tuned for a snippet. If a builder wires a cheap long-context model into their answer layer, the brands that get cited are the ones whose full text is unambiguous.
Most buyers meet GLM 4.6 through the API rather than a consumer app, either directly from Z.ai or via a model router where it sits alongside Western models as a lower-cost option. It also turns up inside products whose builders chose it on price, so a buyer may be reading its output without ever seeing the name.
Nothing published here yet. Changes appear within hours of a provider announcing them.
11 / questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.