Z.ai (GLM)

Z.ai (GLM) — GLM models with an unusually cheap flat-rate coding plan. Zhipu's GLM series, sold internationally as Z.ai, targets coding and agentic workloads directly and releases its flagship weights under MIT. Its most disruptive product is not the token price but the flat-rate coding subscription that undercuts per-token billing for anyone driving an agent hard all day. Widely mirrored by routers, which is how most Western teams reach it. Built by Zhipu AI. Founded 2019. Open source under MIT.

We track it in 1 comparison — LLM APIs — so every claim below is a cell in a table you can open and check rather than an impression. Across those rosters it sits against 17 other tools, and what follows is where it visibly separates from them.

Where it wins.

• $/M output — $2.2 /M tok, 2nd of 8.
• $/M input — $0.6 /M tok, 2nd of 8.

Each of those is ranked against the whole roster on its category page, not against a hand-picked subset, so a first place here means first of everything we list.

Where it gives ground.

• Zero retention — No. Amazon Bedrock records Yes.
• Context window — 200,000 tokens, 9th of 10. Anthropic records 1,000,000 tokens.

None of these disqualify it on their own. They are the fields to check against your own requirements before you commit, because they are the ones where a competitor genuinely does better.

Provenance. 20 of 27 tracked fields carry a value for Z.ai (GLM), and 1 of those cite a document you can open. Last verified 2026-01-15. Every figure keeps its own provenance — measured by us, claimed by the vendor, inferred, or community-reported — and we would rather print a dash than a guess.

Its nearest neighbour in our data is Qwen. Z.ai (GLM) is ahead on Open weights (Yes against Partial) and $/M output ($2.2 /M tok against $6 /M tok). Qwen takes MCP support (Partial against No) and AU region (Partial against No). That pattern repeats across the rest of the roster — see Z.ai (GLM) alternatives for the other rivals, each compared the same way.

Company

Zhipu AI

Founded

2019

Licence

MIT (open source)

Fields we track

20 of 27

Last verified

2026-01-15

The flat-rate coding plan is the genuinely disruptive product here — it makes running an agent hard all day a fixed cost rather than a variable one, which no per-token vendor can match. Model quality is a step below the frontier on hard reasoning; for routine coding turns most users do not notice.

$/M output

$2.2 /M tok2nd of 8

Inferredverified 2026-01-15

$/M input

$0.6 /M tok2nd of 8

Inferredverified 2026-01-15

Zero retention

○No10th of 12

Inferredverified 2026-01-15

Context window

200,000 tokens9th of 10

Inferredverified 2026-01-15

Tool use

●Yes1st of 18

Community-reportedverified 2026-01-15

Pinnable versions

●Yes1st of 18

Inferredverified 2026-01-15

OpenAI-compat API

●Yes1st of 18

Inferredverified 2026-01-15

MCP support

○No6th of 18

Inferredverified 2026-01-15

Z.ai (GLM) is the right call in these situations, each one drawn from a field we actually record:

• Driving a coding agent all day on a predictable monthly cost.
• Cheap high-volume code completion and refactoring.
• Self-hosting an MIT-licensed coding model.
• $/M output is your binding constraint. Z.ai (GLM) records $2.2 /M tok, 2nd of 8 in the LLM APIs roster. We define that field as pay-as-you-go list price per million output tokens for the named flagship model, including reasoning or thinking tokens where those are billed as output.
• $/M input is your binding constraint. Z.ai (GLM) records $0.6 /M tok, 2nd of 8 in the LLM APIs roster. We define that field as pay-as-you-go list price per million input tokens for the named flagship model, uncached, at the standard context tier.

Reach for something else when any of the following is a requirement rather than a nice-to-have:

• Zero retention. Z.ai (GLM) records No on the LLM APIs table. Amazon Bedrock records Yes on the same field. If that is a hard requirement rather than a preference, start elsewhere.
• Context window. Z.ai (GLM) records 200,000 tokens, 9th of 10 in the LLM APIs roster. Anthropic records 1,000,000 tokens on the same field. If that is a hard requirement rather than a preference, start elsewhere.

We publish this block because a comparison that only lists what a tool is good at is marketing. Every figure above sits on the same page as its source, and the field definitions are on the category tables if you want to check how we measured them.

What we don't know. 7 of the 27 fields we track for Z.ai (GLM) are still blank: Max output, TTFT p50, Output tok/s, $/M cache read, Batch discount, Cache TTL and Notice period. Those render as dashes rather than as zeroes or assumptions, because an empty cell and a bad cell are not the same thing and only one of them is honest. If you know any of these figures and can point at a document, tell us.