How much cheaper is GLM than the frontier?

GLM-5.3 is $1.40 input and $4.40 output per million against $10 and $50 for GPT-6 Astra and Claude Fable 5.1, so roughly seven times on input and eleven on output. Cached input is $0.26 against Astra's $1. On a coding agent the cache rate dominates, so the real gap is usually wider than the headline rates suggest.

Is GLM-5.3 as good as GPT-5.6 Sol?

On mainstream terminal coding, close to it: 88.2 against 88.8 on Terminal-Bench 2.1, which is inside the noise on most harnesses. On the harder Terminal-Bench 3.0 it drops to 28.3 against 33.7 for Claude Fable 5. It holds up on ordinary work and falls off at the top end, which is a routing rule rather than a verdict.

Which GLM should we use?

GLM-5.3-Flash for most work. It is MIT licensed at 320B, multimodal where the flagship is text-only, and four points behind on Terminal-Bench 2.1. Move up to the 753B flagship only when an eval on your own tasks shows the ceiling matters and legal has read its bespoke licence.

What is the GLM Coding Plan?

A flat monthly subscription at $18, $80 or $168, or roughly $12.60, $56 and $117.60 billed annually. Almost nobody else offers this shape. Unpredictable agent spend causes more internal friction than high agent spend, because one bad loop can cost more than a fortnight of normal use, and a flat rate removes that risk entirely.

Is the security capability a problem?

It is a governance decision rather than a technical one. GLM-5.3 tops CyberGym vulnerability discovery at 84.5 and trails on ExploitBench exploit development at 54.4, so it is much better at finding flaws than weaponising them. Self-hosting means no vendor classifier and no monitoring, so agree scope, permissions and logging before it lands in a client environment.

★ GLMLLM Models8-week engagement

GLM · Z.AI · OPEN WEIGHTS

Open weights aimed at coding and security.

Z.ai ship GLM as a coding-first family: a 753B flagship and a 321B multimodal tier under MIT, which is the most permissive licence anyone has attached to a model that size. The hosted API undercuts the frontier by roughly seven times, and there is a flat-rate coding subscription.

Open weightsCoding agentsEval pipelines

Start a conversation →All llm models →

Cycle

8 weeks · fixed price

Stack

GLM, hosted or self-hosted

Output

Production code + eval suite

Handoff

Full source ownership

[THE SHORT VERSION]

It does not beat the frontier, and it does not need to.

GLM-5.3 runs level with GPT-5.6 Sol on Terminal-Bench 2.1 at a seventh of the input price, and falls behind on the hardest tasks. That shape is a routing rule, not a verdict. The flat-rate Coding Plan solves a problem teams underrate, because unpredictable agent spend causes more friction internally than high agent spend does.

When it fits

Coding agents where per-token spend is hard to forecast
Commercial deployments that need a licence legal already knows
Defensive security research on weights you control
Volume coding work that does not need the frontier ceiling

When it does not

The hardest long-horizon reasoning, where the frontier still leads
Environments with no policy for a downloadable model that tops vulnerability-discovery benchmarks

[HOW WE BUILD IT]

How we build with GLM.

Scope and fit

We decide where GLM earns its place in your system, and where a simpler tool wins. No resume-driven architecture.

Build on a tested foundation

We integrate GLM against a foundation we trust: typed code, CI, and observability from the first commit. Boring infrastructure, modern surface.

Eval before launch

An eval suite proves the build behaves before it reaches a user. We measure, then ship.

Handoff with ownership

Your team gets the code, the tests, and a runbook. No lock-in to us or to a vendor framework.

[WHAT YOU GET]

What the engagement leaves behind.

Senior

Engineers who have shipped this before

100%

Source ownership at handoff

Eval-first

Tested before it ships

Framework lock-in

[MODELS + VENDORS]

Open weights aimed at coding and security.

Z.ai ships GLM as a coding-first family: a 753B flagship under its own licence, and a 321B multimodal tier under MIT, which is the most permissive licence anyone has attached to a model that size. The hosted API undercuts the frontier by roughly seven times, and there is a flat-rate coding subscription. We integrate both paths behind the same abstraction.

FlagshipZ.ai

25 Aug 2026

GLM-5.3

zai-org/GLM-5.3

Coding flagship, open weights

753B mixture-of-experts, 8 experts per token, shipped in FP8
Z.ai claim a 50% coding improvement over GLM-5.2 and state of the art on CyberGym
Roughly a seventh of GPT-6 Astra on input, with a 1M context

Read the technical brief

FlagshipZ.ai

25 Aug 2026

GLM-5.3-Flash

zai-org/GLM-5.3-Flash

Multimodal, MIT licensed

MIT at 320B. The most permissive licence attached to a model this size
18B active per token, multimodal, with reasoning_effort at low, high and max
84.3 on Terminal-Bench 2.1 without the flagship's licence terms

Read the technical brief

PreviousZ.ai

2026

GLM-5.2

zai-org/GLM-5.2

Previous flagship

Likes

5.1k on Hugging Face

The checkpoint GLM-5.3 claims a 50% coding improvement over
Still widely deployed, and a reasonable pin if you have already qualified it
Superseded at the same API price, so there is no cost reason to stay

In the routing table · brief not yet published

CurrentZ.ai

2026

GLM Coding Plan

glm-coding-plan

Flat-rate subscription

Lite

$18 / month

Max

$168 / month

Three tiers: Lite $18, Pro $80, Max $168 a month
Annual billing drops those to roughly $12.60, $56 and $117.60
Predictable spend on coding agents, which per-token billing rarely gives you

In the routing table · brief not yet published

[METHODOLOGY · K-FRAMEWORK]

Integrated through the
K-Framework.

Every model we integrate runs through the same operating system. Three pillars, sixteen layers, one Compound Growth Loop. The methodology that keeps AI work from rotting after the first ship.

Read the K-Framework

Foundations

Direct API integration with the model. No LangChain, no orchestration vendor, no agent framework built on quicksand. Typed contracts, the same way we wire up Postgres.

Amplification

An eval suite built from your real tasks gates every prompt and model change. Quality is measured before it ships, not vibed in a demo.

Judgment

Governance, audit, and oversight wired in from day one. Who called what, with which prompt version, at what cost. Your auditors get answers, not screenshots.

[OBSERVABILITY]

Observability your team can read.

A model in production without observability is roulette. We instrument every integration so engineering and finance can see the same numbers, and so a regression at 3am surfaces before a customer opens a ticket.

Instrumented

Cost per call

Tokens in, tokens out, dollars spent. Sliced by feature, tenant, and route. Budgets enforced where it matters.

Instrumented

Latency p50 / p95 / p99

Real distributions, not averages. We know which routes are slow, and why.

Instrumented

Eval pass rates

The same eval suite that gates a release runs continuously in production. A regression on real traffic surfaces fast.

Instrumented

Prompt + completion logs

PII scrubbed at the proxy, shipped to your SIEM. Retention controls match your compliance window.

Dashboards your team owns, not ours. At handoff you get the queries, the alerts, and the runbook. We are not in the path to read your metrics.

[COMMON QUESTIONS]

Questions we get asked.

How much cheaper is GLM than the frontier?: GLM-5.3 is $1.40 input and $4.40 output per million against $10 and $50 for GPT-6 Astra and Claude Fable 5.1, so roughly seven times on input and eleven on output. Cached input is $0.26 against Astra's $1. On a coding agent the cache rate dominates, so the real gap is usually wider than the headline rates suggest.
Is GLM-5.3 as good as GPT-5.6 Sol?: On mainstream terminal coding, close to it: 88.2 against 88.8 on Terminal-Bench 2.1, which is inside the noise on most harnesses. On the harder Terminal-Bench 3.0 it drops to 28.3 against 33.7 for Claude Fable 5. It holds up on ordinary work and falls off at the top end, which is a routing rule rather than a verdict.
Which GLM should we use?: GLM-5.3-Flash for most work. It is MIT licensed at 320B, multimodal where the flagship is text-only, and four points behind on Terminal-Bench 2.1. Move up to the 753B flagship only when an eval on your own tasks shows the ceiling matters and legal has read its bespoke licence.
What is the GLM Coding Plan?: A flat monthly subscription at $18, $80 or $168, or roughly $12.60, $56 and $117.60 billed annually. Almost nobody else offers this shape. Unpredictable agent spend causes more internal friction than high agent spend, because one bad loop can cost more than a fortnight of normal use, and a flat rate removes that risk entirely.
Is the security capability a problem?: It is a governance decision rather than a technical one. GLM-5.3 tops CyberGym vulnerability discovery at 84.5 and trails on ExploitBench exploit development at 54.4, so it is much better at finding flaws than weaponising them. Self-hosting means no vendor classifier and no monitoring, so agree scope, permissions and logging before it lands in a client environment.