--- title: "GLM Models: Pricing, Benchmarks and the MIT-Licensed Tier" description: "A senior lab's read on Z.ai's GLM family: GLM-5.3 at $1.40 / $4.40, GLM-5.3-Flash under MIT at 320B, and the flat-rate Coding Plan." source: "https://www.kensink.com/models/glm/" canonical: "https://www.kensink.com/models/glm/" --- ★ GLM LLM Models 8-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 weights Coding agents Eval pipelines [Start a conversation →](https://www.kensink.com/contact) [All llm models →](https://www.kensink.com/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. 01 ### Scope and fit We decide where GLM earns its place in your system, and where a simpler tool wins. No resume-driven architecture. 02 ### 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. 03 ### Eval before launch An eval suite proves the build behaves before it reaches a user. We measure, then ship. 04 ### 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 0 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. [ Flagship Z.ai 25 Aug 2026 ### GLM-5.3 zai-org/GLM-5.3 Coding flagship, open weights API input $1.40 / 1M API output $4.40 / 1M - 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 ](https://www.kensink.com/models/glm/glm-5-3)[ Flagship Z.ai 25 Aug 2026 ### GLM-5.3-Flash zai-org/GLM-5.3-Flash Multimodal, MIT licensed Licence MIT Params 320B / 18B active - 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 ](https://www.kensink.com/models/glm/glm-5-3-flash) Previous Z.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 Current Z.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](https://www.kensink.com/k-framework) 01 ### 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. 02 ### 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. 03 ### 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. Share[](https://twitter.com/intent/tweet?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fglm%2F&text=GLM%20%C2%B7%20LLM%20Models)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fglm%2F) [View .md](https://www.kensink.com/models/glm.md) \[RELATED\] ## Worth a look next. [ LLM Models Qwen Read more ](https://www.kensink.com/models/qwen)[ LLM Models DeepSeek Read more ](https://www.kensink.com/models/deepseek)[ LLM Models Kimi Read more ](https://www.kensink.com/models/kimi) APPLIED K-FRAMEWORK ## Bring the problem. We’ll bring the build. Senior engineers, eval suite at handoff, full source ownership. Sprint, program, or ongoing. We shape the engagement to the work. [Start a conversation →](https://www.kensink.com/contact) [Read the K-Framework](https://www.kensink.com/k-framework)