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GLM (Z.ai) vs Tabnine
A plain-English comparison to help you choose between them.
Two privacy stories that are not the same story. Pick GLM when your route to private coding AI is owning the model: MIT-licensed open weights you can self-host on your own machines, a 1M-token context for large codebases, and a flat coding subscription where the hosted route is acceptable. Pick Tabnine when the organisation needs privacy as a procured product: fully air-gapped or on-premises deployment, zero code retention, an audit-ready compliance posture and custom models trained on your private codebase. Engineering-led sovereignty favours GLM; policy-led sovereignty favours Tabnine.
Side by side
- Summary
GLM is Z.ai's coding-first model family, sold less as a new tool than as a change to what powers the one you already have.
- Best for
- Flat-rate coding subscription from around $18 a month
- MIT-licensed GLM-5.2 open weights you can self-host
- A 1M-token context for large-codebase work
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Runs privately (self-hostable)
- Data
- GLM's hosted API is served from China, a jurisdiction point to weigh before routing real code or prompts through it. Self-hosting the open weights removes that question entirely: the model runs on infrastructure you control, and nothing leaves it.
- Summary
Tabnine is the coding assistant for environments where code must not leave: fully air-gapped and on-premises deployment, zero code retention, a compliance posture built for audits, and custom models trained on your private codebase.
- Best for
- Fully air-gapped and on-premises AI coding
- Zero code retention with an audit-ready posture
- Custom models trained on your private codebase
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Hosted service
- Data
- The privacy architecture is the product: self-hosted and air-gapped options keep code entirely inside your perimeter, with licensed-data provenance documented.
Common questions
Both claim privacy, so what is the actual difference?
Who does the work and who carries the guarantee. Self-hosted GLM means nothing leaves machines you control, but standing up a serious coding model is your infrastructure project, and GLM's hosted API is served from China, so the privacy story only holds on the self-hosted route. Tabnine sells the guarantee managed: air-gapped deployment, zero retention and documentation an auditor can read.
Is the price gap as large as it looks?
GLM is the cheaper story wherever it fits: a flat coding plan from around $18 per month, pay-per-token API access, and free self-hosting where compute is the only cost. Tabnine is enterprise-only with no personal tier, bought through procurement and priced at the premium end of the assistant market. Constraint decides: if policy forces Tabnine's shape, the comparison is with not having AI at all.
What about suggestion quality?
Neither is the frontier, and both deserve honesty on that. GLM is a strong daily driver for routine coding volume, with ambiguous specifications and sustained agentic work still favouring the closed frontier models. Tabnine's product is deployment flexibility rather than peak suggestion quality, so test current output directly as part of any evaluation.
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Tool facts last checked July 2026