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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: published 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.
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Side by side
- Summary
GLM is Z.ai's coding-first model family, sold both as a change to what powers the client you already use and through ZCode, Z.ai's own first-party client on the same Coding Plan quota. The GLM Coding Plan runs open-weight models inside clients such as Claude Code: you keep the client, the workflow and the keybindings, and change what feeds them with a configuration edit rather than a migration. For a team already settled into its tooling, that is the cheapest structural change on offer.
The honest working pattern is a daily driver plus a retained frontier seat. GLM carries the routine coding volume, while ambiguous specs and sustained multi-agent work still go to the closed frontier models, which remain ahead on exactly that kind of task. No tool is best for everyone; this one is unusually clear about which half of the work it wants.
- Best for
- Flat-rate coding subscription
- MIT-licensed GLM-5.2 open weights you can self-host
- A 1M-token context for large-codebase work
- Pay-per-token API access alongside the subscription
- A coding-first family, not a general-purpose assistant
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Runs privately (self-hostable)
- Data
- GLM's hosted terms point to Singapore while its developer is Chinese-founded, 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. It exists for the organisations the cloud assistants cannot serve.
That deployment flexibility, rather than peak suggestion quality, is the product: regulated industries, defence-adjacent teams and strict-sovereignty environments get modern AI coding inside their own walls.
MoreLess
It is enterprise-only, with no individual tier, and current suggestion quality deserves a direct test as part of any evaluation.
- 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
- Regulated teams the cloud assistants cannot serve
- Sovereignty requirements with modern assistance inside
- Cost
- Paid only
- 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.
Pricing
- GLM (Z.ai)
$12.60·$56·$117.60$79.20/user·$169.20/user
Prices as of August 2026.
- Lite
- $12.60per monthbilled annually; $18 per month if billed monthly
- Pro
- $56per monthbilled annually; $80 per month if billed monthly
- Max
- $117.60per monthbilled annually; $168 per month if billed monthly
- Standard Seat
- $79.20per user, per monthbilled annually; $88 per user per month if billed monthly
- Premium Seat
- $169.20per user, per monthbilled annually; $188 per user per month if billed monthly
- Tabnine
$39/user·$59/user
Prices as of August 2026.
- Tabnine Code Assistant
- $39per user, per monthbilled annually; usage billed at API rates
- Tabnine Agentic Platform
- $59per user, per monthbilled annually; usage billed at API rates
By area
Where each one pulls ahead, area by area.
| Area | GLM (Z.ai) | Tabnine |
|---|---|---|
| By job | ||
| Software development | GLM (Z.ai) — when a coding client is already in daily use and the flat plan should carry its routine volume | Tabnine is for the environment that is locked down enough that mainstream assistants are off the table, and rolls out without new data-processing agreements |
| By task | ||
| Coding & software development | GLM (Z.ai) — when MIT-licensed weights and a 1M-token context are the reach a real codebase asks for | Tabnine is for the case where policy or regulation rules out cloud coding assistants and you still want one, providing completions and chat inside air-gapped environments |
| Private, local & self-hosted | GLM's MIT licence converts a jurisdiction question into an infrastructure decision, which is a problem an organisation can solve with machines rather than with paperwork | Tabnine sells deployment flexibility rather than peak suggestion quality, and will train custom models on the private codebase itself |
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 terms point to Singapore while its developer is Chinese-founded, 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 low flat monthly coding plan, 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 August 2026