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DeepSeek vs GLM (Z.ai)
A plain-English comparison to help you choose between them.
Two open Chinese frontier families, sold as different products. Pick DeepSeek when you want the broadest cheap capability: a free chat assistant that holds up on reasoning and coding, the lowest-cost frontier-grade API for builders, and open weights for fully private deployment. Pick GLM when the job is specifically coding inside a client you already run: its Coding Plan feeds open-weight models into tools such as Claude Code on a low-cost flat subscription, with a 1M-token context for large codebases.
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Side by side
- Summary
DeepSeek is the price disruptor at the frontier: a free, capable chat assistant, an API priced far below the western frontier labs, and open-weight model releases that anyone can download and run. Successive model generations have kept it genuinely competitive on reasoning and coding, not merely cheap.
The open weights are the strategic fact. Organisations that cannot send data to a Chinese-hosted service can still use the models by self-hosting them on their own infrastructure, which splits the privacy question from the capability question.
MoreLess
For cost-sensitive builders and self-hosters it is a serious default; for regulated data on the hosted service, the jurisdiction question is the one to answer first.
- Best for
- A free, genuinely capable chat assistant
- The lowest-cost frontier-grade API for builders
- Open weights you can download and self-host
- Reasoning and coding strength per pound spent
- Privacy-critical use via self-hosted deployment
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Runs privately (self-hostable)
- Data
- The hosted service processes data under Chinese jurisdiction, which rules it out for sensitive work; self-hosting the open weights removes that concern entirely.
- 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.
Pricing
- DeepSeek
Free·Custom
Prices as of August 2026.
- DeepSeek Chat
- Free
- Pay as you go
- Price on applicationusage billed at API rates
- 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
By area
Where each one pulls ahead, area by area.
| Area | DeepSeek | GLM (Z.ai) |
|---|---|---|
| By job | ||
| Software development | DeepSeek runs the reasoning-heavy tasks where model cost dominates, and can be benchmarked against costlier models before anything scales | GLM is pointed at Claude Code for routine implementation work and needs no wiring built around it first |
| By task | ||
| Coding & software development | DeepSeek handles long-context codebase tasks affordably and lets agentic coding flows be prototyped without burning budget | GLM moves between the subscription and the pay-per-token API as usage shifts, so the commercial shape follows the work instead of fixing it |
| Private, local & self-hosted | DeepSeek runs distilled variants on modest hardware and lets open checkpoints be fine-tuned for domain tasks | GLM is for the case where your rules about where source code may travel exclude hosted APIs, GLM's own included, and you want its coding capability anyway |
Common questions
They sound similar, so what actually differs?
The product shape. DeepSeek is a generalist offer: chat app, volume API and open weights, with coding as one strength among several. GLM's Coding Plan is unusually clear about its purpose: it exists to power the coding client you already use, changed with a configuration edit rather than a migration. Choose by whether you are buying an assistant or restructuring a coding line item.
Which self-hosts better?
Both publish open weights, and both make self-hosting the answer to the jurisdiction question. DeepSeek's frontier-scale weights need serious hardware, with laptops running only the distilled variants. GLM's published weights, from the generation before the one its plan now serves, carry a 1M-token context and are likewise a serious deployment rather than a laptop experiment. Either way, verify licence terms per release and treat production serving as real infrastructure work.
Is there a shared caveat to both?
Two. DeepSeek's service is served from China; GLM's terms point to Singapore under a Chinese-founded developer. Organisations with data-residency rules should either self-host or keep regulated material out; the weights split privacy from capability in both cases. And on ambiguous specifications and sustained agentic work, the closed frontier models still win, so the honest pattern for either is a daily driver with a retained frontier seat for the hardest problems.
Which suits a developer who wants a local model on one laptop?
Neither at full scale, and this guide says so plainly for both. DeepSeek's frontier-scale weights need serious hardware, with laptops running only the distilled variants. A large-context coding model is likewise a serious deployment rather than a laptop experiment, which is why Ollama is named for the quick local prototype instead.
How does each behave when coding volume rises sharply?
GLM moves between its flat subscription and a pay-per-token API as usage shifts, so the commercial shape follows the work instead of fixing it, and the flat lane only pays off once volume is sustained. DeepSeek meters throughout, which is why it is the one to prototype agentic flows on without burning budget, then benchmark against costlier models before anything scales.
Should a first coding assistant be DeepSeek or GLM?
Neither, on this guide's reading. GLM's value assumes an existing client and enough sustained volume for flat pricing to matter, so it is not the lane for a first assistant. GitHub Copilot is named for the first mile, with a free tier and in-editor integrations, and a generalist who codes only occasionally is pointed at Claude or ChatGPT instead.
Do the two jurisdiction answers require the same response?
They differ in where they point, though the remedy is the same. DeepSeek's hosted service processes data under Chinese jurisdiction. GLM's hosted terms point to Singapore while its developer is Chinese-founded. Self-hosting removes the question in both cases, because the model then runs on infrastructure you control and nothing leaves it, which is why the weights are what privacy-critical buyers actually evaluate.
What should be checked before either model enters a shipped product?
The licence, per release rather than once. This guide's standing advice for DeepSeek is to verify licence terms for each model release before it enters a product, and the same caution applies to any open-weight line whose terms can move between versions. Treat production serving as real infrastructure work on top, not as a deployment detail.
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Tool facts last checked August 2026