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GLM (Z.ai) vs Sourcegraph Cody
A plain-English comparison to help you choose between them.
A cost restructure and an estate map, solving unrelated problems under one category tag: GLM makes daily agent coding cheap, Cody makes a sprawling codebase navigable. Pick GLM when the client is chosen and the bill is the problem, a low-cost flat monthly plan feeding open-weight models into tools such as Claude Code, with a 1M-token context for large-codebase work. Pick Sourcegraph Cody when the organisation's problem is understanding, answers and impact analysis grounded in how code connects across thousands of repositories, bought through enterprise procurement because that is the only remaining door.
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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
Cody is Sourcegraph's enterprise coding AI, built on the code-intelligence platform that indexes very large, multi-repository codebases. Its context is the differentiator: answers, completions and edits grounded in how code actually connects across an organisation's repos, with cross-repo impact analysis the marquee capability.
It is now enterprise-only by deliberate strategy: the free and individual tiers were retired, with individuals directed to a separate spun-off agent product. Cody's audience is the organisation whose codebase is the problem.
MoreLess
For teams inside that shape, few tools see the whole estate the way it does.
- Best for
- Multi-repo and monorepo context at enterprise scale
- Cross-repo impact analysis before changes land
- Answers grounded in how the whole estate connects
- Code intelligence and AI in one platform
- Organisations whose codebase outgrew single-repo tools
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Hosted service
- Data
- Enterprise deployments keep code context inside your environment with admin controls fit for security review.
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
- Sourcegraph Cody
- Inside the Sourcegraph platform
By area
Where each one pulls ahead, area by area.
| Area | GLM (Z.ai) | Sourcegraph Cody |
|---|---|---|
| By job | ||
| Software development | GLM (Z.ai) — when the routine volume is the cost problem and a frontier seat stays reserved for the ambiguous work | Sourcegraph Cody is for the case where understanding the existing system is harder than writing the new code, answering from the whole multi-repo estate |
| By task | ||
| Coding & software development | GLM brings a general-purpose model family with a flat-rate coding subscription, and Z.ai's own ZCode client alongside it | Sourcegraph Cody analyses the blast radius across services before a refactor lands, which is the capability a multi-repo estate is actually short of |
Common questions
Does GLM's 1M context not cover the estate problem?
A long context reads a lot of code; it does not index an organisation. Cody's grounding is the Sourcegraph platform's code intelligence, cross-repo references, usage patterns and impact analysis across the whole estate, machinery a context window does not replicate however large. For a single big repository the context helps; for thousands of repositories, the platform is the point.
Who can actually buy each one?
Nearly disjoint audiences. GLM sells to anyone with a card and an existing coding client, individual developers included, reversible with a configuration edit. Cody retired its free and individual tiers by deliberate strategy: the buying motion is enterprise procurement, individuals were pointed at a separate product line, and evaluating Cody means evaluating the platform beneath it.
Could an enterprise sensibly run both?
Yes, on different lines: Cody as the estate's navigation and assistance layer under enterprise controls, GLM as a cost lever for heavy agent volume in the terminal clients its developers already drive. The frontier caveat spans both, since GLM concedes ambiguous, sustained multi-agent work to the closed frontier models, which neither of these purchases replaces.
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Tool facts last checked August 2026