Coding · Head-to-head

GLM-5.2 vs Devin

GLM-5.2 (freemium, AI Score 8.5/10) vs Devin (paid, AI Score 7.8/10). Side-by-side pricing, features, pros and cons, and which to pick.

The verdict

Pick GLM-5.2 if…
  • you need a genuinely free option
  • budget is the constraint
  • overall capability matters more than price (AI Score 8.5 vs 7.8)
  • your primary use case is engineering teams building coding agents who need frontier-class capability under a license they fully control — self-hosted, fine-tuned, or air-gapped — rather than rented from a closed api.
Try GLM-5.2 →
Pick Devin if…
  • your primary use case is platform and infrastructure teams running repetitive multi-service work — framework upgrades, dependency migrations, test backfill — who want tasks delegated from jira or slack and returned as reviewable prs.
Try Devin →

Side-by-side specs

Spec GLM-5.2 Devin
Category Coding Coding
Pricing model freemium paid
Headline pricing Free chat + MIT open weights; paid API and GLM Coding Plan — check site for current rates Core pay-as-you-go from $20; Team $500/mo; Enterprise custom — check site for current ACU rates
Free tier Yes — free chat access plus MIT-licensed weights you can download and run at no license cost. Hosted API and Coding Plan rates are not stated here because they were not verified against z.ai on the date of this update; check the site directly before budgeting.
AI Score 8.5/10 7.8/10
Best for Engineering teams building coding agents who need frontier-class capability under a license they fully control — self-hosted, fine-tuned, or air-gapped — rather than rented from a closed API. Platform and infrastructure teams running repetitive multi-service work — framework upgrades, dependency migrations, test backfill — who want tasks delegated from Jira or Slack and returned as reviewable PRs.
Editor's pick
Use cases development agents development agents
Date added 2026-06-27 2026-05-01

Pros and cons

GLM-5.2 logo

GLM-5.2

Coding · freemium

Pros

  • MIT license at 744B parameters — commercial use, fine-tuning and redistribution with no revocable API terms or MAU clause
  • 1M-token context handles whole repositories and long agent histories in one pass
  • Tuned for long-horizon, multi-turn tool use rather than one-shot completion, which is the workload coding agents actually run
  • Self-hosting is a genuine option for teams with data-residency or air-gap requirements, not a theoretical one
  • Materially cheaper than closed frontier tiers like GPT-5.6 Sol at $5/$30 per million tokens

Cons

  • ×744B parameters means serious multi-GPU hardware to self-host, so most teams end up on the hosted API anyway — back on someone else's terms
  • ×The launch claim of beating GPT-5.5 on long-horizon coding was vendor- and press-reported and still has no independent replication; GPT-5.6 Sol shipped in July and moved the comparison point
  • ×The cheap-frontier field caught up fast — Qwen 3.8 Max at $2/$6 and Kimi K3 at $3/$15 offer comparable context, so GLM-5.2's cost edge is no longer distinctive
  • ×Hosted API and Coding Plan prices are not published in an easily comparable form, and some enterprises face data-residency or procurement blocks on a China-based provider
Devin logo

Devin

Coding · paid

Pros

  • Usage-based entry tier removed the old $500/month floor, so a team can trial it for the price of a couple of tasks
  • Devin Wiki and Devin Search give grounded, citation-backed answers about large unfamiliar codebases
  • Parallel sessions and enterprise fan-out make repetitive migrations across many services genuinely tractable
  • Delegation from Slack, Linear, Jira and GitHub plus a public API fits existing workflows without an IDE switch
  • Full session replay of every shell command makes failures auditable rather than mysterious

Cons

  • ×Consumption billing is hard to forecast — failed attempts and wrong turns bill the same as successful ones
  • ×Competing async agents from Anthropic, OpenAI, Cursor and GitHub now ship in subscriptions costing a fraction as much
  • ×Still needs tightly scoped tasks; ambiguous or architecture-level work produces confident PRs that get rejected
  • ×No free tier, so evaluation always costs money
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Updated 2026-08-10. Spec data sourced from official product pages and tracked in our public directory at /tools.