Coding · Head-to-head
GLM-5.2 vs Laguna XS.2
GLM-5.2 (freemium, AI Score 8.5/10) vs Laguna XS.2 (free, AI Score 8.2/10). Side-by-side pricing, features, pros and cons, and which to pick.
The verdict
Pick GLM-5.2 if…
- →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.
Pick Laguna XS.2 if…
- →budget is the constraint
- →your primary use case is developers and small teams who want a self-hosted coding model for agentic repo work on a single 36gb gpu or mac, with no per-token bill and no vendor able to retire the model on them.
Side-by-side specs
| Spec | GLM-5.2 | Laguna XS.2 |
|---|---|---|
| Category | Coding | Coding |
| Pricing model | freemium | free |
| Headline pricing | Free chat + MIT open weights; paid API and GLM Coding Plan — check site for current rates | Free open weights (Apache 2.0). Hosted API — check website for current pricing |
| 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. | The weights themselves are permanently free under Apache 2.0 — self-hosting costs only your hardware. Hosted API pricing is unverified; check the vendor site. |
| AI Score | 8.5/10 | 8.2/10 |
| 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. | Developers and small teams who want a self-hosted coding model for agentic repo work on a single 36GB GPU or Mac, with no per-token bill and no vendor able to retire the model on them. |
| Editor's pick | — | — |
| Use cases | development agents | development agents |
| Date added | 2026-06-27 | 2026-05-02 |
Pros and cons
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
Laguna XS.2
Coding · free
Pros
- ✓Apache 2.0 with no commercial or fine-tuning restrictions — the license cannot be revoked on weights you already have
- ✓Roughly 3B active parameters means fast, cheap inference relative to its benchmark tier
- ✓Fits on a single 36GB GPU or Mac, which keeps private and regulated codebases entirely local
- ✓Purpose-built for agentic loops — file editing, test runs, iterative debugging — rather than autocomplete
- ✓Zero marginal cost once self-hosted, unlike per-token frontier coding APIs
Cons
- ×No longer a leader — GLM-5.2, MiniMax M3, DeepSeek and Qwen coder models now compete directly, and frontier closed models are clearly ahead on agentic coding
- ×Code-only by design: no vision, no computer use, no tool-native multimodality, and weak on general knowledge
- ×Thinner ecosystem than the major open-weight labs — fewer quantizations, integrations and community fine-tunes; hosted-access pricing was a temporary preview with unclear current status
- ×36GB memory floor rules out budget laptops and smaller consumer GPUs
Related comparisons
Updated 2026-08-10. Spec data sourced from official product pages and tracked in our public directory at /tools.