GLM 5 vs GLM 5.2
Z.ai · China | Z.ai · China · Updated June 2026
Quick verdict
Both are Z.ai models. GLM 5.2 is the newer, generally stronger default; reach for GLM 5 when its lower price or a specific cost or latency profile matters more than the latest capabilities.
GLM 5 and GLM 5.2 are both Z.ai models, so the real question is not which lab to trust but which tier fits your workload and budget. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. GLM 5.2 is an open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
- ▸Price: GLM 5 is about 1.4× cheaper on input ($1/$3.2 per 1M tokens vs $1.4/$4.4 per 1M tokens) — modest, but it adds up at steady volume.
- ▸Context window: GLM 5.2 holds 5× more — 1M (~1,500 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: GLM 5.2 is the newer model by about 4 months (released June 16, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | GLM 5 | GLM 5.2 |
|---|---|---|
| Provider | Z.ai (China) | Z.ai (China) |
| Released | February 12, 2026 | June 16, 2026 |
| Context window | 200K (~300 pages) | 1M (~1,500 pages) |
| Price (in/out) | $1/$3.2 per 1M tokens | $1.4/$4.4 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | 77.8% | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and it runs cheaper at $1/$3.2 per 1M tokens.
Complex systems design and backend reasoning
GLM 5
GLM 5 lists complex systems design and backend reasoning among its strengths; GLM 5.2 does not.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks among its strengths; GLM 5.2 does not.
Long-horizon agentic coding
GLM 5.2
Its 1M window holds about 5× more than GLM 5's 200K in a single prompt.
Project-level software engineering
GLM 5.2
An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap — and it carries the larger 1M context.
Tool use across long-running tasks
GLM 5.2
An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap — and it is the newer of the two.
Lowest cost at scale
GLM 5
At $1/$3.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GLM 5.2
Its 1M window is about 5× larger than GLM 5's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GLM 5
At $1/$3.2 per 1M tokens it undercuts GLM 5.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GLM 5.2
Larger 1M window fits more in one prompt.
Anyone whose priority is agentic planning and long-horizon coding workflows
→ GLM 5
It is specifically built for that.
Anyone whose priority is long-horizon agentic coding
→ GLM 5.2
That is its strongest area.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget price band.
GLM 5.2: where it fits
An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. Released June 16, 2026 by Z.ai, it is built for long-horizon agentic coding, project-level software engineering, tool use across long-running tasks, and tops the open-weight intelligence index (SWE-bench Pro 62.1).
Its trade-offs: text-only — no native multimodal input, and new release with a limited third-party track record. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
Because GLM 5 and GLM 5.2 come from the same lab (Z.ai), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. GLM 5.2 is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to GLM 5.2 and drop down only with a concrete reason.
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See pricingFrequently asked questions
Is GLM 5 or GLM 5.2 better for coding?
Public SWE-Bench figures are not available for GLM 5.2, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5 leans toward agentic planning and long-horizon coding workflows while GLM 5.2 leans toward long-horizon agentic coding, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or GLM 5.2?
GLM 5 is cheaper — $1/$3.2 per 1M tokens vs $1.4/$4.4 per 1M tokens, roughly 1.4× apart on input.
Which has the bigger context window?
GLM 5.2 — 1M vs 200K, about 5× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from GLM 5 to GLM 5.2?
Since both are Z.ai models, the newer one (GLM 5.2) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, GLM 5 or GLM 5.2?
GLM 5.2 — released June 16, 2026, about 4 months after GLM 5.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.