Both are Z.ai models. GLM 5.2 is the newer, generally stronger default; reach for GLM 4.7 when its lower price or a specific cost or latency profile matters more than the latest capabilities.
GLM 4.7 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 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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
Price: GLM 4.7 is about 2.3× cheaper on input ($0.6/$2.2 per 1M tokens vs $1.4/$4.4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: GLM 5.2 holds 4.9× more — 1M (~1,500 pages) vs 200K (~304 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 6 months (released June 13, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 4.7
GLM 5.2
Provider
Z.ai (China)
Z.ai (China)
Released
December 22, 2025
June 13, 2026
Context window
200K (~304 pages)
1M (~1,500 pages)
Price (in/out)
$0.6/$2.2 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
73.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions: GLM 4.7 — An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it runs cheaper at $0.6/$2.2 per 1M tokens.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — At $0.6/$2.2 per 1M tokens it undercuts GLM 5.2 ($1.4/$4.4 per 1M tokens), and that gap compounds at volume.
An unusually generous 128K maximum output, which suits bulk refactors and long generation: GLM 4.7 — GLM 4.7 lists an unusually generous 128K maximum output, which suits bulk refactors and long generation among its strengths; GLM 5.2 does not.
Long-horizon agentic coding: GLM 5.2 — Its 1M window holds about 4.9× more than GLM 4.7'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 4.7 — At $0.6/$2.2 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 4.9× larger than GLM 4.7's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GLM 4.7 — At $0.6/$2.2 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 genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions: GLM 4.7 — It is specifically built for that.
Anyone whose priority is long-horizon agentic coding: GLM 5.2 — That is its strongest area.
GLM 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 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 13, 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 4.7 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.
Frequently asked questions
Is GLM 4.7 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 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions 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 4.7 or GLM 5.2?
GLM 4.7 is cheaper — $0.6/$2.2 per 1M tokens vs $1.4/$4.4 per 1M tokens, roughly 2.3× apart on input.
Which has the bigger context window?
GLM 5.2 — 1M vs 200K, about 4.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from GLM 4.7 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 4.7 or GLM 5.2?
GLM 5.2 — released June 13, 2026, about 6 months after GLM 4.7.
GLM 4.7 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 4.7 when its lower price or a specific cost or latency profile matters more than the latest capabilities.
GLM 4.7 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 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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 4.7 is about 2.3× cheaper on input ($0.6/$2.2 per 1M tokens vs $1.4/$4.4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: GLM 5.2 holds 4.9× more — 1M (~1,500 pages) vs 200K (~304 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 6 months (released June 13, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 4.7
GLM 5.2
Provider
Z.ai (China)
Z.ai (China)
Released
December 22, 2025
June 13, 2026
Context window
200K (~304 pages)
1M (~1,500 pages)
Price (in/out)
$0.6/$2.2 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
73.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions
GLM 4.7
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it runs cheaper at $0.6/$2.2 per 1M tokens.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
At $0.6/$2.2 per 1M tokens it undercuts GLM 5.2 ($1.4/$4.4 per 1M tokens), and that gap compounds at volume.
An unusually generous 128K maximum output, which suits bulk refactors and long generation
GLM 4.7
GLM 4.7 lists an unusually generous 128K maximum output, which suits bulk refactors and long generation among its strengths; GLM 5.2 does not.
Long-horizon agentic coding
GLM 5.2
Its 1M window holds about 4.9× more than GLM 4.7'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 4.7
At $0.6/$2.2 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 4.9× larger than GLM 4.7's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GLM 4.7
At $0.6/$2.2 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 genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions
→ GLM 4.7
It is specifically built for that.
Anyone whose priority is long-horizon agentic coding
→ GLM 5.2
That is its strongest area.
GLM 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 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 13, 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 4.7 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.
Want both GLM 4.7 and GLM 5.2 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
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 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions 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 4.7 or GLM 5.2?
GLM 4.7 is cheaper — $0.6/$2.2 per 1M tokens vs $1.4/$4.4 per 1M tokens, roughly 2.3× apart on input.
Which has the bigger context window?
GLM 5.2 — 1M vs 200K, about 4.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from GLM 4.7 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 4.7 or GLM 5.2?
GLM 5.2 — released June 13, 2026, about 6 months after GLM 4.7.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.