Both are Z.ai models. GLM 5.1 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 5.1 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 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. 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: 200K vs 200K — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
Recency: GLM 5.1 is the newer model by about 4 months (released April 7, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 4.7
GLM 5.1
Provider
Z.ai (China)
Z.ai (China)
Released
December 22, 2025
April 7, 2026
Context window
200K (~304 pages)
200K (~300 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 5.1 ($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 5.1 does not.
Long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours — and it is the newer of the two.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch): GLM 5.1 — GLM 4.7 is comparatively weak here — its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro
Sustained tool use across thousands of calls: GLM 5.1 — GLM 5.1 lists sustained tool use across thousands of calls among its strengths; GLM 4.7 does not.
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.
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 5.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GLM 4.7 — Larger 200K 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 autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — 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 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. 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 5.1 come from the same lab (Z.ai), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. GLM 5.1 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 5.1 and drop down only with a concrete reason.
Frequently asked questions
Is GLM 4.7 or GLM 5.1 better for coding?
Public SWE-Bench figures are not available for GLM 5.1, 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 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 4.7 or GLM 5.1?
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?
Effectively neither — 200K vs 200K is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from GLM 4.7 to GLM 5.1?
Since both are Z.ai models, the newer one (GLM 5.1) 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 5.1?
GLM 5.1 — released April 7, 2026, about 4 months after GLM 4.7.
GLM 4.7 vs GLM 5.1
Z.ai · China | Z.ai · China · Updated June 2026
Quick verdict
Both are Z.ai models. GLM 5.1 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 5.1 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 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. 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: 200K vs 200K — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
▸Recency: GLM 5.1 is the newer model by about 4 months (released April 7, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 4.7
GLM 5.1
Provider
Z.ai (China)
Z.ai (China)
Released
December 22, 2025
April 7, 2026
Context window
200K (~304 pages)
200K (~300 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 5.1 ($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 5.1 does not.
Long-horizon autonomous agentic engineering (up to 8-hour runs)
GLM 5.1
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours — and it is the newer of the two.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)
GLM 5.1
GLM 4.7 is comparatively weak here — its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro
Sustained tool use across thousands of calls
GLM 5.1
GLM 5.1 lists sustained tool use across thousands of calls among its strengths; GLM 4.7 does not.
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.
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 5.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GLM 4.7
Larger 200K 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 autonomous agentic engineering (up to 8-hour runs)
→ GLM 5.1
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 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. 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 5.1 come from the same lab (Z.ai), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. GLM 5.1 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 5.1 and drop down only with a concrete reason.
Want both GLM 4.7 and GLM 5.1 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 5.1, 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 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 4.7 or GLM 5.1?
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?
Effectively neither — 200K vs 200K is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from GLM 4.7 to GLM 5.1?
Since both are Z.ai models, the newer one (GLM 5.1) 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 5.1?
GLM 5.1 — released April 7, 2026, about 4 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.