Pick GLM 5.2 for long-horizon agentic coding or project-level software engineering. Pick GPT-6 Astra for computer & browser use (screenspot-pro 92.7%) or cybersecurity exploit development (exploitbench 100%). Choose GLM 5.2 if you need self-hosting or data privacy; GPT-6 Astra if you want a managed API.
GLM 5.2 (Z.ai, China) and GPT-6 Astra (OpenAI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 5.2 is an open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. GPT-6 Astra is openAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: GLM 5.2 is about 7.1× cheaper on input ($1.4/$4.4 per 1M tokens vs $10/$50 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: 1M vs 1.05M tokens — 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: GPT-6 Astra is the newer model by about 3 months (released September 3, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
GLM 5.2
GPT-6 Astra
Provider
Z.ai (China)
OpenAI (US)
Released
June 16, 2026
September 3, 2026
Context window
1M (~1,500 pages)
1.05M tokens (~1,575 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$10/$50 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
96.3%
Who wins what
Long-horizon agentic coding: GLM 5.2 — GPT-6 Astra is comparatively weak here — trails Meta's Muse Spark 1.3 on some coding evals (DeepSWE v1.1: 74.1 vs 75.4)
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 runs cheaper at $1.4/$4.4 per 1M tokens.
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 its weights are open while GPT-6 Astra is API-only.
Computer & browser use (ScreenSpot-Pro 92.7%): GPT-6 Astra — OpenAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks — and it is the newer of the two.
Cybersecurity exploit development (ExploitBench 100%): GPT-6 Astra — GPT-6 Astra lists cybersecurity exploit development (ExploitBench 100%) among its strengths; GLM 5.2 does not.
Frontier math reasoning (FrontierMath Tier 4: 97.6%): GPT-6 Astra — GPT-6 Astra lists frontier math reasoning (FrontierMath Tier 4: 97.6%) among its strengths; GLM 5.2 does not.
Lowest cost at scale: GLM 5.2 — At $1.4/$4.4 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 5.2 — At $1.4/$4.4 per 1M tokens it undercuts GPT-6 Astra, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GPT-6 Astra — Larger 1.05M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs: GLM 5.2 — Open weights let you run it on your own hardware; GPT-6 Astra is API-only.
Anyone whose priority is long-horizon agentic coding: GLM 5.2 — It is specifically built for that.
Anyone whose priority is computer & browser use (screenspot-pro 92.7%): GPT-6 Astra — That is its strongest area.
An enterprise with regional data-residency rules: GPT-6 Astra or GLM 5.2 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
GPT-6 Astra: where it fits
OpenAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks. Released September 3, 2026 by OpenAI, it is built for computer & browser use (ScreenSpot-Pro 92.7%), cybersecurity exploit development (ExploitBench 100%), frontier math reasoning (FrontierMath Tier 4: 97.6%), and long-context recall (MRCR v2 512K-1M: 96.3%).
Its trade-offs: no native audio or video input, pricing doubles for prompts over 272K tokens (input/cache 2x, output 1.5x), trails Meta's Muse Spark 1.3 on some coding evals (DeepSWE v1.1: 74.1 vs 75.4), and a separate opt-in "Daybreak" program gives vetted cybersecurity defenders a less-restricted version for legitimate vulnerability research; the public version already refuses ~91.5% of offensive cyber jailbreak attempts by default. At $10 in / $50 out per million tokens, it sits in the premium price band.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 5.2 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-6 Astra gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is GLM 5.2 or GPT-6 Astra better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5.2 leans toward long-horizon agentic coding while GPT-6 Astra leans toward computer & browser use (screenspot-pro 92.7%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5.2 or GPT-6 Astra?
GLM 5.2 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Astra is API-metered at $10/$50 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Effectively neither — 1M vs 1.05M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GLM 5.2 and GPT-6 Astra together?
Yes — a multi-model platform like LumiChats gives you GLM 5.2, GPT-6 Astra and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GLM 5.2 or GPT-6 Astra?
GPT-6 Astra — released September 3, 2026, about 3 months after GLM 5.2.
GLM 5.2 vs GPT-6 Astra
Z.ai · China | OpenAI · US · Updated June 2026
Quick verdict
Pick GLM 5.2 for long-horizon agentic coding or project-level software engineering. Pick GPT-6 Astra for computer & browser use (screenspot-pro 92.7%) or cybersecurity exploit development (exploitbench 100%). Choose GLM 5.2 if you need self-hosting or data privacy; GPT-6 Astra if you want a managed API.
GLM 5.2 (Z.ai, China) and GPT-6 Astra (OpenAI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 5.2 is an open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. GPT-6 Astra is openAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: GLM 5.2 is about 7.1× cheaper on input ($1.4/$4.4 per 1M tokens vs $10/$50 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: 1M vs 1.05M tokens — 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: GPT-6 Astra is the newer model by about 3 months (released September 3, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
GLM 5.2
GPT-6 Astra
Provider
Z.ai (China)
OpenAI (US)
Released
June 16, 2026
September 3, 2026
Context window
1M (~1,500 pages)
1.05M tokens (~1,575 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$10/$50 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
96.3%
Who wins what
Long-horizon agentic coding
GLM 5.2
GPT-6 Astra is comparatively weak here — trails Meta's Muse Spark 1.3 on some coding evals (DeepSWE v1.1: 74.1 vs 75.4)
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 runs cheaper at $1.4/$4.4 per 1M tokens.
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 its weights are open while GPT-6 Astra is API-only.
Computer & browser use (ScreenSpot-Pro 92.7%)
GPT-6 Astra
OpenAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks — and it is the newer of the two.
Cybersecurity exploit development (ExploitBench 100%)
GPT-6 Astra
GPT-6 Astra lists cybersecurity exploit development (ExploitBench 100%) among its strengths; GLM 5.2 does not.
Frontier math reasoning (FrontierMath Tier 4: 97.6%)
GPT-6 Astra
GPT-6 Astra lists frontier math reasoning (FrontierMath Tier 4: 97.6%) among its strengths; GLM 5.2 does not.
Lowest cost at scale
GLM 5.2
At $1.4/$4.4 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 5.2
At $1.4/$4.4 per 1M tokens it undercuts GPT-6 Astra, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-6 Astra
Larger 1.05M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ GLM 5.2
Open weights let you run it on your own hardware; GPT-6 Astra is API-only.
Anyone whose priority is long-horizon agentic coding
→ GLM 5.2
It is specifically built for that.
Anyone whose priority is computer & browser use (screenspot-pro 92.7%)
→ GPT-6 Astra
That is its strongest area.
An enterprise with regional data-residency rules
→ GPT-6 Astra or GLM 5.2
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
GPT-6 Astra: where it fits
OpenAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks. Released September 3, 2026 by OpenAI, it is built for computer & browser use (ScreenSpot-Pro 92.7%), cybersecurity exploit development (ExploitBench 100%), frontier math reasoning (FrontierMath Tier 4: 97.6%), and long-context recall (MRCR v2 512K-1M: 96.3%).
Its trade-offs: no native audio or video input, pricing doubles for prompts over 272K tokens (input/cache 2x, output 1.5x), trails Meta's Muse Spark 1.3 on some coding evals (DeepSWE v1.1: 74.1 vs 75.4), and a separate opt-in "Daybreak" program gives vetted cybersecurity defenders a less-restricted version for legitimate vulnerability research; the public version already refuses ~91.5% of offensive cyber jailbreak attempts by default. At $10 in / $50 out per million tokens, it sits in the premium price band.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 5.2 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-6 Astra gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both GLM 5.2 and GPT-6 Astra 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 either model, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5.2 leans toward long-horizon agentic coding while GPT-6 Astra leans toward computer & browser use (screenspot-pro 92.7%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5.2 or GPT-6 Astra?
GLM 5.2 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Astra is API-metered at $10/$50 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Effectively neither — 1M vs 1.05M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GLM 5.2 and GPT-6 Astra together?
Yes — a multi-model platform like LumiChats gives you GLM 5.2, GPT-6 Astra and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GLM 5.2 or GPT-6 Astra?
GPT-6 Astra — released September 3, 2026, about 3 months after GLM 5.2.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.