Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Pick GPT-5.6 Luna for cheapest gpt-5.6 tier for high-volume drafting and automation or fast, affordable execution while keeping respectable coding. Choose GLM 5 if you need self-hosting or data privacy; GPT-5.6 Luna if you want a managed API.
GLM 5 (Z.ai, China) and GPT-5.6 Luna (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 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. GPT-5.6 Luna is the budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: GLM 5 ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-5.6 Luna is API-metered at $1/$6 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: GPT-5.6 Luna 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: GPT-5.6 Luna is the newer model by about 5 months (released July 9, 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
GPT-5.6 Luna
Provider
Z.ai (China)
OpenAI (US)
Released
February 11, 2026
July 9, 2026
Context window
200K (~300 pages)
1M (~1,500 pages)
Price (in/out)
$1/$3.2 per 1M tokens
$1/$6 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image, 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 — A core design strength of GLM 5.
Complex systems design and backend reasoning: GLM 5 — A core design strength of GLM 5.
Iterative self-correction on autonomous tasks: GLM 5 — A core design strength of GLM 5.
Cheapest GPT-5.6 tier for high-volume drafting and automation: GPT-5.6 Luna — A core design strength of GPT-5.6 Luna.
Fast, affordable execution while keeping respectable coding: GPT-5.6 Luna — A core design strength of GPT-5.6 Luna.
Same 1M context and programmatic tool calling as its siblings: GPT-5.6 Luna — A core design strength of GPT-5.6 Luna.
Largest single-prompt input: GPT-5.6 Luna — Its 1M window is about 5× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: GPT-5.6 Luna — Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs: GLM 5 — Open weights let you run it on your own hardware; GPT-5.6 Luna is API-only.
Anyone whose priority is agentic planning and long-horizon coding workflows: GLM 5 — It is specifically built for that.
Anyone whose priority is cheapest gpt-5.6 tier for high-volume drafting and automation: GPT-5.6 Luna — That is its strongest area.
An enterprise with regional data-residency rules: GPT-5.6 Luna or GLM 5 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 11, 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.
GPT-5.6 Luna: where it fits
The budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning. Released July 9, 2026 by OpenAI, it is built for cheapest GPT-5.6 tier for high-volume drafting and automation, fast, affordable execution while keeping respectable coding, same 1M context and programmatic tool calling as its siblings, and high-throughput simple agentic jobs.
Its trade-offs: weak long-context recall deep in its 1M window (MRCR far below Sol), and lowest raw capability of the three GPT-5.6 tiers; no open weights. At $1 in / $6 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 5 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-5.6 Luna 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 or GPT-5.6 Luna better for coding?
Public SWE-Bench figures are not available for GPT-5.6 Luna, 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 GPT-5.6 Luna leans toward cheapest gpt-5.6 tier for high-volume drafting and automation, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or GPT-5.6 Luna?
GLM 5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-5.6 Luna is API-metered at $1/$6 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?
GPT-5.6 Luna — 1M vs 200K, about 5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and GPT-5.6 Luna together?
Yes — a multi-model platform like LumiChats gives you GLM 5, GPT-5.6 Luna 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 or GPT-5.6 Luna?
GPT-5.6 Luna — released July 9, 2026, about 5 months after GLM 5.
GLM 5 vs GPT-5.6 Luna
Z.ai · China | OpenAI · US · Updated June 2026
Quick verdict
Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Pick GPT-5.6 Luna for cheapest gpt-5.6 tier for high-volume drafting and automation or fast, affordable execution while keeping respectable coding. Choose GLM 5 if you need self-hosting or data privacy; GPT-5.6 Luna if you want a managed API.
GLM 5 (Z.ai, China) and GPT-5.6 Luna (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 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. GPT-5.6 Luna is the budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: GLM 5 ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-5.6 Luna is API-metered at $1/$6 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: GPT-5.6 Luna 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: GPT-5.6 Luna is the newer model by about 5 months (released July 9, 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
GPT-5.6 Luna
Provider
Z.ai (China)
OpenAI (US)
Released
February 11, 2026
July 9, 2026
Context window
200K (~300 pages)
1M (~1,500 pages)
Price (in/out)
$1/$3.2 per 1M tokens
$1/$6 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image, 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
A core design strength of GLM 5.
Complex systems design and backend reasoning
GLM 5
A core design strength of GLM 5.
Iterative self-correction on autonomous tasks
GLM 5
A core design strength of GLM 5.
Cheapest GPT-5.6 tier for high-volume drafting and automation
GPT-5.6 Luna
A core design strength of GPT-5.6 Luna.
Fast, affordable execution while keeping respectable coding
GPT-5.6 Luna
A core design strength of GPT-5.6 Luna.
Same 1M context and programmatic tool calling as its siblings
GPT-5.6 Luna
A core design strength of GPT-5.6 Luna.
Largest single-prompt input
GPT-5.6 Luna
Its 1M window is about 5× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ GPT-5.6 Luna
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ GLM 5
Open weights let you run it on your own hardware; GPT-5.6 Luna is API-only.
Anyone whose priority is agentic planning and long-horizon coding workflows
→ GLM 5
It is specifically built for that.
Anyone whose priority is cheapest gpt-5.6 tier for high-volume drafting and automation
→ GPT-5.6 Luna
That is its strongest area.
An enterprise with regional data-residency rules
→ GPT-5.6 Luna or GLM 5
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 11, 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.
GPT-5.6 Luna: where it fits
The budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning. Released July 9, 2026 by OpenAI, it is built for cheapest GPT-5.6 tier for high-volume drafting and automation, fast, affordable execution while keeping respectable coding, same 1M context and programmatic tool calling as its siblings, and high-throughput simple agentic jobs.
Its trade-offs: weak long-context recall deep in its 1M window (MRCR far below Sol), and lowest raw capability of the three GPT-5.6 tiers; no open weights. At $1 in / $6 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 5 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-5.6 Luna 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 and GPT-5.6 Luna 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 GPT-5.6 Luna, 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 GPT-5.6 Luna leans toward cheapest gpt-5.6 tier for high-volume drafting and automation, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5 or GPT-5.6 Luna?
GLM 5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-5.6 Luna is API-metered at $1/$6 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?
GPT-5.6 Luna — 1M vs 200K, about 5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and GPT-5.6 Luna together?
Yes — a multi-model platform like LumiChats gives you GLM 5, GPT-5.6 Luna 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 or GPT-5.6 Luna?
GPT-5.6 Luna — released July 9, 2026, about 5 months after GLM 5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.