Both are OpenAI models. GPT-6 Astra is the newer, generally stronger default; reach for GPT-5.6 Luna when its lower price or a specific cost or latency profile matters more than the latest capabilities.
GPT-5.6 Luna and GPT-6 Astra are both OpenAI models, so the real question is not which lab to trust but which tier fits your workload and budget. 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. 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. 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: GPT-5.6 Luna is about 10× cheaper on input ($1/$6 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 56 days (released September 3, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-5.6 Luna
GPT-6 Astra
Provider
OpenAI (US)
OpenAI (US)
Released
July 9, 2026
September 3, 2026
Context window
1M (~1,500 pages)
1.05M tokens (~1,575 pages)
Price (in/out)
$1/$6 per 1M tokens
$10/$50 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image, code
text, image
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
96.3%
Who wins what
Cheapest GPT-5.6 tier for high-volume drafting and automation: GPT-5.6 Luna — At $1/$6 per 1M tokens it undercuts GPT-6 Astra ($10/$50 per 1M tokens), and that gap compounds at volume.
Fast, affordable execution while keeping respectable coding: GPT-5.6 Luna — 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)
Same 1M context and programmatic tool calling as its siblings: GPT-5.6 Luna — The budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning — and it runs cheaper at $1/$6 per 1M tokens.
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; GPT-5.6 Luna 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; GPT-5.6 Luna does not.
Lowest cost at scale: GPT-5.6 Luna — At $1/$6 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: GPT-5.6 Luna — At $1/$6 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.
Anyone whose priority is cheapest gpt-5.6 tier for high-volume drafting and automation: GPT-5.6 Luna — 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.
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 are real: 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.
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
Because GPT-5.6 Luna and GPT-6 Astra come from the same lab (OpenAI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. GPT-6 Astra 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 GPT-6 Astra and drop down only with a concrete reason.
Frequently asked questions
Is GPT-5.6 Luna 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, GPT-5.6 Luna leans toward cheapest gpt-5.6 tier for high-volume drafting and automation 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, GPT-5.6 Luna or GPT-6 Astra?
GPT-5.6 Luna is cheaper — $1/$6 per 1M tokens vs $10/$50 per 1M tokens, roughly 10× apart on input.
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.
Should I upgrade from GPT-5.6 Luna to GPT-6 Astra?
Since both are OpenAI models, the newer one (GPT-6 Astra) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, GPT-5.6 Luna or GPT-6 Astra?
GPT-6 Astra — released September 3, 2026, about 56 days after GPT-5.6 Luna.
GPT-5.6 Luna vs GPT-6 Astra
OpenAI · US | OpenAI · US · Updated June 2026
Quick verdict
Both are OpenAI models. GPT-6 Astra is the newer, generally stronger default; reach for GPT-5.6 Luna when its lower price or a specific cost or latency profile matters more than the latest capabilities.
GPT-5.6 Luna and GPT-6 Astra are both OpenAI models, so the real question is not which lab to trust but which tier fits your workload and budget. 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. 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. 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: GPT-5.6 Luna is about 10× cheaper on input ($1/$6 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 56 days (released September 3, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-5.6 Luna
GPT-6 Astra
Provider
OpenAI (US)
OpenAI (US)
Released
July 9, 2026
September 3, 2026
Context window
1M (~1,500 pages)
1.05M tokens (~1,575 pages)
Price (in/out)
$1/$6 per 1M tokens
$10/$50 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image, code
text, image
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
96.3%
Who wins what
Cheapest GPT-5.6 tier for high-volume drafting and automation
GPT-5.6 Luna
At $1/$6 per 1M tokens it undercuts GPT-6 Astra ($10/$50 per 1M tokens), and that gap compounds at volume.
Fast, affordable execution while keeping respectable coding
GPT-5.6 Luna
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)
Same 1M context and programmatic tool calling as its siblings
GPT-5.6 Luna
The budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning — and it runs cheaper at $1/$6 per 1M tokens.
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; GPT-5.6 Luna 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; GPT-5.6 Luna does not.
Lowest cost at scale
GPT-5.6 Luna
At $1/$6 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
→ GPT-5.6 Luna
At $1/$6 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.
Anyone whose priority is cheapest gpt-5.6 tier for high-volume drafting and automation
→ GPT-5.6 Luna
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.
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 are real: 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.
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
Because GPT-5.6 Luna and GPT-6 Astra come from the same lab (OpenAI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. GPT-6 Astra 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 GPT-6 Astra and drop down only with a concrete reason.
Want both GPT-5.6 Luna 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, GPT-5.6 Luna leans toward cheapest gpt-5.6 tier for high-volume drafting and automation 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, GPT-5.6 Luna or GPT-6 Astra?
GPT-5.6 Luna is cheaper — $1/$6 per 1M tokens vs $10/$50 per 1M tokens, roughly 10× apart on input.
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.
Should I upgrade from GPT-5.6 Luna to GPT-6 Astra?
Since both are OpenAI models, the newer one (GPT-6 Astra) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, GPT-5.6 Luna or GPT-6 Astra?
GPT-6 Astra — released September 3, 2026, about 56 days after GPT-5.6 Luna.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.