Both are OpenAI models. GPT-5.6 Luna is the newer, generally stronger default; reach for GPT-5.4 Nano when its lower price or a specific cost or latency profile matters more than the latest capabilities.
GPT-5.4 Nano and GPT-5.6 Luna are both OpenAI models, so the real question is not which lab to trust but which tier fits your workload and budget. GPT-5.4 Nano is openAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. 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. 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-5.4 Nano is about 5× cheaper on input ($0.2/$1.25 per 1M tokens vs $1/$6 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: GPT-5.6 Luna holds 2.5× more — 1M (~1,500 pages) vs 400K (~600 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 4 months (released July 9, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-5.4 Nano
GPT-5.6 Luna
Provider
OpenAI (US)
OpenAI (US)
Released
March 17, 2026
July 9, 2026
Context window
400K (~600 pages)
1M (~1,500 pages)
Price (in/out)
$0.2/$1.25 per 1M tokens
$1/$6 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work: GPT-5.4 Nano — At $0.2/$1.25 per 1M tokens it undercuts GPT-5.6 Luna ($1/$6 per 1M tokens), and that gap compounds at volume.
Classification, extraction, ranking and sub-agent execution at scale: GPT-5.4 Nano — OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning — and it runs cheaper at $0.2/$1.25 per 1M tokens.
A 400K context in the smallest, fastest GPT-5.4 variant: GPT-5.4 Nano — GPT-5.6 Luna is comparatively weak here — weak long-context recall deep in its 1M window (MRCR far below Sol)
Cheapest GPT-5.6 tier for high-volume drafting and automation: 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 carries the larger 1M context.
Fast, affordable execution while keeping respectable coding: GPT-5.6 Luna — GPT-5.4 Nano is comparatively weak here — a nano tier — not built for hard reasoning or frontier coding
Same 1M context and programmatic tool calling as its siblings: GPT-5.6 Luna — Its 1M window holds about 2.5× more than GPT-5.4 Nano's 400K in a single prompt.
Lowest cost at scale: GPT-5.4 Nano — At $0.2/$1.25 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: GPT-5.6 Luna — Its 1M window is about 2.5× larger than GPT-5.4 Nano's 400K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GPT-5.4 Nano — At $0.2/$1.25 per 1M tokens it undercuts GPT-5.6 Luna, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GPT-5.6 Luna — Larger 1M window fits more in one prompt.
Anyone whose priority is cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work: GPT-5.4 Nano — 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.
GPT-5.4 Nano: where it fits
OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. Released March 17, 2026 by OpenAI, it is built for cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work, classification, extraction, ranking and sub-agent execution at scale, a 400K context in the smallest, fastest GPT-5.4 variant, and text and image input for cheap multimodal pipelines.
Its trade-offs are real: a nano tier — not built for hard reasoning or frontier coding, no published SWE-Bench Verified score (OpenAI reported SWE-Bench Pro instead), outclassed by GPT-5.4 and GPT-5.4 Mini whenever a task needs real depth, and image input only — no audio or video. At $0.2 in / $1.25 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
Because GPT-5.4 Nano and GPT-5.6 Luna come from the same lab (OpenAI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. GPT-5.6 Luna 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-5.6 Luna and drop down only with a concrete reason.
Frequently asked questions
Is GPT-5.4 Nano or GPT-5.6 Luna 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-5.4 Nano leans toward cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work 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, GPT-5.4 Nano or GPT-5.6 Luna?
GPT-5.4 Nano is cheaper — $0.2/$1.25 per 1M tokens vs $1/$6 per 1M tokens, roughly 5× apart on input.
Which has the bigger context window?
GPT-5.6 Luna — 1M vs 400K, about 2.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from GPT-5.4 Nano to GPT-5.6 Luna?
Since both are OpenAI models, the newer one (GPT-5.6 Luna) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, GPT-5.4 Nano or GPT-5.6 Luna?
GPT-5.6 Luna — released July 9, 2026, about 4 months after GPT-5.4 Nano.
GPT-5.4 Nano vs GPT-5.6 Luna
OpenAI · US | OpenAI · US · Updated June 2026
Quick verdict
Both are OpenAI models. GPT-5.6 Luna is the newer, generally stronger default; reach for GPT-5.4 Nano when its lower price or a specific cost or latency profile matters more than the latest capabilities.
GPT-5.4 Nano and GPT-5.6 Luna are both OpenAI models, so the real question is not which lab to trust but which tier fits your workload and budget. GPT-5.4 Nano is openAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. 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. 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-5.4 Nano is about 5× cheaper on input ($0.2/$1.25 per 1M tokens vs $1/$6 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: GPT-5.6 Luna holds 2.5× more — 1M (~1,500 pages) vs 400K (~600 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 4 months (released July 9, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-5.4 Nano
GPT-5.6 Luna
Provider
OpenAI (US)
OpenAI (US)
Released
March 17, 2026
July 9, 2026
Context window
400K (~600 pages)
1M (~1,500 pages)
Price (in/out)
$0.2/$1.25 per 1M tokens
$1/$6 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work
GPT-5.4 Nano
At $0.2/$1.25 per 1M tokens it undercuts GPT-5.6 Luna ($1/$6 per 1M tokens), and that gap compounds at volume.
Classification, extraction, ranking and sub-agent execution at scale
GPT-5.4 Nano
OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning — and it runs cheaper at $0.2/$1.25 per 1M tokens.
A 400K context in the smallest, fastest GPT-5.4 variant
GPT-5.4 Nano
GPT-5.6 Luna is comparatively weak here — weak long-context recall deep in its 1M window (MRCR far below Sol)
Cheapest GPT-5.6 tier for high-volume drafting and automation
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 carries the larger 1M context.
Fast, affordable execution while keeping respectable coding
GPT-5.6 Luna
GPT-5.4 Nano is comparatively weak here — a nano tier — not built for hard reasoning or frontier coding
Same 1M context and programmatic tool calling as its siblings
GPT-5.6 Luna
Its 1M window holds about 2.5× more than GPT-5.4 Nano's 400K in a single prompt.
Lowest cost at scale
GPT-5.4 Nano
At $0.2/$1.25 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GPT-5.6 Luna
Its 1M window is about 2.5× larger than GPT-5.4 Nano's 400K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GPT-5.4 Nano
At $0.2/$1.25 per 1M tokens it undercuts GPT-5.6 Luna, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-5.6 Luna
Larger 1M window fits more in one prompt.
Anyone whose priority is cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work
→ GPT-5.4 Nano
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.
GPT-5.4 Nano: where it fits
OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. Released March 17, 2026 by OpenAI, it is built for cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work, classification, extraction, ranking and sub-agent execution at scale, a 400K context in the smallest, fastest GPT-5.4 variant, and text and image input for cheap multimodal pipelines.
Its trade-offs are real: a nano tier — not built for hard reasoning or frontier coding, no published SWE-Bench Verified score (OpenAI reported SWE-Bench Pro instead), outclassed by GPT-5.4 and GPT-5.4 Mini whenever a task needs real depth, and image input only — no audio or video. At $0.2 in / $1.25 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
Because GPT-5.4 Nano and GPT-5.6 Luna come from the same lab (OpenAI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. GPT-5.6 Luna 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-5.6 Luna and drop down only with a concrete reason.
Want both GPT-5.4 Nano 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.
Is GPT-5.4 Nano or GPT-5.6 Luna 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-5.4 Nano leans toward cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work 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, GPT-5.4 Nano or GPT-5.6 Luna?
GPT-5.4 Nano is cheaper — $0.2/$1.25 per 1M tokens vs $1/$6 per 1M tokens, roughly 5× apart on input.
Which has the bigger context window?
GPT-5.6 Luna — 1M vs 400K, about 2.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from GPT-5.4 Nano to GPT-5.6 Luna?
Since both are OpenAI models, the newer one (GPT-5.6 Luna) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, GPT-5.4 Nano or GPT-5.6 Luna?
GPT-5.6 Luna — released July 9, 2026, about 4 months after GPT-5.4 Nano.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.