Both are OpenAI models. GPT-5.6 Luna is the newer, generally stronger default; reach for GPT-5.3-Codex when a specific cost or latency profile matters more than the latest capabilities.
GPT-5.3-Codex 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-5.3-Codex is openAI's coding-specialized agent model for autonomous software engineering. 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-5.6 Luna is about 1.8× cheaper on input ($1/$6 per 1M tokens vs $1.75/$14 per 1M tokens) — modest, but it adds up at steady volume.
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 5 months (released July 9, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-5.3-Codex
GPT-5.6 Luna
Provider
OpenAI (US)
OpenAI (US)
Released
February 24, 2026
July 9, 2026
Context window
400K (~600 pages)
1M (~1,500 pages)
Price (in/out)
$1.75/$14 per 1M tokens
$1/$6 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Dedicated coding agent: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
CLI and IDE integration: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
Autonomous software tasks: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
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.
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.
Largest single-prompt input: GPT-5.6 Luna — Its 1M window is about 2.5× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GPT-5.6 Luna — At $1/$6 per 1M tokens it undercuts GPT-5.3-Codex, 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 dedicated coding agent: GPT-5.3-Codex — 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-5.3-Codex: where it fits
OpenAI's coding-specialized agent model for autonomous software engineering. Released February 24, 2026 by OpenAI, it is built for dedicated coding agent, cLI and IDE integration, autonomous software tasks, and tool calling.
Its trade-offs are real: coding-specialized, narrower general use, and retired in favor of GPT-5.5 Codex. At $1.75 in / $14 out per million tokens, it sits in the mid 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-5.3-Codex 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-5.3-Codex 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-5.3-Codex leans toward dedicated coding agent 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-5.3-Codex or GPT-5.6 Luna?
GPT-5.6 Luna is cheaper — $1.75/$14 per 1M tokens vs $1/$6 per 1M tokens, roughly 1.8× 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-5.3-Codex 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-5.3-Codex or GPT-5.6 Luna?
GPT-5.6 Luna — released July 9, 2026, about 5 months after GPT-5.3-Codex.
GPT-5.3-Codex 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-5.3-Codex when a specific cost or latency profile matters more than the latest capabilities.
GPT-5.3-Codex 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-5.3-Codex is openAI's coding-specialized agent model for autonomous software engineering. 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-5.6 Luna is about 1.8× cheaper on input ($1/$6 per 1M tokens vs $1.75/$14 per 1M tokens) — modest, but it adds up at steady volume.
▸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 5 months (released July 9, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-5.3-Codex
GPT-5.6 Luna
Provider
OpenAI (US)
OpenAI (US)
Released
February 24, 2026
July 9, 2026
Context window
400K (~600 pages)
1M (~1,500 pages)
Price (in/out)
$1.75/$14 per 1M tokens
$1/$6 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Dedicated coding agent
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
CLI and IDE integration
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
Autonomous software tasks
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
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.
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.
Largest single-prompt input
GPT-5.6 Luna
Its 1M window is about 2.5× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GPT-5.6 Luna
At $1/$6 per 1M tokens it undercuts GPT-5.3-Codex, 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 dedicated coding agent
→ GPT-5.3-Codex
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-5.3-Codex: where it fits
OpenAI's coding-specialized agent model for autonomous software engineering. Released February 24, 2026 by OpenAI, it is built for dedicated coding agent, cLI and IDE integration, autonomous software tasks, and tool calling.
Its trade-offs are real: coding-specialized, narrower general use, and retired in favor of GPT-5.5 Codex. At $1.75 in / $14 out per million tokens, it sits in the mid 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-5.3-Codex 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-5.3-Codex 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-5.3-Codex 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-5.3-Codex leans toward dedicated coding agent 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-5.3-Codex or GPT-5.6 Luna?
GPT-5.6 Luna is cheaper — $1.75/$14 per 1M tokens vs $1/$6 per 1M tokens, roughly 1.8× 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-5.3-Codex 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-5.3-Codex or GPT-5.6 Luna?
GPT-5.6 Luna — released July 9, 2026, about 5 months after GPT-5.3-Codex.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.