GPT-6 Luna wins on price ($0.1 vs $0.2/1M input). GPT-5.6 Luna wins on coding (88 vs 84). For most workflows, GPT-6 Luna is the stronger default — openai's budget tier at half the old price.
The safest GPT-6 Luna vs GPT-5.6 Luna default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GPT-6 Luna is the strongest answer here for GPT-6 Luna vs GPT-5.6 Luna — pick it when quality of output matters more than the $0.10/1M/1M input you pay for it.
Claude Sonnet 5.5 is the cheaper way in for GPT-6 Luna vs GPT-5.6 Luna, at $2.00/1M/1M input against GPT-6 Luna's $0.10/1M/1M.
GPT-5.6 Luna is the fastest of these for GPT-6 Luna vs GPT-5.6 Luna — worth it when latency is what the reader notices, not the last few points of reasoning depth.
GPT-5.6 Luna leads on coding with a score of 88 vs 84 for GPT-6 Luna.
GPT-6 Luna is cheaper at $0.1/1M input tokens vs $0.2/1M for GPT-5.6 Luna.
GPT-6 Luna is the stronger default for coding tasks.
GPT-6 Luna is the safer default: it is built for high-volume, focused tasks where cost per call decides the model, which covers most of what people bring to this comparison.
GPT-5.6 Luna earns its place when your work is mostly cheap high-throughput summarization and drafting, even though it loses the overall count here.
Both models serve different primary workflows — GPT-6 Luna for high-volume and focused tasks where cost per call decides the model, GPT-5.6 Luna for cheap high-throughput summarization and drafting — so running each where it has a clear edge often beats forcing one to do both.
Switch the scoring lens to see whether the GPT-6 Luna vs GPT-5.6 Luna answer changes when cost, speed, or long-document depth leads the decision.
OpenAI / Budget / Aug 6, 2026
Earlier OpenAI budget tier — GPT-6 Luna costs half as much.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
Your workload actually uses the long context window — recall drops to 41% past 512K tokens.
Where the GPT-6 Luna vs GPT-5.6 Luna recommendation shifts once you weigh price or latency differently.
OpenAI's budget tier at half the old price.
Earlier OpenAI budget tier — GPT-6 Luna costs half as much.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GPT-6 LunaOpenAI | $0.10/1M | $0.50/1M | $2.00 | 1.1M tokens | Very fast | 84 | 82 | 80 |
| GPT-5.6 LunaOpenAI | $0.20/1M | $1.20/1M | $4.40 | 1.1M tokens | Fast | 88 | 85 | 84 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for GPT-6 Luna vs GPT-5.6 Luna, what it is genuinely good at, and where we would steer you away from it.
The default answer for GPT-6 Luna vs GPT-5.6 Luna — 84/100 on the coding axis, and the model we would start with unless the price below rules it out.
OpenAI's September 22, 2026 low-cost reasoning model, replacing GPT-5.6 Luna at $0.10/$0.50 per 1M — half the predecessor's price, with the same 1.05M context.
You are choosing a budget model from scratch — Claude Haiku 5.5 costs the same and scores higher on Artificial Analysis's index.
A cheaper Luna rather than a smarter one. At $0.10/$0.50 it matches Claude Haiku 5.5 on price, but independent scoring puts Haiku 5.5 slightly ahead; choose Luna when you are already on the OpenAI stack.
Full pricing, benchmark table and release notes on the GPT-6 Luna page.
Here for latency: it answers fastest of anything listed for GPT-6 Luna vs GPT-5.6 Luna, at 88/100 on coding.
The small, fast, cheap tier of the GPT-5.6 family — near-frontier scores on many benchmarks at commodity pricing after its ~80% July price cut.
Your workload actually uses the long context window — recall drops to 41% past 512K tokens.
Superseded: GPT-6 Luna (September 22, 2026) costs half as much at $0.10/$0.50. After its July price cut, GPT-5.6 Luna delivered benchmark scores that embarrass models 10x its price. Just don't trust it with genuinely long context — recall collapses past 512K tokens.
Full pricing, benchmark table and release notes on the GPT-5.6 Luna page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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GPT-6 Luna wins on more of the categories we score — coding, writing, budget — so it is the better default of the two. GPT-5.6 Luna is the better pick when your work is mostly cheap high-throughput summarization and drafting. Neither is universally "better": GPT-6 Luna is aimed at high-volume and focused tasks where cost per call decides the model, GPT-5.6 Luna at cheap high-throughput summarization and drafting.
GPT-6 Luna is cheaper at $0.1/1M input and $0.5/1M output. GPT-5.6 Luna costs $0.2/1M input and $1.2/1M output.
Both GPT-6 Luna and GPT-5.6 Luna have the same 1.05M context window.
GPT-5.6 Luna is better for coding with a score of 88 vs GPT-6 Luna's 84 (out of 100). Claude Opus 5.5 is the overall coding leader in this directory at 100/100.
GPT-6 Luna is faster with a very fast speed rating (score: 5) vs GPT-5.6 Luna's fast rating (score: 4). Speed matters most for interactive and high-throughput work; for batch jobs the GPT-5.6 Luna latency penalty is usually invisible.
Artificial Analysis scores it 38 on its Intelligence Index, behind Claude Haiku 5.5 (43) and Gemini 3.8 Flash (41). Prompts above 272K input tokens are billed at a higher rate. Avoid it if you are choosing a budget model from scratch — Claude Haiku 5.5 costs the same and scores higher on Artificial Analysis's index. That is the main case for looking at GPT-5.6 Luna instead.
Long-context recall collapses at scale: 41.3% on 512K–1M token tasks vs Terra's 72.5%. Text and image input only — no video, audio, or native PDF ingestion like Gemini 3.6 Flash. Avoid it if your workload actually uses the long context window — recall drops to 41% past 512K tokens. Against GPT-6 Luna specifically, the gap shows up most on coding (84 vs 88).
Take a moderate workload of 10M input and 2M output tokens a month. GPT-6 Luna runs $2.00 (at $0.1/1M in and $0.5/1M out); GPT-5.6 Luna runs $4.40 (at $0.2/1M in and $1.2/1M out). That is a $2.40/month difference — GPT-6 Luna is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is GPT-6 Luna for high-volume and focused tasks where cost per call decides the model, with GPT-5.6 Luna handling cheap high-throughput summarization and drafting. Since GPT-6 Luna is both the stronger and the cheaper option here, a split mainly makes sense if GPT-5.6 Luna covers a capability you specifically need.