Model index · Text · text generation · reasoning · OpenAI · released 2026-07-09
GPT-5.6 Luna pricing
Served by 6 providers from $0.2250 to $0.4950 per 1M tokens on the standard tier — a 2.2× gap for identical weights.
A reasoning model — the headline price is not the whole bill
This model thinks before it answers, and those thinking tokens are billed at the output rate while never appearing in the response. A request can therefore cost several times what the per-1M figure suggests, and the multiplier depends on your prompts rather than on anything we can publish. Compare it against other reasoning models — the alternatives below are ordered to put those first.
GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for...
OpenAI · first published 2026-07-09 · Text model · text generation · 1.05M context · description as published by the source, not written by us
$0.2250
Cheapest standard /1M — OpenAI
1.05M
Native context window
9
Providers serving it
100
Measured quality /100 · frontier
Measured quality
Our own measurement (text-v1 v1), run 2026-08-12 at temperature 0 and scored by deterministic checks — no LLM judge, no vendor claim. Served by openai/gpt-5.6-luna. Basic-competence probes: 7/7 passed — only 7 of 12 could be run. Ranking probes: 5/5. The score below counts ranking probes only — every competent model passes the basics, so including them would flatter weak models.
Incomplete run
5 of the 17 probes could not be executed — the provider returned an error rather than an answer, so those checks are missing rather than failed. Ratios above are passes out of probes that actually ran, not out of the full suite.
| Capability | Score | Confidence |
|---|---|---|
| coding | 100/100 | 0.333 — too low to rank on |
| document understanding | 100/100 | 0.667 |
| factuality | 100/100 | 0.333 — too low to rank on |
| instruction following | 100/100 | 0.333 — too low to rank on |
| long context | 100/100 | 0 — too low to rank on |
| math | 100/100 | 1 |
| multilingual | 0/100 | 0 — too low to rank on |
| reasoning | 100/100 | 0 — too low to rank on |
| safety | 100/100 | 0.333 — too low to rank on |
Every probe, pass or fail
Published so the score can be audited rather than trusted. Ranking probes carry the score; basics are a floor check.
| Probe | Capability | Tier | Result |
|---|---|---|---|
| instruction-exact-word | instruction following | basic | pass |
| math-rectangle-area | math | basic | pass |
| math-percentage-chain | math | ranking | pass |
| reasoning-relative-order | reasoning | basic | fail |
| reasoning-negation | reasoning | basic | fail |
| format-json-extract | document understanding | basic | pass |
| long-context-needle | long context | basic | fail |
| instruction-negative-constraint | instruction following | basic | fail |
| multilingual-exact-translation | multilingual | basic | fail |
| safety-over-refusal | safety | basic | pass |
| factuality-stable | factuality | basic | pass |
| coding-trace-output | coding | basic | pass |
| math-compound-ordering | math | ranking | pass |
| long-context-needle-distractors | long context | basic | pass |
| reasoning-date-arithmetic | reasoning | ranking | pass |
| instruction-conflicting-order | instruction following | ranking | pass |
| document-computed-field | document understanding | ranking | pass |
What this does not measure: prose quality, tone, code architecture or taste. Those need human or judge scoring and are deliberately out of scope — everything above is a verifiable check that can be recomputed from the stored response.
At 1M requests/month (1500 in / 500 out tokens), moving from Amazon (Bedrock) to OpenAI saves about $540/month.
Illustrative traffic shape, stated so you can check it against your own. Same weights both sides — but confirm quantisation and context below before switching.
Every provider and service tier
Standard on-demand rows first, cheapest of each group first. Tiers are not interchangeable — see the note below the table.
| Provider | In /1M | Out /1M | Blended /1M | Tier | Region | Source |
|---|---|---|---|---|---|---|
| OpenAI cheapest standard | $0.1000 | $0.6000 | $0.2250 | standard | — | third_party |
| Azure | $0.2000 | $1.20 | $0.4500 | standard | — | third_party |
| OpenRouter (default route) * resells other hosts | $0.2000 | $1.20 | $0.4500 | standard | — | provider_api |
| Azure | $0.2200 | $1.32 | $0.4950 | standard | eu | third_party |
| Amazon (Bedrock) | $0.2200 | $1.32 | $0.4950 | standard | us-east-1 | third_party |
| Amazon (Bedrock) | $0.2200 | $1.32 | $0.4950 | standard | — | official_docs |
| OpenAI | $0.0500 | $0.3000 | $0.1125 | flex | — | third_party |
| OpenRouter (default route) * resells other hosts | $0.1000 | $0.6000 | $0.2250 | batch | — | provider_api |
| OpenAI | $0.2000 | $1.20 | $0.4500 | priority | — | third_party |
Not every row is the same product
This table mixes service tiers, and they are not interchangeable. Rows marked standard are ordinary on-demand requests. The others trade delivery for price:
- flex — queued at lower priority, so latency is higher and requests can be deferred under load
- batch — submitted as a batch and collected later, typically within 24 hours — not for interactive use
- priority — reserved capacity for tighter, more predictable latency
Every comparison elsewhere on this site uses the standard tier, so a model is never shown at a queued rate beside another model's on-demand rate.
Cheaper if your workload can wait: flex at $0.1125 per 1M on OpenAI — 50% below standard , queued at lower priority, so latency is higher and requests can be deferred under load.
* OpenRouter chooses a host for each request, and the figure above is what its default choice costs — not a fixed rate for this model. Selecting a specific host through the same account costs whatever that host charges, which is why this row can sit above or below the hosts listed beside it. Token rates are passed through from the underlying provider with no markup, so the per-1M price above is what that host charges directly — OpenRouter's own charge sits on funding instead: 5.5% (min $0.80) by Stripe or 5% by Coinbase to buy credits, 5% of the equivalent spend if you bring your own provider key. So budget about 5% above the rate shown unless you already hold credit. Fee schedule.
Compare GPT-5.6 Luna with alternatives
One model per vendor, ordered by how prominent the vendor is and how many independent providers serve the model — hosts only carry what customers ask for, so that is a real demand signal. This is not traffic or popularity data, which we do not have. Every alternative below does the same job — text generation — because a price comparison between a generator and, say, an upscaler is arithmetic rather than advice. Prices on both sides are standard on-demand rates, so the comparison is like for like — a discounted batch or flex rate is never shown against another model’s standard rate.
| Alternative | Vendor | $/1M | vs GPT-5.6 Luna | Quality | Kind | Context | Hosts |
|---|---|---|---|---|---|---|---|
| DeepSeek V4 Pro | DeepSeek | $0.5281 Baidu | 135% dearer | 100/100 | reasoning open weights | 1.05M | 19 |
| Claude Sonnet 5 | Anthropic | $4.00 Anthropic | 1678% dearer | 100/100 | reasoning | 1M | 9 |
| Gemini 3.5 Flash Lite | $0.4250 | 89% dearer | 100/100 | reasoning | 1.05M | 7 | |
| Qwen3 VL 235B A22B Thinking | Alibaba | $1.21 DeepInfra | 438% dearer | 100/100 | reasoning open weights | 131k | 4 |
| Mistral Small 4 | Mistral | $0.2625 Mistral | 17% dearer | 40/100 | reasoning | 262k | 2 |
| Kimi K2.5 | MoonshotAI | $0.7875 DigitalOcean | 250% dearer | 75/100 | reasoning open weights | 262k | 15 |
| MiniMax M2.5 | Minimax | $0.3900 Inceptron | 73% dearer | 0/100 | reasoning open weights | 205k | 12 |
| Grok 4.3 | xAI | $1.56 xAI | 594% dearer | 100/100 | reasoning | 1M | 4 |
Prices are the cheapest host for each model, blended at a 3:1 input:output ratio. "Quality" is our own probe suite where we have run it — see what it measures. A cheaper alternative is only a real saving if it also passes on the capability your workload needs.
Before you switch
Identical weights do not guarantee identical output. Check that the cheaper host serves the model at full precision rather than a quantised copy, that its context window matches what you need, and that its rate limits carry your peak traffic. A cheaper host that cannot take your throughput is not cheaper — it is an outage.
Move to OpenAI without a rewrite
VernaOne fronts every provider on this page with one API, so switching host is a config change — with automatic fallback if quality or latency regresses.
Try VernaOne free →Frequently asked
How much does GPT-5.6 Luna cost?
The cheapest standard-tier provider is OpenAI at $0.2250 per 1M tokens blended (3:1 input:output). A flex tier is available at $0.1125, 50% less, if the work can be queued. The dearest is Amazon (Bedrock) at $0.4950 — a 2.2× difference for the same weights. Prices change often; verify with the provider before budgeting.
Which provider is best for GPT-5.6 Luna?
Cheapest is not automatically best. Compare quantisation (a lossy copy of the weights changes output), the context window each host actually serves, and rate limits. Where a provider serves less context than the model's native window we flag it in the table above.
Can I switch providers for GPT-5.6 Luna without changing code?
Yes, if you route through an abstraction. VernaOne exposes one API across every provider listed here, so switching host is a config change and you keep automatic fallback if the new one degrades.
← All models · Generated catalog of AI models and every provider that serves them, with normalised prices in USD per 1,000,000 tokens. Blended prices assume a 3:1 input:output ratio.