GPT-6 Luna pricing: real cost per million tokens
What OpenAI's GPT-6 Luna really costs across 4 plans from 4 providers: monthly fees, usable allowances and real per-token prices, against the official API list price and independent leaderboards.
Data updated · How real prices are computed
As of 2026-10-08, the cheapest way to use GPT-6 Luna is Droid Max at a real cost of about $0.00198 per million tokens ($200/month for roughly 101.04B tokens a month). We track 4 plans and API channels that serve GPT-6 Luna.
- Lowest real price
- $0.00198/MTokDroid Max
- API list price
- —
- Most monthly tokens
- 101.04BDroid Max
- Plans & APIs
- 44 subscriptions · 0 metered APIs
Plot GPT-6 Luna on the price–capability chart →
GPT-6 Luna plans and real prices
Real price = monthly fee ÷ tokens you can actually use in a month. “vs API” shows how many times cheaper than the official API list price (standard workload mix) the plan is. “Evidence” opens the full calculation and sources for that row.
| Plan | Provider | Billing | Monthly fee | Monthly tokens | Real $/MTok | vs API | Confidence | Data date | |
|---|---|---|---|---|---|---|---|---|---|
| Droid Max | Factory | Subscription | $200/mo | 101.04B | $0.00198 | — | Medium | Evidence ↓ | |
| ChatGPT Plus | OpenAI | Subscription | $20/mo | 9.203B | $0.00217 | — | High | Evidence ↓ | |
| Command Code GOAT | Command Code | Subscription | $10/mo | 1.361B | $0.00735 | — | Medium | Evidence ↓ | |
| OpenCode Go | OpenCode | Subscription | $10/mo | 1.02B | $0.0098 | — | Medium | Evidence ↓ |
Behind each number
For every plan: the data date, the exact calculation, the quota basis, how leaderboard scores were mapped, and the original adoption evidence with its sources.
Droid MaxFactory$0.00197941/MTok
- Data date
- 2026-09-29 · derived; uses the data date of Droid Max · claude-opus-5.5
- Real price / MTok
- $0.00197941
- Monthly tokens
- 101,040,000,000 (101.04B)
- Quota confidence
- Medium
- Calculation
- $200 / month ÷ 101,040,000,000 tokens × 1,000,000 ≈ $0.00197941 / MTok
- Quota basis
Anthropic workload: 97% cache reads / 2.5% cache writes / 0.5% output
- Benchmark mapping
- Artificial Analysis Intelligence Index v4.3: Medium (6 configurations)
Terminal-Bench 4.0 (AA): Low (6 configurations)
Code Arena | WebDev Overall: Low (1 configuration)
Agent Arena · Overall (Ranking / Pareto): Low (1 configuration)
Artificial Analysis Coding Agent Index v1.5: Low (1 configuration)
OpenDesign Arena · Quality ranking: Low (1 configuration)Mapping confidence: how directly each score refers to the model served here.
Adoption evidence · source and rationale (translated from the Chinese record)
Derived from same-plan claude-opus-5.5 2.526B × 40.0
Factory official multipliers Opus 5.5 1.6× / gpt-6-luna 0.04×: the same Standard Usage pool scaled by multiplier — not a measurement of this model; inherits the Opus 5.5 row's Anthropic-tier workload, not recomputed for each model's own price gap; docs.factory.ai/docs/models; droid-model-multipliers-2026-09-29.json
droid-model-multipliers-2026-09-29.jsonBrowse the public evidence archive
ChatGPT PlusOpenAI$0.0021732/MTok
- Plan
- ChatGPT Plus · OpenAI
- Data date
- 2026-09-27 – 2026-09-28 · sampled
- Real price / MTok
- $0.0021732
- Monthly tokens
- 9,203,000,000 (9.203B)
- Quota confidence
- High
- Calculation
- $20 / month ÷ 9,203,000,000 tokens × 1,000,000 ≈ $0.0021732 / MTok
- Quota basis
Standard workload: 97% cache reads / 2.5% input / 0.5% output
- Benchmark mapping
- Artificial Analysis Intelligence Index v4.3: Medium (6 configurations)
Terminal-Bench 4.0 (AA): Low (6 configurations)
Code Arena | WebDev Overall: Low (1 configuration)
Agent Arena · Overall (Ranking / Pareto): Low (1 configuration)
Artificial Analysis Coding Agent Index v1.5: Low (1 configuration)
OpenDesign Arena · Quality ranking: Low (1 configuration)Mapping confidence: how directly each score refers to the model served here.
Adoption evidence · source and rationale (translated from the Chinese record)
Maintainer's local Codex measurement (effort=max): new weekly window 09-27 22:09 97% → 09-28 20:44 91%; the entire delta is gpt-6-luna total 137,648,446 tokens (input 3,129,683 / output 757,355 incl. reasoning 471,268 / cache_read 133,761,408, hit 97.71%); chatgpt-gpt6luna-plus-round2-2026-09-28.json
9.177B→9.203B (2026-09-30 decision: uniform workload-normalization for measured samples with token breakdown): segment worth $2.0293 (cache 133.76M×$0.01 + input 3.13M×$0.1 + output 0.76M×$0.5) ÷6%×4 weeks = $135.28 ÷ standard-tier blended price $0.0147/MTok; the raw-basis 91.77 is kept for comparison. Original adoption decision — 4.405B→9.177B: 137,648,446÷6%×4 weeks (original raw basis); panel integer-% readings Δpp 5~7 map to 7.888B~11.044B normalized (raw basis 78.66~110.12); this week's 22.94M tok/pp ≈ 2.08× round1's merged sample of 11.01M (2.25× of the standard tier's 10.20M); the rounding intervals on the two sides do not overlap — judged to be weekly-pool enlargement or an effort-weighting change (max this week; round1 did not record effort, and the two causal chains cannot be distinguished), not noise; ruled the new week is recorded separately — round1's 4.405B stays as comparison, not merged or averaged; within the segment gpt-5.6-luna accumulated 205,934,189 while 6sol/astra did not move, so all 6pp go to 6-luna; Pro 5x/20x not derived (follows the Sol ruling); score reuses scores-gpt6luna-round1-2026-09-26.json
chatgpt-gpt6luna-plus-round2-2026-09-28.jsonscores-gpt6luna-round1-2026-09-26.jsonBrowse the public evidence archive
Command Code GOATCommand Code$0.00735024/MTok
- Data date
- 2026-09-30 · official source date
- Real price / MTok
- $0.00735024
- Monthly tokens
- 1,360,500,000 (1.361B)
- Quota confidence
- Medium
- Calculation
- $10 / month ÷ 1,360,500,000 tokens × 1,000,000 ≈ $0.00735024 / MTok
- Quota basis
Standard workload: 97% cache reads / 2.5% input / 0.5% output
- Benchmark mapping
- Artificial Analysis Intelligence Index v4.3: Medium (6 configurations)
Terminal-Bench 4.0 (AA): Low (6 configurations)
Code Arena | WebDev Overall: Low (1 configuration)
Agent Arena · Overall (Ranking / Pareto): Low (1 configuration)
Artificial Analysis Coding Agent Index v1.5: Low (1 configuration)
OpenDesign Arena · Quality ranking: Low (1 configuration)Mapping confidence: how directly each score refers to the model served here.
Adoption evidence · source and rationale (translated from the Chinese record)
https://commandcode.ai/docs/plans/goat official per-model allowance and three-part pricing; https://commandcode.ai/pricing; $10->$70 credits; code-subscriptions-round1-2026-09-06.json; goat-opencode-catalogs-round3-2026-09-30.json
Added 1.3605B: min(shared monthly pool $70, model allowance $20) ÷ project standard-workload weighted price; official three-part price; New models table $20 (cache write $0.125); goat-opencode-catalogs-round3-2026-09-30.json. Processing fee ignored; allowances of different models in the same plan are not additive; no usage-panel token+% screenshot — computed from official absolute credits and the price list
https://commandcode.ai/docs/plans/goathttps://commandcode.ai/pricing;code-subscriptions-round1-2026-09-06.jsongoat-opencode-catalogs-round3-2026-09-30.jsonBrowse the public evidence archive
OpenCode GoOpenCode$0.00980008/MTok
- Plan
- OpenCode Go · OpenCode
- Data date
- 2026-09-30 · official source date
- Real price / MTok
- $0.00980008
- Monthly tokens
- 1,020,400,000 (1.02B)
- Quota confidence
- Medium
- Calculation
- $10 / month ÷ 1,020,400,000 tokens × 1,000,000 ≈ $0.00980008 / MTok
- Quota basis
Standard workload: 97% cache reads / 2.5% input / 0.5% output
- Benchmark mapping
- Artificial Analysis Intelligence Index v4.3: Medium (6 configurations)
Terminal-Bench 4.0 (AA): Low (6 configurations)
Code Arena | WebDev Overall: Low (1 configuration)
Agent Arena · Overall (Ranking / Pareto): Low (1 configuration)
Artificial Analysis Coding Agent Index v1.5: Low (1 configuration)
OpenDesign Arena · Quality ranking: Low (1 configuration)Mapping confidence: how directly each score refers to the model served here.
Adoption evidence · source and rationale (translated from the Chinese record)
https://opencode.ai/docs/go/ official per-model Usage and three-part pricing; opencode-go-round5-2026-09-06.json; goat-opencode-catalogs-round3-2026-09-30.json
Added 1.0204B: min(shared monthly pool $60, model Usage $15) ÷ project standard-workload weighted price; new official row; <=272K list price; >272K band kept in research variants; goat-opencode-catalogs-round3-2026-09-30.json. The official request counts serve only as a cross-check and are no longer the primary allowance figure; allowances of different models in the same plan are not additive
https://opencode.ai/docs/go/goat-opencode-catalogs-round3-2026-09-30.jsonopencode-go-round5-2026-09-06.jsonBrowse the public evidence archive
GPT-6 Luna benchmark scores
Highest archived configuration per leaderboard; ranks are among all models each board archives in this dataset. Scores are never mixed across boards.
| Leaderboard | Metric | Score | Rank | Configuration | Effort | Harness | Configs | Snapshot |
|---|---|---|---|---|---|---|---|---|
| Artificial Analysis Intelligence Index v4.3 | Intelligence Index | 37.26 | #31 / 60 | GPT-6 Luna (max) | Max | — | 6 | 2026-10-01 |
| Terminal-Bench 4.0 (AA) | Resolution Rate % | 12.63 | #28 / 53 | Artificial Analysis - GPT-6 Luna (max) | Max | Artificial Analysis | 6 | 2026-10-01 |
| Code Arena | WebDev Overall | Arena Score | 1,582 | #25 / 54 | gpt-6-luna-max | Max | — | 1 | 2026-09-30 |
| Agent Arena · Overall (Ranking / Pareto) | Net Improvement % | 1.68 | #20 / 39 | GPT 6 Luna (Max) | Max | — | 1 | 2026-09-30 |
| Artificial Analysis Coding Agent Index v1.5 | Coding Agent Index | 41.07 | #18 / 19 | Codex - GPT-6 Luna (max) ({'reasoning_effort': 'max'}) | Max | Codex | 1 | 2026-10-01 |
| OpenDesign Arena · Quality ranking | Average task score | 83.1 | #5 / 16 | GPT-6 Luna | — | OpenDesign | 1 | 2026-10-01 |
Every archived configuration
Artificial Analysis Intelligence Index v4.3 6 configurations
| Configuration | Score | Score interval | Harness / effort / mode | Source task cost (mean / median) | Checked | Links |
|---|---|---|---|---|---|---|
| GPT-6 Luna (max) | 37.256 | — | — / Max / — | — / — | 2026-10-01 | Source · Archive |
| GPT-6 Luna (xhigh) | 33.8846 | — | — / xhigh / — | — / — | 2026-10-01 | Source · Archive |
| GPT-6 Luna (high) | 32.1482 | — | — / High / — | — / — | 2026-10-01 | Source · Archive |
| GPT-6 Luna (medium) | 29.462 | — | — / Medium / — | — / — | 2026-10-01 | Source · Archive |
| GPT-6 Luna (low) | 20.9225 | — | — / Low / — | — / — | 2026-10-01 | Source · Archive |
| GPT-6 Luna (non-reasoning) | 18.2617 | — | — / — / — | — / — | 2026-10-01 | Source · Archive |
Terminal-Bench 4.0 (AA) 6 configurations
| Configuration | Score | Score interval | Harness / effort / mode | Source task cost (mean / median) | Checked | Links |
|---|---|---|---|---|---|---|
| Artificial Analysis - GPT-6 Luna (max)AA's own-harness independent measurement of Terminal-Bench 4.0 (the terminalBench40 field in the models-board payload, fraction×100); scored separately from the official tbench.ai board. | 12.6263 | — | Artificial Analysis / Max / — | — / — | 2026-10-01 | Source · Archive |
| Artificial Analysis - GPT-6 Luna (xhigh)AA's own-harness independent measurement of Terminal-Bench 4.0 (the terminalBench40 field in the models-board payload, fraction×100); scored separately from the official tbench.ai board. | 8.0808 | — | Artificial Analysis / xhigh / — | — / — | 2026-10-01 | Source · Archive |
| Artificial Analysis - GPT-6 Luna (high)AA's own-harness independent measurement of Terminal-Bench 4.0 (the terminalBench40 field in the models-board payload, fraction×100); scored separately from the official tbench.ai board. | 4.5455 | — | Artificial Analysis / High / — | — / — | 2026-10-01 | Source · Archive |
| Artificial Analysis - GPT-6 Luna (medium)AA's own-harness independent measurement of Terminal-Bench 4.0 (the terminalBench40 field in the models-board payload, fraction×100); scored separately from the official tbench.ai board. | 2.5253 | — | Artificial Analysis / Medium / — | — / — | 2026-10-01 | Source · Archive |
| Artificial Analysis - GPT-6 Luna (non-reasoning)AA's own-harness independent measurement of Terminal-Bench 4.0 (the terminalBench40 field in the models-board payload, fraction×100); scored separately from the official tbench.ai board. | 1.5152 | — | Artificial Analysis / — / — | — / — | 2026-10-01 | Source · Archive |
| Artificial Analysis - GPT-6 Luna (low)AA's own-harness independent measurement of Terminal-Bench 4.0 (the terminalBench40 field in the models-board payload, fraction×100); scored separately from the official tbench.ai board. | 0 | — | Artificial Analysis / Low / — | — / — | 2026-10-01 | Source · Archive |
Code Arena | WebDev Overall 1 configuration
Agent Arena · Overall (Ranking / Pareto) 1 configuration
Artificial Analysis Coding Agent Index v1.5 1 configuration
| Configuration | Score | Score interval | Harness / effort / mode | Source task cost (mean / median) | Checked | Links |
|---|---|---|---|---|---|---|
| Codex - GPT-6 Luna (max)Codex - GPT-6 Luna (max) ({'reasoning_effort': 'max'})Score belongs to this named agent/harness × model configuration; it is not a harness-independent model measurement. indexScore stored as percent (raw fraction × 100). | 41.0744 | — | Codex / Max / — | $0.1759 / — | 2026-10-01 | Source · Archive |
OpenDesign Arena · Quality ranking 1 configuration
These are reference mappings, not benchmarks of a subscription or API channel. Harness and quota-measurement effort alignment are unverified. Source task costs are not subscription task costs.
- Exact served-model reference; quota-measurement effort is unverified.
- Exact served-model reference only; product harness and quota-measurement effort are unverified. Not a benchmark measurement of this subscription or API channel.
Frequently asked questions
How much does GPT-6 Luna really cost per million tokens?
Measured as monthly fee ÷ tokens you can actually use, GPT-6 Luna ranges from $0.00198 (Droid Max) to $0.0098 (OpenCode Go) per million tokens.
Which subscription gives the most GPT-6 Luna usage?
Droid Max allows roughly 101.04B tokens of GPT-6 Luna per month for $200.
How does GPT-6 Luna score on independent leaderboards?
GPT-6 Luna has scores on 6 leaderboards; its strongest placement is 83.1 (Average task score) on OpenDesign Arena · Quality ranking, #5 of 16 models tracked.
Who makes GPT-6 Luna, and where can I use it?
GPT-6 Luna (model ID gpt-6-luna) is made by OpenAI and is available through Command Code, Factory, OpenAI, OpenCode.
More OpenAI models
| Model | Lowest real $/MTok | Cheapest plan | Plans |
|---|---|---|---|
| GPT 5.6 Luna | $0.00142 | ChatGPT Pro 20x | 7 |
| GPT 5.6 Terra | $0.00801 | ChatGPT Pro 20x | 5 |
| GPT 5.6 Sol | $0.0155 | ChatGPT Pro 20x | 6 |
| GPT-6 Sol | $0.01818 | ChatGPT Plus | 2 |
| GPT 5.5 | $0.02004 | ChatGPT Pro 20x | 3 |
| GPT-6.1 Sol | $0.02299 | ChatGPT Plus | 1 |
| GPT-6 Astra | $0.05247 | ChatGPT Pro 20x | 5 |