Cost-efficient GPT-5.6 model for fast, high-volume workloads
GPT-5.6 Luna by openai is offered by 41 providers on this page. Public prices are shown for 39 of them. Its official list price is $0.20 per 1M input tokens and $1.20 per 1M output tokens. The lowest paid channel is Bothub at $0.06 / $0.37 per 1M, about 17× below the list price. That channel is a third-party gateway, so confirm its availability and rate limits before depending on it. It also has 1 free ($0) channel; free tiers usually carry rate limits, and subscription-covered access bills $0 per token only after the subscription fee.
Artificial Analysis rates it 37.3 on the Intelligence Index, with 71.4 for coding and 42.1 for agentic tasks. Against its lowest blended price of $0.138 per 1M, that is roughly 271 index points per dollar, which is the value ratio the leaderboards rank on. Median output speed is 128 tokens per second, with 101.99s to the first token. Running one task of the Artificial Analysis suite costs about $0.1783, which reflects how many tokens its reasoning consumes rather than the unit price alone.
The context window is 1,050,000 tokens at the reference host, but hosts report different limits, from 400,000 to 1,050,000, so the usable window depends on the provider you pick. It supports reasoning, tool use, and structured output. Accepted input modalities are Text, Image, and PDF. Its training knowledge cuts off at 2026-02-16.
Quality is independently evaluated by Artificial Analysis. Speed/latency are model-level medians.
| Provider | Tier | Input | Output | Cache read | Cache write | Context | Output limit | Status |
|---|---|---|---|---|---|---|---|---|
| Kenari gpt-5-6-luna | Gateway | Free | — | — | 1,050,000 | 128,000 | ||
| Bothub | Gateway | $0.06 | $0.37 | — | — | 1,050,000 | 128,000 | |
| NanoGPT | Gateway | $0.20 | $1.20 | $0.02 | $0.25 | 1,050,000 | 128,000 | |
| Kilo Gateway openai/gpt-5.6-luna-pro | Gateway | $0.20 | $1.20 | $0.02 | $0.25 | 1,050,000 | 128,000 | |
| Kilo Gateway | Gateway | $0.20 | $1.20 | $0.02 | $0.25 | 1,050,000 | 128,000 | |
| 302.AI | Gateway | $0.20 | $1.20 | — | — | 1,050,000 | 128,000 | |
| OpenRouter | Gateway | $0.20 tiered >272K: $0.40 | $1.20 | $0.02 | $0.25 | 1,050,000 | 128,000 | |
| Azure | Cloud | $0.20 tiered >272K: $0.40 | $1.20 | $0.02 | $0.25 | 1,050,000 | 128,000 | |
| Impossibl | Gateway | $0.20 tiered >272K: $0.40 | $1.20 | $0.02 | $0.25 | 1,050,000 | 128,000 | |
| Cloudflare AI Gateway | Cloud | $0.20 | $1.20 | $0.02 | $0.25 | 1,050,000 | 128,000 | |
| OpenAIOfficial | First-party | $0.20 tiered >272K: $0.40 | $1.20 | $0.02 | $0.25 | 1,050,000 | 128,000 | |
Sorted by blended price (input×0.75 + output×0.25) asc. The official channel always shows regardless of rank. Whether a gateway's low price is actually usable can't be verified.
Interleaved thinking (reasoning between tool calls) is declared by 4 of 41 providers.
2 / 41 providers expose no reasoning control (reasoning_options: []).
| Mode | Provider | Input | Output | Cache read | Cache write |
|---|---|---|---|---|---|
| fast | OpenAI | $0.40 | $2.40 | $0.04 | $0.50 |
| pro | OpenAI | — | — | — | — |
1 more channels offer $0 (Kenari); free tiers usually have rate limits and no SLA, excluded from ranking.
| Name | Conditions | Score | Metric | Source |
|---|---|---|---|---|
| SWE-Bench Pro | — | 62.7 | resolve rate | Source ↗ |
| Terminal-Bench | v2.1 | 84.7 | success rate | Source ↗ |
| DeepSWE | v1.1 | 67.2 | resolve rate | Source ↗ |
| GPQA Diamond | — | 92.3 | accuracy | Source ↗ |
| FrontierMath | dataset: Tier 1-3 · vv2 | 78.6 | accuracy | Source ↗ |
| BrowseComp | — | 83.3 | accuracy | Source ↗ |
| OSWorld | v2.0 | 45.6 | success rate | Source ↗ |
| MMMU Pro | variant: no tools | 78.4 | accuracy | Source ↗ |
| Agents' Last Exam | — | 50.3 | — | Source ↗ |
| Toolathlon | — | 53.4 | success rate | Source ↗ |
| Artificial Analysis Intelligence Index | variant: max · v4.1 | 51.2 | index score | Source ↗ |
| Artificial Analysis Coding Agent Index | harness: Codex · variant: max · v1.1 | 74.6 | index score | Source ↗ |
The same benchmark scores very differently across harness / dataset, so the qualifying conditions must be shown together.
Individual evaluations run by Artificial Analysis, on the same reasoning tier as the intelligence score above. Each benchmark has its own task set and harness, so rows are not comparable with one another. The Intelligence Index above draws on a different, newer set of evaluations.