GPT-6 Luna Decisions Alternatives: Hosted and Self-Hosted Options
GPT-6 Luna Decisions alternatives: when OpenAI's decision API fits, and when to look at other hosted decision models or open-weights options like Helm.
OpenAI shipped GPT-6 Luna Decisions on 2026-10-06, and it's a good product: GPT-6 Luna served through a Decisions API that returns probabilities for yes/no, choice and rubric-score questions, many per request, with text, JSON or image state. It's a hosted decision model: you ask typed questions and get per-option probabilities back instead of free text. If you're on OpenAI and need those probabilities, it may be all you need. OpenAI hadn't published a separate Decisions API price when we checked on 2026-10-10, so get pricing and limits from OpenAI's own pages.
This guide covers GPT-6 Luna Decisions alternatives for the cases where your constraints rule it out. Here is when to look elsewhere, and what is actually there.
When Luna Decisions fits
- You're already on OpenAI; a second credential and vendor review is more overhead than it's worth.
- You want image input or JSON state in the same call.
- You want hosted latency without running anything.
- Your data policy permits sending this content to a provider.
When to look elsewhere
- Data must stay in-house. Regulated data, customer-committed confidentiality, or policy. A hosted API, however well-run, is still egress.
- Offline or air-gapped operation. Field sites, ships, factories, locked-down networks.
- Native multi-label. Luna answers multi-select as one yes/no question per tag; it works, but if tagging is your core loop, a model with a native multi-choice type returns independent memberships in one pass.
- Flat cost at high volume. Per-token input billing is cheap until it isn't; at very high decision volumes a fixed self-hosting cost can win. Do your own math with your volumes.
- Version control. You pin the weights, so you choose when the version changes.
- Workflow-native integration. You want a node whose outputs are already "accepted" and "fallback", not an HTTP call you wrap.
GPT-6 Luna Decisions alternatives: what is actually there
Other hosted decision APIs
The category has multiple hosted entrants since September 2026 (TypeSafe's System One models started it; others followed). They differ in price, question types, context limits and regional hosting. Hosted options worth evaluating alongside Luna Decisions:
- OpenRouter's Decisions API, which serves decision models from several providers behind one endpoint.
- TypeSafe's System One and Jev models, the first hosted decision models in the category.
- Saina's hosted API (
api.saina.run), the same server as the self-hosted Helm, with a zero-retention policy; keys from the API page.
Check current pricing and specs on each provider's own pages before you compare; this market reprices often.
Open-weights decision models you host
Saina Helm is one example of this pattern: a 0.8B open-weights decision model that runs entirely inside your network with no external calls.
- Single-choice, multi-label, yes/no and rating questions, 1–256 per
/v1/askrequest, in one call. - Decision mode: confidence
threshold(default 0.8) andmin_margin, set per request or per question; every answer carries a reason (accepted,below_threshold,below_margin,tie). - Runs on CPU (~4 GB RAM; measured 0.9–2.7 s per request, local Docker test, 2026-10-06). Also available on Ollama (≥0.40) and as MLX for Apple silicon; Ollama serves the System One format, so multi-label requires the
/v1/askserver. - n8n community node with Selected/Fallback outputs on self-hosted n8n; HTTP Request template for n8n Cloud.
- Weights are pinned to a Hub revision (v1.0); you decide when to move.
Other open-weights decision models exist on the Hub (the category tag has grown fast since September); evaluate them the same way: question types, licensing, whether you can pin the version, and accuracy on your labeled set, which is the only accuracy number that counts.
Comparison table (checked 2026-10-10)
| GPT-6 Luna Decisions | Other hosted decision APIs | Saina Helm (self-hosted) | |
|---|---|---|---|
| Data location | OpenAI | Provider varies | Your infrastructure |
| Price model | Per-token billing; see OpenAI's pricing page | Varies | Fixed hosting cost |
| Question types | yes/no, choice, score | Varies | yes/no, single-, multi-choice, rating |
| Multi-label | Yes/no per tag | Varies; check | Native |
| Max questions/call | See OpenAI's docs | Varies | 256 |
| Latency | Hosted; see OpenAI's docs | Varies | 0.9–2.7 s CPU (measured, local Docker test) |
| Version pinning | Provider-managed | Provider-managed | You pin the revision |
| Offline | No | No | Yes |
| Setup effort | Lowest | Low | You run a server |
How to choose without marketing
- Write down your constraint first: data location, volume, latency floor, integration surface. The constraint usually picks the column.
- Build a 50–100 item labeled set from real traffic.
- Run the candidates that survive step 1 in shadow mode; measure agreement with your current routing, not with each other's marketing.
- Compare cost at your volume using current prices from official pages (note the date); include review time and hosting.
- Pick, pin the version, and schedule test-set re-runs.