GPT-5.6 Luna cost & VRAM calculator
What does GPT-5.6 Luna cost to run for your workload, and can you run it on your own hardware? Set your workload below - we compute per-provider API cost live and tell you honestly whether local hardware can run it.
This is where owning stops making sense
GPT-5.6 Luna is a large-billion-parameter model. No rig an individual can buy runs it - so unlike a workstation GPU model, there's no break-even to compute. The honest answer for nearly everyone is the per-provider API cost below.
Efficient proprietary dense model from OpenAI, the cost-optimized sibling of GPT-5.6 Sol. Same 1,050,000-token context and 128K max output as Sol. Text + image in, text out. Reasoning effort spans none through max; medium is default. Pricing: $0.20/1M input, $1.20/1M output. Agents on Rails benchmark (Aug 2026, Le Mans round). 73.0% accuracy on 63 runs at default effort for only $0.014 mean cost - the cheapest run in the 16-model field by a wide margin. Scaling reasoning effort lifted it to 86% at $1.36 (high) and 89% at $2.34 (xhigh) in the earlier 8-model round. The headline finding still holds: “a dollar gets you most of the way.”
Your workload
API cost for your workload
| Provider | Rate ($/1M) | Monthly cost |
|---|---|---|
| OpenAI may be stale | $0.20 in · $1.20 out | $0.0 cheapest |
| DigitalOcean may be stale | $0.20 in · $1.20 out · $0.02 cache | $0.0 cheapest |
Monthly cost is an estimate from list prices and your workload - verify against the provider before committing. Cached fraction applies the cache rate to that share of input.
Can you run it locally?
GPT-5.6 Luna has no published quantization that fits a rig one person can buy, so there's no local-hardware recommendation and no break-even to compute. The honest answer is the per-provider API cost above.
Efficient proprietary dense model from OpenAI, the cost-optimized sibling of GPT-5.6 Sol. Same 1,050,000-token context and 128K max output as Sol. Text + image in, text out. Reasoning effort spans none through max; medium is default. Pricing: $0.20/1M input, $1.20/1M output. Agents on Rails benchmark (Aug 2026, Le Mans round). 73.0% accuracy on 63 runs at default effort for only $0.014 mean cost - the cheapest run in the 16-model field by a wide margin. Scaling reasoning effort lifted it to 86% at $1.36 (high) and 89% at $2.34 (xhigh) in the earlier 8-model round. The headline finding still holds: “a dollar gets you most of the way.”
See the model card for the full architecture notes and any cloud subscription plans.