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OpenAI's fast, cost-efficient GPT-5.6 tier for high-volume chat, classification, and lightweight agentic tasks with a 1.05M-token context window.
Key strengths. Third-party Artificial Analysis benchmarking puts Luna's coding index at 71.4, its strongest measured dimension, with an intelligence index of 52.3 that's the lowest of the three GPT-5.6 tiers — roughly level with GPT-5.4 and well below the family's reasoning-focused Sol variant. It supports text-and-image input with a large 1.05M-token context window and 128K max output, and it offers automatic prompt caching, useful for repeated-context workloads at scale.
When to use it. Use Luna for high-volume, latency-sensitive workloads — chat interfaces, classification pipelines, and lightweight agentic tasks — where a fast response profile matters more than deep reasoning chains. Its straightforward complexity fit and solid coding index make it a reasonable choice for routine code assistance and general text tasks, but for multi-step reasoning or the family's top intelligence scores, Sol is the better fit within the same family.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €0.18 / 1.08+7 rates | Online |
15 use cases classified by the curator. The note says why.
Curated description names classification as a core built-for use case, and the low-cost tier suits high-volume categorization workloads.
Explicitly positioned as OpenAI's fastest, lowest-cost GPT-5.6 tier for high-volume chat — ideal cost/latency profile for support chatbot deployments at scale.
Same low-latency, low-cost profile that fits classification extends to tagging at scale across large document sets.
Built for high-volume, lightweight decision tasks — well suited to fast triage and routing pipelines where latency and cost matter more than peak reasoning depth.
Vision capability plus large context window supports OCR-style document tasks, though not marketed as a top-tier vision reasoning model.
1.05M-token context window allows ingesting very long documents, but as the fastest/lowest-cost tier it trades off some depth versus higher GPT-5 variants.
Low-cost, high-throughput profile suits large-scale entity extraction tasks over the model's 1.05M-token context window.
Positioned for lightweight agentic tasks — adequate for straightforward function-calling apps where speed and cost outweigh the need for deep reasoning.
Vision capability is present, and the low-cost tier is well suited to high-volume image Q&A workloads rather than complex visual reasoning.
Vision-enabled and optimized for low-cost, high-volume tasks — a reasonable fit for lightweight screenshot interpretation workflows.
Positioned for lightweight agentic tasks, not complex coding agents; family's higher-tier variants (e.g. gpt-5.4 at 71.1 coding index) are better fits for agentic coding.
Extended-thinking is present, but the model is explicitly the fastest/lowest-cost tier built for lightweight tasks, not deep multi-step planning — heavier GPT-5 variants are better suited.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-5.6-luna",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at