Intelligence
52.3
52
General reasoning and benchmark headroom.
SituationalGPT-5.6 Luna (max) is a budget-priced text-first model from OpenAI with heavy runtime profile, partial context coverage, and the clearest fit around agent workflows / coding.
Benchmark blend
Dev workflow signal
N/A
Budget tier
GPT-5.6 Luna (max) currently reads as a budget text-first option with partially published context and a heavy runtime profile.
Decision Strip
Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.
General reasoning and benchmark headroom.
SituationalTTFT 65.59s
SituationalHow much prompt and task state can stay in view.
Unavailable$1.20 output / 1M
EfficientEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
The GPT-5.6 Luna (max) AI model by OpenAI.
OpenAI text-first profile
Agent workflows / Coding with partially published context and heavy runtime.
Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.
Coding indicators point to a strong developer workflow fit.
Budget-friendly input pricing is a strength, but raw capability may vary by workload.
Latency profile is better for deliberate runs than rapid back-and-forth chat.
Current metadata points to a text-first profile rather than a broad multimodal one.
Context limits are only partially published, so long-session planning needs extra validation.
Code generation, refactors, test writing, and tool-assisted debugging.
Benchmarks
Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.
Broad reasoning, knowledge depth, and flagship benchmark posture.
Software implementation, debugging quality, and coding benchmark signal.
Long-horizon execution quality and interactive benchmark evidence.
Extra benchmark cuts are not available for this category yet.
Specs & Pricing
Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.
This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.
Metadata
Verification details remain available, but the page no longer forces them ahead of the editorial read.