GPT-6 Luna and Qwen3.6 27B carry source-labelled ratings with evidence and any approved estimates in their category breakdowns. Models may use different benchmarks and test settings. This is an indicative composite, not a controlled head-to-head comparison or community Elo. Admin-approved sentiment estimates fill categories without accepted benchmark results. Estimates are labelled and do not increase benchmark coverage.
5 direct measurements, category ratings, capability breakdown, and verified sources
Models may use different benchmarks and test settings. This is an indicative composite, not a controlled head-to-head comparison or community Elo. Admin-approved sentiment estimates fill categories without accepted benchmark results. Estimates are labelled and do not increase benchmark coverage.
Different class (2s+ vs 500ms–2s)
Different class (50–100 tok/s vs <50 tok/s)
$0.5 / 1M tokens
Side-by-side comparison across all domain lists these models share, evaluated under standard configurations.
Ratings represent CompareLLM category rankings derived from disclosed evidence recipes.
Methodology & Recipe →Simulate monthly API production cost in USD (US Dollar).
GPT-6 Luna saves approx. $47.50/mo ($570/yr).
Full Technical Dossiers & Specs
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Published results are normalized using declared scales and weighted by category benchmark family. The breakdown lists the actual inputs, sources and weights. Models may use different benchmarks and test settings. This is an indicative composite, not a controlled head-to-head comparison or community Elo.
Coding separates them most, favouring GPT-6 Luna.
Raw benchmark → percentile among comparable active models → workload weight → weighted contribution.
Raw: No canonical data
Raw: No canonical data
Raw: Out $/1M tok $0.5/1M tok
Raw: Speed (tok/s) 62 tok/s · TTFT 2,294 ms
Raw: Context (tokens) 1.1M tokens
Raw: No canonical data
Raw: No canonical data
Raw: Out $/1M tok $3.2/1M tok
Raw: Speed (tok/s) 21 tok/s · TTFT 1,973 ms
Raw: Context (tokens) 262k tokens
