GPT-5.6 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
No overlapping benchmark runs recorded yet for this metric.
Different class (2s+ vs 500ms–2s)
Different class (50–100 tok/s vs <50 tok/s)
$1.2 / 1M tokens
CompareLLM category Elo ratings across every applicable domain list at default effort.
Ratings represent CompareLLM category rankings derived from disclosed evidence recipes.
Methodology & Recipe →Simulate monthly API production cost in USD (US Dollar).
GPT-5.6 Luna saves approx. $33.50/mo ($402/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.
Context separates them most, favouring GPT-5.6 Luna. Partial fit: GPT-5.6 Luna is scored without coding, reasoning; Qwen3.6 27B is scored without coding, reasoning.
Raw benchmark → percentile among comparable active models → workload weight → weighted contribution.
Raw: No canonical data
Raw: No canonical data
Raw: Out $/1M tok $1.2/1M tok
Raw: Speed (tok/s) 55 tok/s · TTFT 2,215 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
