Claude Sonnet 5 and DeepSeek V4.1 Flash 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 tok/s vs 100–200 tok/s)
$0.6 / 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).
DeepSeek V4.1 Flash saves approx. $205.75/mo ($2,469/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.
Reasoning separates them most, favouring Claude Sonnet 5.
Raw benchmark → percentile among comparable active models → workload weight → weighted contribution.
Raw: No canonical data
Raw: No canonical data
Raw: Out $/1M tok $10/1M tok
Raw: Speed (tok/s) 49 tok/s · TTFT 2,193 ms
Raw: Context (tokens) 1M tokens
Raw: No canonical data
Raw: No canonical data
Raw: Out $/1M tok $0.6/1M tok
Raw: Speed (tok/s) 142 tok/s · TTFT 1,000 ms
Raw: Context (tokens) 1M tokens
