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    GPT Image 2.5 Flare cut median generation time by 63% versus GPT Image 2 in preliminary tests, ArtificialAnlys reports

    ArtificialAnlys says the new GPT Image 2.5 models improve image quality while retaining GPT Image 2's pricing: $30 per million image output tokens.

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    1 Source, 19d ago, first seen 19d ago

    TLDR

    ArtificialAnlys reports that GPT Image 2.5 Flare (max) reduced median generation time from 142.9 to 52.7 seconds versus GPT Image 2 (high)—a 63% reduction in its preliminary results. It reports a 24% reduction for Sunburst (max), to 108.9 seconds. The tests used 1024 × 1024 PNG outputs across several runs and included both inference and image transfer. The benchmarker also says both new models improve image quality at the same image output token rate as GPT Image 2.

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    1 Source, first seen 19d ago

    Combined views

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    1 Source, first seen 19d ago

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    1 Source

    @ArtificialAnlysFlare pairs its quality gains with a much shorter wait for images and edits, with GPT Image 2.5 Flare (max) having a ~63% faster latency than GPT Image 2 (high) In our independent inference speed benchmarking, our preliminary results show similar improvements to OpenAI's claim of 50% lower latency for Flare versus GPT Image 2: ➤ Flare (max) reduced median generation time by 63%, from 142.9 to 52.7 seconds ➤ Sunburst (max) reduced median generation time by 24% to 108.9s Our testing used 1024 × 1024 PNG outputs and represents a median across several testing runs including inference and image transfer.

    1 Source

    @ArtificialAnlysFlare pairs its quality gains with a much shorter wait for images and edits, with GPT Image 2.5 Flare (max) having a ~63% faster latency than GPT Image 2 (high) In our independent inference speed benchmarking, our preliminary results show similar improvements to OpenAI's claim of 50% lower latency for Flare versus GPT Image 2: ➤ Flare (max) reduced median generation time by 63%, from 142.9 to 52.7 seconds ➤ Sunburst (max) reduced median generation time by 24% to 108.9s Our testing used 1024 × 1024 PNG outputs and represents a median across several testing runs including inference and image transfer.