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    Celeris Unveils Celeris-1 Magnus Hybrid Model

    Celeris claims the model outperforms GPT-5.6-sol on an agentic banking benchmark.

    CE
    1 Source, 30d ago, first seen 30d ago

    TLDR

    Celeris posted an announcement introducing Celeris-1 Magnus as a hybrid diffusion model derived from qwen3.8-27b. The company stated the model targets agentic workloads. Its post reported that Magnus reached 41.2 percent at a 55-second median on the τ³-bench banking task. The same message listed GPT-5.6-sol at 38.1 percent with a 79-second median. Celeris positioned these outcomes as evidence of faster performance suited to agentic work. The announcement included a link to its site for further details. No other posts in the packet provide independent confirmation of the results.

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    1 Source, first seen 30d ago

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    1 Source, first seen 30d ago

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    80 comments
    544 saves
    93 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    1 Source

    @Celeris_aiIntroducing Celeris-1 Magnus. A model built for agentic work. On τ³-bench banking, Magnus delivers 41.2% at a 55-second median, against GPT-5.6-sol's 38.1% at 79 seconds. It’s a hybrid diffusion model derived from qwen3.8-27b, optimized for agentic workloads. For more info, check out: http://celeris.ai/celeris-1-magnus

    1 Source

    @Celeris_aiIntroducing Celeris-1 Magnus. A model built for agentic work. On τ³-bench banking, Magnus delivers 41.2% at a 55-second median, against GPT-5.6-sol's 38.1% at 79 seconds. It’s a hybrid diffusion model derived from qwen3.8-27b, optimized for agentic workloads. For more info, check out: http://celeris.ai/celeris-1-magnus