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Saturday

  1. Story4 reports · 1 facts

    Block releases full-duplex speech model Dessa

    Block's internal AI research lab released the full-duplex speech-to-speech model Dessa on October 23. According to Unite.AI, the model was trained entirely from scratch on human conversational speech, can listen and speak simultaneously, and natively perceives emotion, sarcasm, and laughter; its inference is faster than real time and it can run locally on a MacBook Pro M4 Max or higher configuration, with the team completing training on a single NVIDIA GB200 NVL72. Block has released demo recordings and a public waitlist, and has opened early access to a small number of AI safety researchers. Other reports note that the model does not rely on a text intermediary and supports full-duplex conversation with the ability to interrupt and interject at any time, and that training consumes hundreds of times fewer tokens than large language models, but the author points out that this is currently only a small-scale research preview from Block's internal lab, the demos were selected by the company, and questions remain about how pure audio models perform on complex multi-step logical reasoning.

    sarcasm, and laughter; its inference is faster than real time and it can run locally on a MacBook Pro M4 Max or higher configuration, with the team completing training on a single NVIDIA GB200 NVL72. Block has released demo recordings and a public waitlist, and has opened early access to a small number of AI safety researchers. Other reports note that the model does not rely on a text intermediary and supports full-duplex conversation with the ability to interrupt and interject at any time, and

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Sunday

  1. Story1 reports · 1 facts

    Inception releases diffusion language model Mercury 3

    On October 21, 2026, Inception released the diffusion language model Mercury 3. According to a report by The Decoder the same day, Inception claims the model won 8 of 12 benchmarks and runs four to eight times faster than GPT-6 Luna and Claude Haiku 5.5. In Inception's own testing, Mercury 3 scored 76.84 on IFBench, 15 points higher than GPT-6 Luna; it scored 51.08 on SWE-Bench Atlas, while its competitors scored 33.33 and 32.26 respectively. Claude Haiku 5.5 led on the DeepSWE long-code test and long-text retrieval. The model generates 1,068 tokens per second on Nvidia GPUs, is priced at $0.20 per million input tokens and $0.70 per million output tokens, is available at 50% off during the launch period, and is now live on the Inception platform as well as OpenRouter, Baseten, and Vercel. The report notes that there are currently no independent tests.

    1.08 on SWE-Bench Atlas, while its competitors scored 33.33 and 32.26 respectively. Claude Haiku 5.5 led on the DeepSWE long-code test and long-text retrieval. The model generates 1,068 tokens per second on Nvidia GPUs, is priced at $0.20 per million input tokens and $0.70 per million output tokens, is available at 50% off during the launch period, and is now live on the Inception platform as well as OpenRouter, Baseten, and Vercel. The report notes that there are currently no independent tests.

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Friday

  1. Story2 reports · 1 facts

    Inception releases flagship diffusion language model Mercury 3

    Inception AI Inc. releases its flagship diffusion language model Mercury 3. Information published by OpenRouter on October 20 shows that Mercury 3 is now available on the platform, with a generation speed exceeding 1000 tok/s, a context window of 1M, and a speed 4 times that of Mercury 2.5; Inception claims that it surpasses GPT-6 Luna and Haiku 5.5 on IFBench, GPQA Diamond, and SWE-Bench Atlas, and during its launch period the pricing is $0.10/M tokens for input and $0.35/M tokens for output, equivalent to a 50% discount. SiliconANGLE reported on October 21 that Inception outperforms GPT-6 Luna and Claude Haiku 5.5 on 8 of its own 12 benchmarks, with speeds 4 to 8 times faster; the company says it reaches 1068 tokens per second on NVIDIA GPUs, compared with about 240 for Haiku 5.5 and about 130 for GPT-6 Luna; the context window has been increased to 1 million tokens, the parameter count was not disclosed, with only a claim that it exceeds Mercury 1 by more than 40 times, and pricing is 2 per million input tokens

    SiliconANGLE reported on October 21 that Inception outperforms GPT-6 Luna and Claude Haiku 5.5 on 8 of its own 12 benchmarks, with speeds 4 to 8 times faster; the company says it reaches 1068 tokens per second on NVIDIA GPUs, compared with about 240 for Haiku 5.5 and about 130 for GPT-6 Luna; the context window has been increased to 1 million tokens, the parameter count was not disclosed, with only a claim that it exceeds Mercury 1 by more than 40 times, and pricing is 2 per million input tokens

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Wednesday

Friday

  1. Story5 reports · 1 facts

    SGLang natively supports and powers the Beam production API

    On October 16, 2026, LMSYS announced that SGLang now natively supports Reflection's Beam model and is being used to power its production API. Beam is a 501B-parameter MoE model with 23B active parameters per token and a native 1M-token context window, planned for release under Apache 2.0; SGLang provides BF16, NVFP4, and dynamic FP8 support for it, along with DFlash2 speculative decoding and hybrid attention prefix caching. Running OpenHands multi-turn agentic coding replay with NVFP4 on 4 NVIDIA GB300 GPUs, it reached 127.6K tokens/sec per GPU and 185.87 tokens/sec per user (p50) at a concurrency of 32. On the same day, Reflection released the Beam model weights, with Ollama offering them free for a limited time as a day-zero partner with zero data retention; Beam has approximately 501 billion total parameters, about 23 billion active parameters per token, a 1 million-token context window, and is released under the Apache 2.0 license. Subsequent information from AWS states that the Beam weights have been released on Hugging Face, offering three checkpoints—BF16, FP8, and NVFP4—and are now available on Amazon SageMaker JumpStart, and can also be accessed via SageMa

    P4, and dynamic FP8 support for it, along with DFlash2 speculative decoding and hybrid attention prefix caching. Running OpenHands multi-turn agentic coding replay with NVFP4 on 4 NVIDIA GB300 GPUs, it reached 127.6K tokens/sec per GPU and 185.87 tokens/sec per user (p50) at a concurrency of 32. On the same day, Reflection released the Beam model weights, with Ollama offering them free for a limited time as a day-zero partner with zero data retention; Beam has approximately 501 billion total par

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  2. Story4 reports · 2 facts

    Artificial Analysis publishes evaluation results for Reflection's first model, Beam

    On October 16, 2026, Artificial Analysis published the evaluation results for Beam, the first model from Reflection: Beam scored 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). The model is an open-weight model with 501B total parameters and 23B activated parameters, consuming about 26k output tokens per intelligence index task, of which about 19k are used for reasoning and 7k for answering; it scored 48.5% on AutomationBench-AA and only 3.0% on Terminal-Bench 4.0; AA-Omniscience accuracy was 33% with a hallucination rate of 47%. On October 17, Arena.ai announced that Beam-1.0-501B-A23B entered Agent Arena, ranking 12th among open-weight models with a Net Improvement of -3.67% and a margin of error of ±3.84%; on the Code Arena: WebDev open-source leaderboard it ranked 22nd with a score of 1438.

    del from Reflection: Beam scored 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). The model is an open-weight model with 501B total parameters and 23B activated parameters, consuming about 26k output tokens per intelligence index task, of which about 19k are used for reasoning and 7k for answering; it scored 48.5% on AutomationBench-AA and only 3.0% on Terminal-B

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Thursday

Wednesday

  1. Story2 reports · 1 facts

    Liquid AI Releases LFM2.5-24B-A2B On-Device Reasoning Model

    On October 13, 2026, Liquid AI released the LFM2.5-24B-A2B on-device agent model. With 24B total parameters and only 2B activated per token, it is the largest and final model in the LFM2.5 family, and both the base and post-trained versions have open weights on Hugging Face and Playground. According to the official blog, it uses a pure reasoning mode with explicit chain-of-thought, expands the context window from 32,768 to 131,072 tokens, grows the vocabulary from 65K to 128K, and scales pretraining data from 17T to 33T tokens; it leads or ties Qwen3.6-35B-A3B on five of eight agent and tool-calling benchmarks, scoring 27.69 versus 8.72 on AutomationBench and 48.09 versus 38.19 on LiveClaw; it decodes at 184 tokens/s on an Apple M5 Max and outputs over 21K tokens/s under high concurrency on a single NVIDIA B300 GPU. On October 14, OpenRouter announced that the model is now available, saying it reasons before answering and targets agent and tool-calling scenarios.

    28K, and scales pretraining data from 17T to 33T tokens; it leads or ties Qwen3.6-35B-A3B on five of eight agent and tool-calling benchmarks, scoring 27.69 versus 8.72 on AutomationBench and 48.09 versus 38.19 on LiveClaw; it decodes at 184 tokens/s on an Apple M5 Max and outputs over 21K tokens/s under high concurrency on a single NVIDIA B300 GPU. On October 14, OpenRouter announced that the model is now available, saying it reasons before answering and targets agent and tool-calling scenarios.

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Sunday

Monday