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Thursday

  1. SiliconANGLESelected

    Mistral Opens the Weights of Mistral Large 4, a 1-Trillion-Parameter Natively Multimodal Model

    Mistral AI announced that it is opening the weights of its frontier model Mistral Large 4 (codename Le Chonk), which has 1 trillion parameters with 52 billion active parameters and native multimodal support. ML4 was trained in a European data center on 3,800 Nvidia Grace Blackwell GPUs, improving from 28% to 41% on Terminal-Bench 4.0, tying GLM-5.3 for the best coding-agent score while using an average of 47% fewer tokens per task. Open weights mean enterprises can deploy it on their own hardware and behind their firewalls, and the model offers three reasoning modes: low, medium, and high.

    Mistral AI announced that it is opening the weights of its frontier model Mistral Large 4 (codename Le Chonk), which has 1 trillion parameters with 52 billion active parameters and native multimodal support. ML4 was trained in a European data center on 3,800 Nvidia Grace Blackwell GPUs, improving from 28% to 41% on Terminal-Bench 4.0, tying GLM-5.3 for the best coding-agent score while using an average of 47% fewer tokens per task. Open weights mean enterprises can deploy it on their own hardwar

    Matched fieldsTitle

    #hot-signal#Model releases#Open-source ecosystem#Multimodal

Monday

Tuesday

Saturday

  1. Clément Delangue

    Perplexity Open-Sources pplx-decider-v1.2 and Flash Multimodal Decision Models

    Perplexity has released its next-generation open-weight multimodal decision models, comprising two sizes, pplx-decider-v1.2 and pplx-decider-flash-v1.2, with improvements in both text and vision scores, inference up to 5x faster, and all questions in a single request completed in one pass. Hugging Face co-founder Clément Delangue reposted the news and noted that the weights have been published on Hugging Face. Perplexity says the flash version is the top-ranked 4B model on the Decision Index.

    Matched fieldsTitle

    #editorial#Industry news#Open-source ecosystem#Hugging Face

Friday

  2. UnslothSelected

    Meta releases open-source model Muse Spark Open, supporting 1M context and multimodal input

    Meta releases open-source model Muse Spark Open, with weights released the same day. Muse-Spark-Open-816B-A42B supports text, image, video and audio input, with a context window of 1M. Unsloth provides a Dynamic 2-bit GGUF quantized version that can run on Mac Studio, along with a GGUF download address and usage guide.

    Matched fieldsTitle

    #company#Model releases#Multimodal#Open-source ecosystem
  3. Unite.AISelected

    Meta open-sources 816B-parameter multimodal model Muse Spark Open

    On October 15, Meta released the weights of Muse Spark Open, a natively multimodal open-source model with 816B total parameters, 42B activated parameters, and a 1M token context window, targeting coding and long-horizon agent tasks. The model uses a MoE Transformer plus Perception Encoder architecture, can be invoked through API platforms such as Baseten, Fireworks AI, and Groq, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and elsewhere; running the NVFP4 version locally requires at least 700GB of VRAM. Meta claims it is the first model of its class that can run on desktop, carried by two Mac Studios equipped with M5 Ultra paired with the MLX framework; the model card shows its Artificial Analysis Intelligence Index at 39, lower than GLM 5.3's 45 and Kimi K3's 44 in the table.

    On October 15, Meta released the weights of Muse Spark Open, a natively multimodal open-source model with 816B total parameters, 42B activated parameters, and a 1M token context window, targeting coding and long-horizon agent tasks. The model uses a MoE Transformer plus Perception Encoder architecture, can be invoked through API platforms such as Baseten, Fireworks AI, and Groq, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and elsewhere; running th

    Matched fieldsTitle

    #hot-signal#Model releases#Multimodal#Open-source ecosystem

Thursday

  4. Hugging FaceSelected

    Swiss AI releases the Apertus 1.5 multimodal model, expanding context to 262k tokens

    Swiss AI has released Apertus 1.5, which builds on 1.0 by adding native image and audio understanding, a thinking mode, and a 262,144-token context window—4 times larger than 1.0. The model uses early fusion to convert images and audio into discrete tokens and feed them into the same sequence as text; the architecture is unchanged, and it only understands multimodal input while outputting text only. The team says the 8B version outperforms open-source models of the same scale on instruction following and multilingual tasks, its multimodal performance is close to Gemma 4 12B, and it has also released the training data mixture, preference data, and intermediate checkpoints from each stage.

    Swiss AI has released Apertus 1.5, which builds on 1.0 by adding native image and audio understanding, a thinking mode, and a 262,144-token context window—4 times larger than 1.0. The model uses early fusion to convert images and audio into discrete tokens and feed them into the same sequence as text; the architecture is unchanged, and it only understands multimodal input while outputting text only. The team says the 8B version outperforms open-source models of the same scale on instruction foll

    Matched fieldsTitle

    #lab#Model releases#Multimodal#Open-source ecosystem

Wednesday

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