#Open-source ecosystem

LIVE STREAM: New Open Weight Model https://x.com/i/broadcasts/1DxleVaPLagKL

LIVE STREAM: New Open Weight Model https://x.com/i/broadcasts/1DxleVaPLagKL

Reflection releases Beam, a 501B open model for coding and agentic work

Reflection released Beam on Hugging Face, a 501B MoE model with 23B active parameters per token, a 1M-token context window, and an Apache 2.0 license. The text-only model targets coding and agentic work, with BF16, FP8, and NVFP4 checkpoints and day-0 support in transformers, vLLM, and SGLang. Hugging Face provides deployment and post-training recipes alongside its own task evaluations and demos.

Reflection’s Beam scores 30 on Artificial Analysis Intelligence Index, ranking #2 among US open weights models

Reflection’s first model, Beam, scores 30 on the Artificial Analysis Intelligence Index, placing it #2 among US open weights models. Beam is an open weights model with 501B total and 23B active parameters, released on Hugging Face.

Reflection launches Beam, a 501B open-weight MoE model

Reflection launched Beam, a 501B MoE model, with open weights under the Apache 2.0 license and 40+ ecosystem partners. NVIDIA AI says it was trained on NVIDIA GB300 NVL72 and delivers over 70K tokens/sec/GPU on Blackwell Ultra out of the box on day 0. Reflection’s quoted announcement includes BF16, FP8 and NVFP4 weights, a DFlash2 drafter, and Mirror CLI; the Beam 1.0 NVFP4 checkpoint is available at https://huggingface.co/reflection/Beam-1.0-501B-A23B-NVFP4.

BFL releases FLUX 3 Image Klein with open weights and 5.6B parameters

BFL announced FLUX 3 Image Klein with open weights, 5.6B parameters, native 4K, bounding boxes, and pixel-perfect editing, saying it runs on a single consumer GPU. https://t.co/uIOfPdNhuL NVIDIA AI says the model was trained on NVIDIA GB200 NVL72, can run on an RTX GPU, and also has an NVFP4 variant: https://t.co/7rlalk7Otf https://t.co/zf1f7e4kJ7

The same story is shown in the picks as 《Black Forest Labs releases FLUX 3 Image Klein with 8 GB VRAM support》

Meta launches Muse Spark Open, an open-weight AI model designed for Macs

Meta has launched Muse Spark Open, a natively multimodal open-weight model with 816 billion parameters and a 1 million token context window, built for complex coding tasks and agentic workflows. It is designed to run locally on Macs, though Engadget reports it requires at least 1 terabyte of RAM. Meta claims superior performance over other popular open-weight models in its evaluations and is making the model available now through multiple API platforms.

The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》

Ant open-sources Ling-3.1-flash for sustained work across tools and documents

Ant has open-sourced Ling-3.1-flash, a model with approximately 560B total parameters, about 25B activated per token, and a context window of up to 1M. QbitAI tested it on an e-commerce promotion plan and a simulated medical appointment preparation task, reporting traceable calculations and explicit handling of missing information. The review also found occasional attention drift with long inputs and noted that the current version lacks native visual understanding.

Meta releases Muse Spark Open for coding and agentic tasks

Meta has released Muse Spark Open, an open-weight model with 816 billion parameters (42 billion active), up to one million tokens of context, and text, image, video, and audio support. Meta reports leads over Qwen 3.8, GLM 5.3, and Kimi K3 in several benchmark categories, while trailing some competitors in coding. Weights are on Hugging Face; use requires the label "Built with Muse Spark Open," and API providers earning more than $20 million annually need a separate agreement with Meta.

The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》

Alexandr Wang Says Muse Spark Open Weights Are Out Today

Muse Spark Open — weights out today! Best US open-source model, and runs on the new Mac Studio 🍎 https://t.co/C8PU0RvlUA

Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model

Meta’s Muse Spark Open is an open-weight mixture-of-experts model with approximately 816 billion parameters, around 42 billion active per token, and a context window of up to one million tokens. Trained for agentic coding, it accepts text, images, audio, and video and generates text. Hugging Face provides inference and deployment recipes, reports its own quantization tests, and demonstrates attention-layer LoRA fine-tuning with TRL while keeping the experts frozen.

Black Forest Labs releases FLUX 3 Image Klein with 8 GB VRAM support

Black Forest Labs has released FLUX 3 Image Klein, a 5.6-billion-parameter open-weights model for text-to-image generation, image editing, and generation with up to ten reference images. It supports native 4K output, and its INT8 build runs on NVIDIA GPUs with 8 GB of VRAM, generating an image in under 9 seconds on an RTX 4070. The FLUX Kommunity License allows free commercial use for companies making under $5 million annually.

Nvidia releases Nemotron 3.5 Super VL for continuously running enterprise AI agents

Nvidia released Nemotron 3.5 Super VL, an open multimodal model with 120 billion parameters and 12 billion active per token, for continuously running enterprise agents. Nvidia says it approaches Nemotron 3 Ultra’s accuracy on specified agent tasks with about one-fifth the parameters, and is releasing weights, datasets, and training recipes. The accompanying Nemotron 3.5 Bolt 74B-A9B 24GB targets local agents, while Bolt E4B 4GB is planned for later this month.

NVIDIA Expands Nemotron Family With Super VL and Bolt Open Models

NVIDIA released Nemotron 3.5 Super VL, an open 120-billion-parameter model for multimodal agents, and Nemotron 3.5 Bolt 74B-A9B 24GB for local agentic workloads; Bolt E4B 4GB is coming Oct. 23. Super VL targets tool use, business workflows and visual document understanding, with open weights, datasets and training recipes. Bolt 74B-A9B uses native 2-bit quantization to fit in 24GB of memory, while Bolt E4B targets physical and conversational agents.

StepFun releases open weights for Step 5 Preview, a 600B MoE model

NVIDIA AI congratulates StepFun on Step 5 Preview, calling it StepFun’s largest open weights release. NVIDIA says the 600B MoE model supports agent tasks lasting up to 24 hours and runs at nearly 8K tokens/sec/GPU on NVIDIA GB300 using NVFP4. StepFun’s quoted announcement lists 27B active parameters per token, a 1M-token context window, native text and vision input, and BF16 and FP8 weights on Hugging Face and ModelScope. NVP4 checkpoint: https://t.co/lSECChAwlW Deploy with https://t.co/GO1wqTSBOk https://t.co/28TBzUo6ZU

Apertus 1.5 Release Notes Detail Multimodal Understanding, Thinking Mode, and Longer Context

Project Apertus released Apertus 1.5 in 8B and 70B sizes, adding image and audio understanding, optional thinking, a 262,144-token context window, and improved instruction-following and tool use. The weights were released on Hugging Face on July 24, 2026 under Apache 2.0; the models generate only text, and spoken-language processing remains experimental. The team also publishes training data, recipes, intermediate checkpoints, and reproducible evaluation records.

Kunlun Tech releases and open-sources Matrix-Game 4.0 for single-GPU real-time gameplay

Kunlun Tech released Matrix-Game 4.0, reporting 480p gameplay at 60 FPS on a single NVIDIA RTX 5090 with end-to-end response latency below 150 milliseconds. Its game engine maintains world state and interaction rules, a coding agent modifies game logic, and a video model generates detailed visuals from structural frames and dynamic semantic conditions. Code, weights, and simplified scene models are available at https://huggingface.co/Skywork/Matrix-Game-4.0.

Fish Audio’s Drama 3 Preview leads open-weights models in Artificial Analysis TTS rankings

Artificial Analysis reports that Fish Audio’s Drama 3 Preview ranks #1 among open-weights models on all three of its Text to Speech leaderboards and leads all models in Pronunciation Robustness. It scores 93.2% on pronunciation robustness, ahead of Eleven v4 at 91.7% and Eleven v4 Turbo at 90.1%, while ranking #10 in Provider Voice and #14 in Controlled Voice. Its weights are available on Hugging Face under the Fish Audio Community License, and Fish Audio API pricing is $15/1M characters.

Liquid AI releases LFM2.5-24B-A2B for fast, private on-device agents

Liquid AI released LFM2.5-24B-A2B, an open-weight reasoning model with 24B total parameters and 2B active parameters, for on-device agents that reason and call tools. Its context window expands from 32K to 131K tokens, and both the reasoning and Base versions are available on Hugging Face. Liquid AI reports 184 tokens/s on an Apple M5 Max and competitive tool-use results, while acknowledging that larger models retain advantages on knowledge-heavy and coding benchmarks.

vLLM Congratulates vllm-sr on Open-Sourcing Decision 3.0 Multimodal Model

vLLM congratulated the vllm-sr team on releasing Decision 3.0, described as a state-of-the-art multimodal Jev model whose weights are now open-sourced on Hugging Face. The quoted announcement from Xunzhuo Liu says the open model family spans 0.6B to 27B and ranks #1 on both Jev Decision Index boards in its evaluation, on a new Pareto frontier.

Step 5 Preview Tops OpenRouter Trending Ahead of October 15 Open-Source Release

StepFun's flagship base model Step 5 Preview reached the top of OpenRouter's Trending list and second place on its daily ranking, with an official open-source release set for October 15. The sparse MoE model has 600 billion total parameters and about 27 billion activated per token, and Artificial Analysis gives it a composite intelligence index of 44, placing it in the global open-source top three.

Qwen releases Qwen-Image-2.1-Turbo, generating and editing images with 8-step denoising and open weights

Qwen has released Qwen-Image-2.1-Turbo, based on the same 7B visual generation architecture as Qwen-Image-2.1, requiring only 8 denoising steps to generate and edit images, with weights now open on ModelScope and Hugging Face.

Liquid AI open-sources d1-3B and d1-omni-600M on-device decision models

Liquid AI released two open-weight models in its d1 decision-model family: d1-3B and the experimental d1-omni-600M.

Mistral releases 1-trillion-parameter Mistral Large 4, codenamed Le Chonk

Mistral released its 1-trillion-parameter Mistral Large 4, codenamed Le Chonk. Anyone can use and customize it for free during the preview; the final version is due by the end of the month. The model is for general tasks, with additional optimization for coding, cyber defense, and fields such as manufacturing, finance, and electrical engineering. Mistral says Le Chonk is the strongest open-weight model outside China so far and comes close to some closed models. The company emphasized that it was trained from scratch rather than distilled.

Google DeepMind releases EmbeddingGemma 2, a lightweight open multimodal embedding model

Google DeepMind released EmbeddingGemma 2, a 740M-parameter open model based on the Gemma 4 architecture and licensed under Apache 2.0. It maps text, images, audio, and video into a shared embedding space.

Musubi releases open-weight PolicyLM-1.7B decision model for real-time content moderation

Musubi released PolicyLM-1.7B, a lightweight decision model with open weights that applies content policies written in plain English to messages in 50 milliseconds. Its cost and speed are close to the AI classifiers used for moderation by major social platforms, while it offers the flexibility of a modern LLM and needs no specialized training to follow complex policies. Policy changes do not require retraining. Musubi co-founder and chief AI officer Filip Jankovic said this lets platform managers label content proactively.

Google DeepMind releases EmbeddingGemma 2, an on-device multimodal embedding model

Google DeepMind released EmbeddingGemma 2, which it calls the first natively multimodal open embedding model for on-device use. It combines code, images, audio, and video with text in a shared space.

Google releases EmbeddingGemma 2, an open multimodal embedding model

Sundar Pichai announced EmbeddingGemma 2, described as Google's first open, natively multimodal embedding model. It handles text, code, image, video, and audio tasks in a modular 740M parameter form factor, is positioned for offline, privacy-first RAG when paired with Gemma 4, and is said to outperform some specialist models more than twice its size. Weights are available now on Hugging Face.

Mistral releases trillion-parameter Mistral Large 4, targeting cybersecurity tasks

Mistral released its largest model to date, Mistral Large 4, with 1 trillion parameters and 49 billion active parameters. An API preview is available in Mistral Studio; weights are expected at the end of October.

Mistral launches public preview of Mistral Large 4, a 1-trillion-parameter multimodal model

Mistral released a public preview of Mistral Large 4, a natively multimodal model with 1 trillion parameters and 49 billion active parameters. Its weights will be opened at the end of the month.

Reflection releases open-weight Beam model focused on low compute costs

AI company Reflection released its first free, open model, Beam, targeting coding, logical reasoning, and agent tasks. It uses a mixture-of-experts architecture, activating 23 billion of its 501 billion total parameters per token.

Reflection releases Beam, a U.S. open MoE model with 501B total and 23B active parameters

Reflection released Beam, a text-only mixture-of-experts model for coding, agents, and scientific work. It has 501B total parameters and 23B active parameters, was trained from scratch, and will release all weights under Apache 2.0 later this month.

Reflection releases open-weight Beam model to compete with Chinese open models

Reflection AI officially released its first frontier open-weight model, Beam, saying it performs comparably to leading Chinese open models on advanced reasoning benchmarks with 3–4 times less inference compute. Beam is a 501-billion-parameter, 23-billion-active-parameter text model, pretrained on 23.8 trillion tokens with a 1-million-token context window. The company says it matches Z.ai's GLM-5.2 and exceeds current leading Western open models.

Reflection AI announces Beam, a 501B-parameter agentic open model

Reflection AI introduced Beam, described as a highly efficient agentic open model with 501B total parameters and 23B active parameters. The company says it advances the Western open frontier on coding and agentic tasks and was trained end-to-end from scratch, with full weights to be released this month. Ollama said more US models are coming to its platform.

Aleph Alpha releases Kolibri, an open-weight 78B model

Aleph Alpha released Kolibri, a bilingual German-English model with 78B total parameters using a mixture-of-experts architecture that activates about 3B parameters per token. The company says Kolibri sits on the Pareto frontier for quality and operating cost in both languages. It scores 71% on German benchmarks and decodes faster than comparable models including GPT OSS A5B, Qwen 3.6 A3B, and Gemma 4 A4B.

Cloudflare releases Clef and Clef-flash decision models, saying agent decisions no longer need human involvement

Cloudflare released Clef and Clef-flash, two decision models for AI agents. They return brief classifications with probabilities rather than long text, and the company says agent decisions no longer need human involvement.

AI2 releases Olmo-core 3, open and scalable training infrastructure for large MoE models

AI2 released Olmo-core 3, a major upgrade to its large language model development framework. It redesigns the open MoE training system to scale MoE training to trillions of parameters while maintaining compute efficiency.

Alibaba's Qwen-Audio-3.1-TTS tops Artificial Analysis speech leaderboard

On September 28, Alibaba's Qwen-Audio-3.1-TTS speech model ranked first globally on the Controlled Voice Arena leaderboard by Artificial Analysis with an Elo rating of 1,177.

NVIDIA releases Kumo Tabular, a foundation model for tabular prediction

NVIDIA has released Kumo Tabular, an open-source tabular foundation model that predicts labels for new rows in a single forward pass from labeled table rows, supporting classification and regression without training, tuning, or feature engineering. The model comes in three sizes from 28M to 215M parameters, was pretrained entirely on synthetic tables, is commercially usable under the OpenMDW-1.1 license, and ranks first on four benchmarks: TabArena, BeyondArena, TALENT, and ScoringBench.

Zhizhi Innovation Institute releases IQuest-Q1, with 320B total and 15B active parameters

IQuest Research at Zhizhi Innovation Institute has released IQuest-Q1, a decoder-only Transformer with a sparse mixture-of-experts architecture, about 320B total parameters and about 15B active parameters. It is available on GitHub and Hugging Face.

AINews: Opus 5.5 excels at generating explainer videos

Latent Space's AINews roundup of developments from September 24–25 says community feedback was positive after Opus 5.5 launched that week, especially for generating explainer videos with code.