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Nvidia releases open Nemotron 3.5 Transcribe for transcription, translation, and speaker recognition

Nvidia has released Nemotron 3.5 Transcribe, an open speech recognition model that handles transcription, speaker recognition, and translation and accepts instructions as text prompts. Nvidia says it leads several benchmarks while using only one-sixth of the active parameters of its strongest open rival. The release also includes a dataset of more than 800,000 hours of speech and tools for fine-tuning.

Why it matters: It gives readers a concrete new open speech model release with combined capabilities, benchmark claims, and supporting data and fine-tuning tools.

The DecoderOct 28, 2026Score 86

Nvidiaspeech recognitionopen models

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Liquid AI Releases LFM3: 6 Open-Weight 2B-Class Multimodal Models

Liquid AI introduced LFM3, a family of six open-weight 2B-class models built on one tri-hybrid backbone for text, vision, audio, omni and decision tasks. The 1.7B text model runs in about 1GB at 4-bit with a 256K context and can be fine-tuned on a single GPU. Liquid reports strong agentic benchmark results versus larger models, while knowledge and agentic coding trail bigger systems. Weights, docs and API access are available.

Why it matters: This is a concrete open-weight model release with practical on-device deployment details and fresh benchmark comparisons, useful for developers evaluating small multimodal backbones.

MarkTechPostOct 28, 2026Score 84

Liquid AILiquid Foundation Modelssmall language models

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OpenAI introduces GPT-6.1 Terra, a midrange model focused on visual reasoning and computer use

OpenAI released GPT-6.1 Terra, a fast, efficient model that emphasizes detailed visual reasoning, precise computer use, and clearer natural writing. It is positioned as a midrange option between the more capable Sol and the cheaper Luna, with pricing at $1 per million input tokens and $5 per million output tokens. The model is described as a significant step up from GPT-5.6 Terra and particularly suited for tasks such as drafting proposals, interpreting technical drawings, filling forms, and edi

Why it matters: This provides concrete details about a new OpenAI model release, including positioning, pricing, and intended use cases, which is directly useful for AI practitioners and subscribers.

Simon WillisonOct 27, 2026Score 80

OpenAIGPT-6.1computer use

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Hugging Face Releases Carbon-X, a 14.8B Open DNA Language Model

Hugging Face Bio has released Carbon-X, an Apache 2.0 decoder-only model with 14.8B parameters that processes raw DNA and English in a single token stream and answers in natural language. Built on Qwen3-14B with a hybrid tokenizer adding 5,515 DNA tokens over 6-mers, it was trained in four stages and runs on one 80GB GPU or quantized GGUF builds. Hugging Face reports a 66.7% average across five DNA benchmarks, leading on BioReason Coding at 74.9% but weaker on GenomeQA splice-site questions at 2

Why it matters: It gives readers a concrete, openly licensed DNA model with weight-level access, deployment guidance and self-reported benchmarks for judging practical use.

MarkTechPostOct 26, 2026Score 80

Carbon-XHugging Facegenomics

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NVIDIA Releases Nemotron 3.5 Bolt E4B, an Open Small Model for Local Agents

NVIDIA released Nemotron 3.5 Bolt E4B, an open small language model built by RL post-training of Google's Gemma 4 E4B without supervised fine-tuning. It has 8B total and 4B effective parameters with 64K context, targeting local voice assistants, game NPCs and robot interfaces on RTX, Jetson and Spark hardware. NVIDIA reports top scores on 15 of 21 benchmarks in its own harness, but Qwen3.5-4B leads on Tau2-Bench and long-context tests. Weights, data and training recipes are public, with the Q4_K

Why it matters: It gives developers a concrete open-weight option for running capable tool-calling agents locally on constrained hardware, with disclosed training methods and honest benchmark weaknesses.

MarkTechPostOct 24, 2026Score 83

NVIDIANemotronGemma

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Moonshot releases Kimi K3.1 Pro with open weights promised by Nov 4

Moonshot launched Kimi K3.1 Pro on its API, priced at $2.00 per million input tokens and $10 per million output tokens, with a higher-priced "Highspeed" variant that runs 2-3x faster. The model is not yet open weights, but Moonshot promises full weights by Nov 4, 2026. Moonshot's own benchmarks place it near Claude Opus 5.5 and GPT-6 Astra, and Artificial Analysis scored it 55, behind only Opus 5.5 at 58. Simon Willison tested it through the Moonshot API, including a pelican SVG comparison acros

Why it matters: It signals a soon-to-be open-weight model reaching near-frontier benchmark standing at competitive API pricing, which matters to teams choosing models.

Simon WillisonOct 24, 2026Score 80

MoonshotKimi K3.1 Proopen-weight models

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Gradium Open-Sources Phonon, a 31M-Parameter On-Device TTS Model Under Apache 2.0

Gradium, a Paris voice AI company spun out of Kyutai, has released Phonon, a family of small text-to-speech models that run fully on-device. The English Phonon 31m ships under Apache 2.0 at 64.5 MB (q8 GGUF) with 31M parameters and 37 built-in voices, producing 24 kHz mono audio with no cloud call or GPU. It uses a causal transformer backbone, SentencePiece tokenization without phoneme conversion, a single-step flow-matching head and a reduced Mimi decoder. Gradium's own evaluations report first

Why it matters: Provides concrete deployment, licensing and performance details for a new openly licensed on-device TTS model developers can self-host.

MarkTechPostOct 23, 2026Score 81

Gradium Phononon-device TTSopen-weight speech models

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IBM Releases Granite Speech 5.0 2B, an Apache 2.0 Multilingual Speech Model

IBM released Granite Speech 5.0 2B, a 2B-parameter open speech-language model for transcription and translation in 6 languages under Apache 2.0. It combines a 16-block Conformer speech encoder with a window q-former and a fine-tuned granite-4.0-1b-base backbone, and adds language-ID prompting, keyword biasing up to 2,000 terms, and requested punctuation and capitalization. IBM reports 5.68% average English WER on the Open ASR Leaderboard, ranking second among open-weight models behind Qwen3-ASR-

Why it matters: Developers get a concrete, benchmarked open-weight ASR and translation release with deployment options and licensing details they can evaluate directly.

MarkTechPostOct 22, 2026Score 83

IBM Granitespeech recognitionopen-weight models

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Recursive Releases Flint, an Open-Weight 40B MoE Local Coding Model

Recursive Superintelligence released Flint, a text-only mixture-of-experts coding model with 39.8B total and roughly 2.5B active parameters, under Apache 2.0 on Hugging Face. It targets coding agents, supports reasoning and OpenAI-compatible tool calls, and runs in about 23 GB at 4-bit on 48 GB Apple Silicon Macs or NVIDIA GPUs. Recursive reports 74.2% on SWE-bench Verified and 54.9% on Terminal-Bench 2.1, trailing the strongest dense models on the latter. The company says its Eureka Machine aut

Why it matters: It gives readers concrete details on a new open-weight local coding model, including architecture, hardware needs and self-reported benchmark results.

MarkTechPostOct 22, 2026Score 83

Recursive SuperintelligenceFlintopen-weight coding models

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MiniMax Releases M3.1 Flash: 428B MoE Agentic Model at $0.15/M Input Tokens

MiniMax has launched MiniMax M3.1 Flash on its API, a multimodal mixture-of-experts model with 428B total parameters, about 23B active, and a 1,000,000-token context window. It is priced at a flat $0.15 per 1M input tokens and $0.60 per 1M output tokens, half of M3's standard per-token rate. MiniMax reports near-frontier agentic results, including a top Automation-Bench v1.0.6 score of 62.2, but weaker hard-coding results such as 38.1 on Terminal Bench 4.0. Weights are due October 23, 2026, with

Why it matters: It gives developers concrete pricing, context, benchmark and deployment details for a newly released low-cost agentic model, with vendor-reported figures clearly flagged.

MarkTechPostOct 19, 2026Score 80

MiniMaxMiniMax M3.1 Flashmixture-of-experts

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Mistral Releases Timestral, a CPU-Friendly Time Series Foundation Model Family

Mistral AI has released Timestral, a family of four zero-shot time series forecasting models ranging from 5M to 209M parameters, with open weights and inference code. The 209M checkpoint is reported by Mistral to rank first among pretrained zero-shot models on GIFT-Eval, TIME and fev-bench, though several margins are narrow. The models support long context, 21 quantiles per step and covariate-aware attention, and run inference on standard server CPUs without a GPU. Weights are released under a M

Why it matters: It gives developers a concrete, openly weighted forecasting model family with CPU-only inference and benchmark comparisons against TimesFM-3, Chronos-2 and Toto 2.0.

MarkTechPostOct 19, 2026Score 83

Mistral AItime series foundation modelsGIFT-Eval

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Kyutai Releases REKI, a 2.4B Vision-Language-Action Model with Cached Register Memory

Kyutai, with Inria and École des Ponts, has released REKI, a 2.4B-parameter vision-language-action model for robot manipulation that was pretrained only on public data. It pairs a Qwen3.5-2B backbone with a 200M-parameter flow-matching action expert, and adds memory through 32 learned register tokens cached over the last three frames rather than feeding past frames into the VLM. The authors report 90.4% on LIBERO-Plus and 65.8% on RoboTwin Clean2Rand, with plain-language controls cutting arm osc

Why it matters: It offers a concrete, openly released VLA and a cheap memory design that robotics practitioners can inspect, reproduce and finetune.

MarkTechPostOct 18, 2026Score 81

Kyutaivision-language-action modelsrobot manipulation

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StepFun Releases Step 5 Preview Open Weights: 600B MoE, 27B Active, 1M Context

StepFun has published open FP8 and BF16 checkpoints for Step 5 Preview, a sparse Mixture-of-Experts model with 600B total parameters, 27B active per token and up to 1M tokens of context, targeting long-horizon agentic work. The release lists data-center-only self-hosting requirements, including 4x B300 or 8x H200 GPUs for the FP8 checkpoint, and an API priced at $1.00 per 1M input tokens and $2.70 per 1M output tokens. StepFun-reported results put it near GLM-5.3 and Kimi K3 on DeepSWE v1.1, whi

Why it matters: It gives developers a concrete new self-hostable open-weight option for agentic and long-context work, with clear hardware requirements, pricing and benchmark caveats.

MarkTechPostOct 17, 2026Score 81

StepFunopen-weight LLMsmixture-of-experts

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Reflection AI Releases Beam, a 501B-Parameter Open-Weight Model for Agentic Coding

Reflection AI has released Beam, an open-weight sparse mixture-of-experts model built for agentic coding and long-horizon software tasks. Beam has 501 billion total parameters with 23 billion active, a reported 1 million-token effective context length, and Apache 2.0 weights on Hugging Face in BF16, FP8 and NVFP4 checkpoints. The AWS post details deployment via SageMaker JumpStart real-time endpoints or SageMaker HyperPod Inference with JumpStart, SGLang or vLLM, including GPU sizing and tuning

Why it matters: It documents a new open-weight agentic coding model with concrete parameter counts, licensing and deployable AWS serving paths that developers can evaluate.

AWS Machine Learning BlogOct 16, 2026Score 82

Reflection AIBeamopen-weight models

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Meta Releases Muse Spark Open: 816B Open-Weight Multimodal MoE With 1M Context

Meta Superintelligence Labs released Muse Spark Open, an 816B-parameter Mixture-of-Experts model that activates 42B parameters per token and accepts text, image, video and audio input with a 1M-token context window. Weights ship in BF16 and NVFP4 on Hugging Face, with day-one support in SGLang, vLLM, TensorRT-LLM, Transformers, MLX, llama.cpp and Unsloth. Meta reports top scores on tool use (MCP-Atlas 89.2) but trails Kimi K3 and GLM 5.3 on Terminal-Bench 2.1 (76.4) and on Artificial Analysis In

Why it matters: It gives developers concrete open-weight release details, deployment options, license terms and comparative benchmark results for a large multimodal model they could actually adopt.

MarkTechPostOct 16, 2026Score 85

Meta Superintelligence LabsMuse Spark Openopen-weight LLMs

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NVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model

NVIDIA released Nemotron 3.5 Super VL, an open vision-language agent model with 120B total parameters and 12B active per token in a mixture-of-experts design, supporting text, image, document and video input with up to 1M-token context. It ships with an NVFP4 checkpoint runnable on a single DGX Spark, H200 or GB200, plus open datasets, training recipes and RL environments in NeMo Gym under the OpenMDW-1.1 license. NVIDIA-reported results include 89.90 on GPQA-Diamond and 71.07 on SWE-bench Verif

Why it matters: Developers get a concrete, deployable open multimodal agent model with published weights, licensing, hardware requirements and benchmark numbers to weigh against alternatives.

MarkTechPostOct 16, 2026Score 85

NVIDIANemotron 3.5open-weight LLMs

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Black Forest Labs releases open-weights FLUX 3 Image Klein image model

Black Forest Labs has released FLUX 3 Image Klein, a 5.6-billion-parameter open-weights image model with native 4K generation, pixel-precise editing and support for up to ten reference images. It runs on consumer GPUs with as little as 8 GB of VRAM and has 38 percent fewer parameters than its predecessor. The company says internal tests show it outperforming proprietary competitors.

Why it matters: An open-weights image model that runs on low-end consumer GPUs is directly useful to developers and creators building local image generation workflows.

The DecoderOct 16, 2026Score 80

Black Forest LabsFLUX 3 Image Kleinopen-weights image models

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NVIDIA Expands Nemotron Family With Open Super VL and Bolt Models

NVIDIA released Nemotron 3.5 Super VL, an open 120-billion-parameter vision-language model aimed at multimodal agentic tasks such as tool use, instruction following and document understanding, along with open weights, datasets and training recipes. It also introduced two edge models, Nemotron 3.5 Bolt 74B-A9B 24GB for local agentic workloads and Bolt E4B 4GB for conversational and physical agents. Super VL and Bolt 74B-A9B are available now, with Bolt E4B due Oct. 23. NVIDIA says several organiz

Why it matters: It gives readers concrete details on three newly released open models spanning data center to edge, including deployment options, availability and enterprise customization efforts.

NVIDIA BlogOct 15, 2026Score 80

NVIDIANemotronopen-weight models