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Model releases Latest updates

New model releases, open-sourcing, and iteration: flagship updates from major model vendors, open-weight releases, and first-hand records of performance and price changes.

49 条精选Past 30 days: 42 updatesTotal: 146 updates

Updated

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Nov 6Fri 21–40 updates
  1. Hacker News AI82

    Tencent Launches Hy4, One of the Leading Open-Source AI Models

    Tencent announced Tencent Hy4, its latest-generation large language model, which it says ranks among the world's leading open-source models, with model weights and system cards planned for release in the coming days. Hy4 adds visual understanding and computer-use capabilities for the first time, along with improvements in instruction following, long-context comprehension and long-horizon agentic tasks, and is available across Tencent Cloud TokenHub, WorkBuddy, CodeBuddy, Yuanbao, WeCom's AI Assistant (Dayuan) and ima, with API access via OpenRouter, Hermes and OpenCode. Pricing remains about US$0.90 per million input tokens, US$2.70 per million output tokens and US$0.045 per million cached input tokens, with Hy4 Flash scheduled for release soon.

    Why it matters: The announcement is a detailed primary source on a major open-source model release, including benchmark comparisons, safety assessments and pricing.

  2. Hugging Face Blog62

    New NVIDIA Cosmos 3 Open Models for Interactive World Simulation

    NVIDIA's Cosmos world model platform adds four new open-weight checkpoints to its Cosmos 3 family: Cosmos3-Nano-Transfer-Auto for multi-view driving data with varied weather, lighting and time of day; Cosmos3-Nano-Sim-Auto for streaming synchronized camera and LiDAR driving simulation; Cosmos3-Nano-Sim-Transfer for turning simulator controls and text prompts into photorealistic video; and Cosmos3-Nano-Sim-Bimanual for predicting video from robot or camera actions. All four are post-trained examples of Cosmos 3 Nano with 16B trainable parameters and use OpenMDW1.1, permitting commercial and non-commercial use under its terms. The post also details a four-stage training recipe (bidirectional adaptation, teacher forcing, consistency distillation, self-forcing) and post-training guidance for adapting the models to new sensor rigs and embodiments.

    Why it matters: It gives a concrete inventory of newly released open-weight world-model checkpoints plus the training recipe, useful for researchers weighing them for robotics or driving simulation.

  4. Artificial Analysis Articles62

    Nanbeige has released Nanbeige4.3-33B scoring 20 on the Intelligence Index

    Nanbeige, the foundation model research team at BOSS Zhipin (operated by Kanzhun Limited), has released Nanbeige4.3-33B, an Apache 2.0 open-weights dense reasoning model with text-only input and output and a 524K-token context window. It scores 20 on the Artificial Analysis Intelligence Index v5, matching Qwen3.8 27B (xhigh) at 20, with relative strengths in terminal use (30% on Terminal-Bench 4.0 vs. 6%) and physics reasoning (16% on CritPt v2's Hard Subset vs. 4%), but trails on AA-Briefcase v1.1 (1162 vs. 1391 Elo), FrontierCode 2.0 (11% vs. 15%) and HLE-Diamond (7% vs. 15%). It shows high abstention and low hallucination on Omniscience (18% vs. 29% hallucination rate, declining 71% of questions) and used 158K output tokens per Intelligence Index task vs. 110K for Qwen3.8 27B.

    Why it matters: Useful for readers tracking small open-weights models, since it sets a new Pareto point for Intelligence Index versus total parameters and details trade-offs against Qwen3.8 27B.

  5. Google DeepMind80

    Gemini Omni 1.2 Flash: Fast and cost-effective with longer clips

    Google DeepMind released Gemini Omni 1.2 Flash, now generally available across the Gemini API and Gemini for business, adding native 15-second video generation, multi-turn conversational editing, and stronger reference adherence. The model is priced 30% below Gemini Omni 1.1 at $0.07 per second for 720p video, with duration settable in one-second increments from 3 to 15 seconds. It also adds more natural speech and lip-sync, more controllable cinematic camera moves, and outputs carrying an imperceptible SynthID watermark.

    Why it matters: Useful for developers and creators weighing a cheaper, longer-clip video model against its stated limits on fast action and complex physics.

Nov 5Thu
  1. Hugging Face Blog71

    A Closer Look at Phonon TTS. Launching High-Quality On-Device Speech That Tops the Benchmarks.

    Gradium details Phonon, a small on-device text-to-speech model released two weeks ago that needs no server or network, with Phonon 31m (English) under Apache 2.0 and Phonon 40m covering English, French, German, Spanish and Portuguese under a non-commercial license. The models, about 65 MB and 75 MB, offer over 100 built-in voices plus voice cloning and voice design, and on Hugging Face's Open TTS Leaderboard Phonon ranks first in German and Spanish on intelligibility and tops the CPU streaming benchmark on time-to-first audio. The post also covers the ptts inference framework, Transformers integration from v5.21, launch demos, and more than 200,000 downloads in the first two weeks.

    Why it matters: It offers a detailed look at how a small on-device TTS model was built, benchmarked and integrated into developer tooling.

  2. OpenAI News60

    Introducing Images 2.6

    Images 2.6 introduces faster image generation, GIF creation, more precise editing, and new API controls across ChatGPT, Codex, and the API.

    Why it matters: A brief release note useful for readers tracking image-generation feature updates across OpenAI products.

  3. MarkTechPost76

    Ricursive AI Releases Ripple-65B Preview: A 65B Dense Open-Weight Model Scoring 80.5 on Terminal-Bench 2.1

    Ricursive Intelligence released Ripple-65B Preview, a 65B dense open-weight model pretrained from scratch on 12T tokens with up to 1M context, shipping three Apache 2.0 checkpoints (instruction-tuned BF16, NVFP4, and BF16 base). Ricursive reports 80.5 on Terminal-Bench 2.1, the top self-reported score among US open-weight models, while trailing larger MoE rivals on SciCode (39.4) and DeepSWE 1.1 (44.3), with full R&D cost stated at $32M. Weights are on Hugging Face, with Together AI, GMI Cloud and OpenRouter listed at launch and an experimental NVFP4 build targeting NVIDIA DGX Spark.

    Why it matters: Useful for readers tracking open-weight agentic coding models, since it pairs self-reported benchmark claims with a detailed architecture, cost and deployment breakdown.

  4. Hugging Face Blog60

    Dharma Field Extractor: Any Document In, Structured Fields in JSON Out

    Dharma-AI introduces the Dharma Field Extractor, a 2-billion-parameter model built on DharmaOCR-LITE v1.1 with a LoRA adapter trained on 43,384 examples, which takes a document image plus either an open request or an exact schema and returns typed JSON, including null for fields not present. On a 546-document, 1,638-case benchmark it scored 0.834 field-level F1, above every frontier configuration tested (Claude Opus 5 at 0.816 to GPT-5.6 Luna at 0.741), while running self-hosted on a single L40S at a cost the company reports as 6 to 147 times lower than the APIs compared. The company frames the result as evidence that a small model trained close to a bounded task can beat a general frontier model on that task.

    Why it matters: Useful for teams weighing self-hosted specialized extraction against frontier APIs, though the benchmark and cost figures come from the vendor itself.

Oct 29Thu
  1. Hugging Face Blog80

    Welcome Le Chonk: Mistral Large 4 on Hugging Face

    Mistral Large 4 ("Le Chonk") is a multimodal, multilingual Mixture-of-Experts model with about 1T total and 49B active parameters and a 1M-token context window, released in FP8 and NVFP4 variants under the Mistral Ecosystem Open-Weight License. It combines a 72-layer text decoder using MLA with a DeepSeek Sparse Attention indexer and a 1.6B Pixtral vision encoder, and the post reports 82% on the Artificial Analysis Cyber Index vulnerability reproduce-and-patch test and 93% on Cybench. Day-0 support covers Transformers, vLLM, llama.cpp, TRL fine-tuning, Inference Endpoints and Dell Enterprise Hub, with GGUFs from Unsloth and the llama.cpp team.

    Why it matters: A detailed, hands-on release guide that pairs architecture specifics and benchmark claims with runnable deployment and fine-tuning recipes for a large open-weight model.

  2. Mistral AI82

    Le Chonk: Built for Coders, Trusted by Defenders.

    Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model trained from scratch in Europe, reporting leading open-weight results on coding benchmarks such as FrontierCode 2.0 (19.7%) and Terminal-Bench 4.0 (41%), plus 51% on the Artificial Analysis Cyber Index. It offers three reasoning modes (Low, Medium, High) and is priced at $0.70 input / $0.07 cached input / $2.10 output per million tokens under a modified MIT license that requires a separate commercial license for Model-as-a-Service offerings.

    Why it matters: The post details benchmark scores, pricing, licensing terms and deployment options for a flagship open-weight model, useful for developers weighing coding performance against cost and control.

  3. Latent Space82

    [AINews] Gemini 4 Carbon tops leaderboards, launches for cyber defenders

    Google DeepMind released Gemini 4 Carbon, a frontier model that takes the #1 spot on Artificial Analysis' Intelligence Index v5 with a score of 59, two weeks after Argon. Access is initially limited to trusted cyber defenders through the Fairwind Program, with claimed results of 88.2% on SEC-bench Pro, 54.8% on ExploitGym and 71.0% on CWE-bench, a 1M-token context window, and pricing listed at $4/$20 per million tokens with a 50% launch discount. Independent evaluations place it first in Text Arena and Vending-Bench 2 but behind Opus 5.5 on coding-agent and FrontierSWE benchmarks, and its hallucination rate rose from Argon's 4% to 11%.

    Why it matters: Useful for tracking how a new frontier model's leaderboard gains compare with its coding and hallucination trade-offs, and for the debate over benchmark methodology changes.

  4. Google DeepMind82

    Gemini 4 Carbon: Our next frontier model

    Google announced Gemini 4 Carbon, its strongest model with state-of-the-art cybersecurity and long-horizon capabilities, ranking #1 on the Artificial Analysis Intelligence Index v5.0 at 59 and scoring 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. It launches at Argon's introductory price of $2 per million input tokens and $10 per million output tokens, initially available to a subset of cyber defenders and trusted partners through the Fairwind Program before wider release. Google reports reduced misalignment alerts, a 0.0% prompt-injection attack success rate on Gray Swan's IPI Benchmark, and 0.0% successful cheating on Terminal-Bench 4.0.

    Why it matters: It documents a major frontier model release with concrete benchmark numbers and a staged, safety-focused rollout, useful for tracking competitive and cybersecurity trends.

Oct 28Wed
  1. Hugging Face Blog78

    NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt

    NVIDIA released Nemotron 3.5 Transcribe, an open-weight, promptable model that handles transcription in 40 languages, translation of 31 to or from English, speaker attribution in 13 languages, and audio question answering in one checkpoint, running on a single NVIDIA GPU. It ranks first among open-weight models on the Hugging Face Open ASR Leaderboard for English at 5.33% average WER and posts the lowest diarization error rate on David AI's 21-language leaderboard. The release also includes Granary v2, an open data curation pipeline covering 463,062 hours across 48 languages, and NeMo Speech fine-tuning recipes that cut held-out WER by 45.8% relative to the best prompt in a medical case study.

    Why it matters: It documents concrete benchmark standings, an open data pipeline, and a reproducible fine-tuning recipe for teams evaluating or adapting open speech models.

  2. Hugging Face Blog78

    Build specialized multimodal models with LFM3

    Liquid AI introduces LFM3, a generation of Liquid Foundation Models in the 2B parameter range that places text, vision, audio, omni, and decision models on one shared backbone built from LFM3-1.7B-Base, which was pre-trained on 22T tokens and uses a tri-hybrid layer mix with a 256K context window. The release includes six 2B-class models, new in-house vision and audio encoders, and LiquidCodec, with every model fine-tunable on a single GPU and the text model fitting in about 1 GB at 4-bit precision. All LFM3 models are open-weight and available on Hugging Face, with fine-tuning notebooks and agent harness guides provided.

    Why it matters: The post is a detailed technical release covering architecture changes, benchmark comparisons, and runnable code for six models, useful for developers evaluating small multimodal models for on-device deployment.

  3. Google DeepMind82

    Genie 4: A new generation of world simulation

    Google announced Genie 4, a natively multimodal world model that generates photorealistic, real-time interactive simulations with native audio, including ambient sound and interactive dialogue, and builds on Gemini models for reasoning and coherence. It is rolling out as a research preview to trusted testers, with plans to reach Google AI Ultra subscribers in Project Genie and early access partners via the Gemini API and Google AI Studio. Google notes limitations such as drift in visual details and object permanence over longer sessions, and says all generated video and audio include SynthID watermarking.

    Why it matters: Useful for readers tracking frontier world models, as it details Genie 4's capabilities, staged rollout, and acknowledged limitations.

Oct 27Tue
Oct 24Sat
  1. Latent Space82

    [AINews] Kimi 3.1 Pro 104BA-2.8T: Fable-5.1-Class Open Weights narrows Closed-AI lead to 1 month... and Pro Highspeed API with 2-3× speed

    Moonshot launched Kimi K3.1 Pro, a multimodal 3T-class mixture-of-experts model with 2.8 trillion total parameters, 104 billion active parameters and a 1-million-token context window, available via API with weights promised for November 4, 2026. Artificial Analysis placed it fourth on its Intelligence Index at 55, behind Opus 5.5 at 58 and Sonnet 5.5 and Muse Wonder Full at 56, and said the leading-open-versus-proprietary gap would narrow from 12 points to three once weights are released. Standard pricing is $2/M input, $10/M output and $0.20/M cached input tokens, with a Highspeed version at twice the price that Moonshot claims generates output 2–3× faster.

    Why it matters: Useful for tracking how close open-weight models have come to the closed frontier, plus the caveats around benchmark provenance, serving economics and the still-unreleased weights.

  2. The Decoder84

    Chinese AI model Kimi K3.1 Pro is closing in on Anthropic's Claude Opus 5.5

    Moonshot AI released Kimi K3.1 Pro, a post-trained successor to Kimi K3 that scores 55 points on the Artificial Analysis Intelligence Index, an 11-point gain that places it fourth behind Claude Opus 5.5 (58), Claude Sonnet 5.5 (56) and Muse Wonder Full (56). Moonshot says the model keeps K3's 2.8 trillion total and 104 billion active parameters with a one-million-token context window, cuts token prices by 33 percent, and shows its largest gains on agentic tasks, while cyber-related training data was stripped for the planned open-weights release. A faster K3.1 Pro Highspeed variant is also available through Kimi.ai, Kimi Work, Kimi Code and the Kimi API.

    Why it matters: It quantifies how close an open-weights Chinese model has come to top proprietary systems and documents Moonshot's deliberate removal of offensive cyber capabilities before release.

Oct 22Thu
  1. Hugging Face Blog75

    NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required

    NVIDIA introduces Kumo Relational, a pre-trained relational foundation model that predicts directly on enterprise databases via in-context learning in a single forward pass, requiring no task-specific training, feature engineering, or table flattening. Across 41 tasks on 15 databases, it beats supervised relational deep learning models and expert-engineered gradient-boosted tree pipelines, and on the SAP SALT benchmark it surpasses AutoGluon ensembles by 8% in mean reciprocal rank (MRR). It scales to databases with 500B+ rows and is available through NVIDIA's open-source structured-data-models and kumo-engine libraries or as an NVIDIA NIM.

    Why it matters: Useful for ML teams weighing whether to replace production XGBoost pipelines with a pre-trained relational model, though the reported gains come from NVIDIA's own paper and benchmarks.