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Model releases Latest

New models from every vendor: flagship updates, open weights released, and capability and price changes.

33 picked26 in the last 30 days68 collected

November 13 07:06 updated

Model releases · Picks · page 2

October 27TuesdayItems 21–33 of 33
  1. Preferred Networks News60

    PFN releases PLaMo 3.1 Prime with Web search and Deep Research

    Preferred Networks began offering PLaMo 3.1 Prime on October 27, 2026, adding Web search and a Deep Research feature that autonomously plans, collects, analyzes and writes source-cited reports over several minutes to tens of minutes. Deep Research also runs on-premises and can connect to internal document search systems, while the company reports an average ~15% score improvement across eight hallucination-related benchmarks including AA Omniscience versus PLaMo 3.0 Prime. PFN also cites 1.7x faster processing via speculative decoding and a 90% reduction in output tokens in non-reasoning mode, and launched a free PLaMo API plan plus a PLaMo Chat campaign offering Deep Research twice daily until November 30, 2026.

    Why it matters: PFN's PLaMo 3.1 Prime adds autonomous Deep Research that runs on-premises, giving Japanese enterprises a locally deployable option for source-cited investigation work.

October 20Tuesday
  1. Mistral AI82

    Mistral releases Voxtral 3 speech models and voice agent SDK

    Mistral released Voxtral 3, a family of speech-to-text and text-to-speech models plus a voice agent SDK built on LiveKit that streams listening, reasoning, tool calling and speaking in one pipeline. Voxtral Small Transcribe 3 covers 50+ languages with diarization and context biasing, Voxtral Mini TTS 3 supports 35+ languages and 50+ dialects, and the stack is available through Mistral's EU-hosted API or self-deployment. Pricing starts at $0.0035/min for batch transcription and $13 per 1M characters for TTS.

    Why it matters: Mistral's Voxtral 3 pairs multilingual speech models with a LiveKit-based voice agent SDK, giving teams a sovereign-deployable option for real-time voice agents.

October 19Monday
  1. MiniMax Blog83

    MiniMax releases M3.1 Flash, claiming 70% lower cost and nearly 3x faster inference

    MiniMax officially rolled out MiniMax M3.1 Flash, a next-generation Flash model that it says cuts usage cost by 70% versus its predecessor and makes online inference nearly three times faster. The company reports 73.7% on DeepSWE v1.1, 76.2 on BankerToolBench, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam and 82% on CyberGym, with output speeds of 200+ TPS and a median of 170+ TPS. Pricing is $0.15 per 1M input tokens, $0.60 per 1M output tokens and $0.015 per 1M cached input tokens, and the model is available through the MiniMax API, M Plan and MiniMax Code, with weights to be released later.

    Why it matters: MiniMax's Flash model claims top-tier agentic and coding scores at $0.15 per 1M input tokens, offering a concrete data point on the cost side of agent workloads.

October 15Thursday
  1. Mistral AI71

    Mistral releases Timestral time-series foundation models, topping GIFT-Eval, TIME and fev-bench

    Mistral released Timestral, its first family of time-series foundation models, in four sizes from 5M to 209M parameters. The largest model ranks first on all three main public benchmarks, GIFT-Eval, TIME and fev-bench, among pre-trained zero-shot models from a single checkpoint without ensembling, using two-thirds of the parameters of the previous state of the art. Timestral produces probabilistic multivariate forecasts with 21 quantiles, supports past-only and known-future covariates, runs on CPU without a GPU, and is available through Mistral Forge for fine-tuning, with a GitHub repository and technical report published under a modified MIT licence.

    Why it matters: Mistral's first time-series foundation model family claims top zero-shot rankings on GIFT-Eval, TIME and fev-bench with a 209M-parameter checkpoint, offering a size-accuracy tradeoff reference for forecasting work.

  2. inclusionAI Repositories67

    inclusionAI releases Ming-3.0-audio-flash and Ming-3.0-audio-tiny audio models

    inclusionAI released Ming-3.0-audio-flash, a 126B-parameter Mixture-of-Experts audio model with 6.5B activated parameters, alongside the compact Ming-3.0-audio-tiny at 9.3B parameters with 2.7B activated. The flash model covers Chinese and English speech recognition across dialects and noisy multi-speaker settings, long-audio understanding, non-speech sound and music perception, audio reasoning, and end-to-end spoken dialogue with optional synthesized speech output. It ships with vLLM-Omni, SGLang, and Transformers inference paths under the MIT License, with default Flash profiles using tensor parallelism across four 80 GB H800 GPUs at 128K context.

    Why it matters: The release details a 126B-parameter MoE audio model with 6.5B activated parameters, plus a 9.3B tiny variant, giving a concrete reference for open audio-language model sizing and deployment.

  3. Meta AI Research Blog88

    Meta releases Muse Spark Open, an 816B open-weight multimodal model that runs on two Mac Studios

    Meta released the weights for Muse Spark Open, an open-weight natively multimodal model with 816B total parameters (42B active) and a 1M-token context window, positioned for coding and agentic work. It is the first model in its class to run on a desktop using two Apple M5 Ultra Mac Studios with the MLX framework, and it is available through API partners including Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, with weights on Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models. Meta says the model ships with auto-tunable Helion GPU kernels in SGLang, delivering 1.03-3.95x kernel-level speedups on NVIDIA and AMD GPUs and a 3% end-to-end speedup on AMD MI350.

    Why it matters: Meta releases open weights for an 816B-parameter multimodal model that runs on two Mac Studios, giving developers a local option for long-horizon coding and agentic tasks.

October 13Tuesday
  1. Every77

    Google DeepMind releases Gemini 4 Argon, returning Google to the frontier

    Google DeepMind released Gemini 4 Argon today, marking Google's first major flagship model upgrade since Gemini 3 Pro last November and a minor 3.1 Pro bump in February. The release follows a summer in which Google shipped four rapid updates to its faster, lower-cost Flash line while the promised Gemini 3.5 Pro stayed in testing, and Every frames Argon as putting Google back in the frontier conversation.

    Why it matters: Google DeepMind releases Gemini 4 Argon, its first flagship upgrade since Gemini 3 Pro, returning Google to the frontier model conversation.

October 12Monday
  1. Meta AI Research Blog82

    Meta releases Muse Wonder and Muse Wonder Full frontier models for personal and professional agents

    Meta introduced the Muse Wonder series, comprising Muse Wonder and Muse Wonder Full, which it says delivers state-of-the-art performance on personal and professional agentic tasks, memory and coding. Muse Wonder is available today through the Meta Model API and Muse Code, while Muse Wonder Full is available today for Muse Code subscribers. The models pair Active Memory, which carries context across conversations and retrieves task-relevant details, with Active Focus, which sustains work on long-running goals and checks in before important actions; Meta says both were trained at its gigawatt-scale Prometheus data center.

    Why it matters: Meta's Muse Wonder series claims state-of-the-art results on agentic, memory and coding benchmarks, with Active Memory and Active Focus as the two capabilities it is built around.

October 6Tuesday
  1. Mistral AI82

    Mistral launches Mistral Large 4 preview, a 1T-parameter multimodal model with weights due this month

    Mistral has launched a public preview of Mistral Large 4, a 1 trillion-parameter natively multimodal model with 52 billion active parameters, available now through the Mistral Studio API. Mistral says the model is competitive with the strongest open-source models and outperforms other open-weight models from the US and Europe, scoring 82% on an Artificial Analysis Cyber Index vulnerability reproduction and patching test and 49.8% on its combined Coding Agent Index. The weights are set to be released by the end of the month, after red-teaming with cybersecurity partners and authorities; Mistral says the model was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in its own European datacenters.

    Why it matters: Mistral's largest model yet claims open-weight leadership in cybersecurity and agentic coding, with weights promised by month's end.

September 28Monday
  1. Anthropic · Claude.dev82

    Anthropic releases Claude Sonnet 5.5 with unchanged per-token pricing and 30% faster output

    Anthropic released Claude Sonnet 5.5, the second model in the Claude 5.5 family after Opus 5.5, which the company says is smarter, more efficient and 30% faster than Sonnet 5 at the same per-token price. Because it typically needs fewer tokens per task, Anthropic says most work costs up to 30% less, and the model ships with a native 1M-token context window, 128k max output and thinking on by default. The release guide lists breaking changes for Sonnet 5 users, including replacing disabled thinking with the between_tools setting, swapping forced tool_choice for auto plus strict tools, and moving computer use to the computer_toolset_20260801 toolset.

    Why it matters: Anthropic's Sonnet 5.5 keeps Sonnet 5 pricing while cutting per-task token use, and the migration notes show what changes for teams already building on Sonnet 5.

August 13Thursday
  1. MiniMax Blog71

    MiniMax releases Music 3.0, an open-weights music generation model for full songs up to five minutes

    MiniMax introduced Music 3.0, an open-weights music generation model that composes, arranges and produces a complete song of up to five minutes from a creative concept and optional lyrics in a single generation. The pipeline pairs an 8B Global LLM initialized from Qwen3.5-8B with a 0.6B Local LLM, fuses their continuous hidden states to condition a 2.4B flow-matching module and a 123M Flow-VAE, and uses eight-layer RVQ plus Structured Captions that describe emotion, instrumentation and vocal delivery over time. MiniMax says this yields more accurate interpretation of creative intent, fuller arrangements and clearer, more natural vocals.

    Why it matters: MiniMax's architecture breakdown shows how an 8B global LLM paired with a 0.6B local model and flow-matching stack targets the long-song coherence problem.

July 31Friday
  1. MiniMax Blog82

    MiniMax launches H3, a general-purpose multimodal generation model with native stereo video at 2K

    MiniMax launched H3, a general-purpose multimodal generation model that understands text, images, video and audio as a unified context and generates video with native stereo sound up to 15 seconds at 2K resolution. The company says 2K output costs less than a third of mainstream models per second, and it plans to open the model weights in the coming days subject to applicable laws and regulations. H3 is built on Contextual Omni Representation, H3-VAE, H3-Omni Transformer and In-Context Regeneration, and MiniMax says a full technical report is coming soon.

    Why it matters: MiniMax's H3 unifies text, image, video and audio generation in one model and claims 2K pricing below a third of mainstream models, with weights promised soon.

June 1Monday
  1. MiniMax Blog87

    MiniMax M3 released with 1M context, frontier coding and native multimodality

    MiniMax released M3, an open-weight model it says is the first to combine frontier coding and agentic performance, a 1M-token context window, and native image and video input. M3 uses MiniMax's new MSA sparse attention architecture, which the company reports cuts per-token compute at 1M context to 1/20 of the previous generation and speeds prefill by more than 9x and decoding by more than 15x. It scores 59.0% on SWE-Bench Pro, 66.0% on Terminal-Bench 2.1 and 74.2% on MCP Atlas, and is available through MiniMax Code, the Token Plan and the API, with weights and a technical report promised within 10 days.

    Why it matters: MiniMax M3 combines a 1M-token context, frontier coding and agentic scores, and native image and video input in one open-weight model, with MSA cutting per-token compute at long context.