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November 10Tuesday
  1. Every65

    Review: Gemini 4 Carbon's coding outpaces its judgment

    Every's hands-on review finds Gemini 4 Carbon, released today by Google DeepMind, is a much stronger coder than Gemini 4 Argon and can build better 3D objects and work for hours with little supervision. Testers disagree on how much supervision it needs, and the review says Anthropic's Fable 5.2, released a day earlier, has raised expectations that a flagship agent also pick the right work, respect limits and know when it is finished. Google gave Every early access and had no input on the review.

November 9Monday
  1. Zyphra Research78

    Zyphra releases ZONOS3-Mini, an Apache 2.0 zero-shot voice-cloning TTS model that runs on-device

    Zyphra released ZONOS3-Mini, a sparse MoE text-to-speech model with 100M active and 800M total parameters under Apache 2.0, trained on 112 NVIDIA H100s in 2.5 days. It reaches under 570 ms time-to-first-audio in the browser on a Pixel 9 and under 150 ms on an M3 MacBook Air, with runtimes for WebGPU, SGLang, vLLM and GGML. The model adds control over identity, accent, quality, mood and delivery, including word-level steering, and Zyphra reports it ranks first among open models on its new ZTTS2-Eval benchmark.

    Why it matters: Zyphra's compact MoE TTS model pairs zero-shot voice cloning with word-level delivery control while running in real time on phones and laptops.

  2. Meta AI Research Blog78

    Meta releases Muse Ember, its smallest and most efficient model for agent tasks

    Meta released Muse Ember, its smallest and most efficient model, aimed at the repetitive work agents do all day such as tool calls, coding subtasks, routing, extraction and long document parsing. Meta says it matches Claude Haiku 5.5's best score at about a third of the cost and beats every GPT-6 Luna setting, at $0.10 per million input tokens and $0.50 per million output tokens. It ships with a 1M-token context window, up to 512K output tokens, native text, image, audio and video input, and is available today on Muse Code and the Meta Model API as muse-ember-1.0.

    Why it matters: Meta's smallest model targets the high-volume agent steps, and its cost-per-task comparison against Claude Haiku 5.5 and GPT-6 Luna shows where the efficiency claim comes from.

  3. Every78

    Every review: Fable 5.2 can build a full product with little steering

    Every reports that Fable 5.2, Anthropic's newest model, let Kieran Klaassen build a video editor combining Adobe Premiere, After Effects and Ableton-style features, with an AI agent running it, and Everycut went live three days after he started. The review says Fable 5.2 can take a whole product and keep building, testing and refining until it is usable daily, unlike Opus 5.5, which keeps the user checking its work, and Fable 5 and 5.1, which run large tasks for hours or days. Every calls it the first model it has tested that lets professionals build production-grade tools without reading the underlying code.

    Why it matters: Every's hands-on account of Fable 5.2 shows how far a single model can carry a product build with minimal steering, offering a concrete comparison against Opus 5.5 and Fable 5/5.1.

  4. Artificial Analysis Articles71

    Meta's Muse Ember is the cheapest model scoring 36 on the Artificial Analysis Intelligence Index

    Meta's Muse Ember scores 36 on the Artificial Analysis Intelligence Index, matching Claude Haiku 5.5 (max) at $0.14 per task versus $0.41 for Claude Haiku 5.5. It stands out on AutomationBench-AA at 68.1% and scores 46 on the Coding Agent Index in Muse Code, ahead of Claude Haiku 5.5 in Claude Code (37) and GPT-6 Luna in Codex (31). Its main weakness is knowledge: AA-Omniscience accuracy of 21.1% versus 34.7% for Claude Haiku 5.5, largely because it declines to answer 64% of questions, which holds its hallucination rate to 17.4%.

  5. Preferred Networks News55

    Preferred Networks releases PLaMo 3.0 Fin finance-specialized small language model as open weights

    Preferred Networks released PLaMo 3.0 Fin, a finance-specialized small language model built on the PLaMo 3 NICT pretraining model from joint research with NICT and further trained on Japanese financial data, publishing open weights on Hugging Face under the PLaMo Community License. The model adds step-by-step Reasoning with reasoning traces output in Japanese, plus tool calling for web search, calculation and internal database access, and can run on a single GPU in closed cloud or on-premise environments. PFN says the instruction-following training for Reasoning and tool use was carried out on its own MN-Core 2 AI chip, with code porting completed within a week and training speed comparable to a GPU environment.

November 6Friday
  1. Google DeepMind78

    Google DeepMind releases Gemini Omni 1.2 Flash with 15-second clips and 30% lower price

    Google DeepMind rolled out Gemini Omni 1.2 Flash, now generally available across the Gemini API and Gemini for business, with native 15-second video generation, multi-turn conversational editing and stronger reference following. The price drops 30% from Gemini Omni 1.1 to $0.07 per second for 720p video, and users can draft at 360p or 720p before upscaling to 1080p or 4K. Clips can be set from 3 to 15 seconds in one-second increments, and outputs carry an imperceptible SynthID watermark.

    Why it matters: Gemini Omni 1.2 Flash raises native clip length to 15 seconds and cuts the price 30% to $0.07 per second of 720p video, with multi-turn conversational editing now generally available.

  2. Google Blog · AI80

    Google releases Gemini Omni 1.2 Flash with 15-second clips and a 30% price cut

    Google DeepMind rolled out Gemini Omni 1.2 Flash and made it generally available across the Gemini API and Gemini for business. The model generates and edits native clips up to 15 seconds, up from 10 seconds in Omni 1.1, supports multi-turn conversational editing, and holds character, product, and style references more consistently; 720p video now costs $0.07 per second, 30% less than Omni 1.1. Outputs carry a SynthID watermark, and the model is rolling out in Flow, Google Vids, the Gemini app, and Google Ads.

    Why it matters: Gemini Omni 1.2 Flash cuts the price 30% to $0.07 per second for 720p video while extending native clips to 15 seconds, giving teams producing video at scale a cheaper option to compare against prior versions.

  3. Meta AI Research Blog60

    Meta brings specialized Muse and Meta AI models with Active Memory and Active Focus

    Meta is rolling out specialized versions of its existing models, trained using Muse Wonder, to people who use Muse and Meta AI. The specialized models add Active Memory and Active Focus, and Meta reports the current Muse model scores 47% on its Personal Superintelligence Index versus 38% for the previous model, 90% versus 76% on the Active Memory eval, and a 67% improvement in completing everyday tasks where unexpected issues arise.

    Why it matters: Meta's specialized Muse and Meta AI models bring Active Memory and Active Focus to more users, with reported gains on memory and task-completion evals.

November 5Thursday
  1. Preferred Networks News71

    PFN releases PLaMo 3 Translate open-weight translation LLM for edge devices

    Preferred Networks released PLaMo 3 Translate, a translation-specialized LLM used in its PLaMo Translation service, as an open-weight model on Hugging Face on November 5. 8B, 2B, 8B and 31B) and supports 53 languages including Japanese and English, running on smartphones, laptops and internal servers without a cloud connection.

    Why it matters: PFN releases a translation-specialized LLM in four sizes from 0.8B to 31B, letting companies and individuals run 53-language translation on-device without cloud access.

  2. Artificial Analysis Articles78

    Thinking Machines releases Inkling 2 Small, scoring 31 on the Artificial Analysis Intelligence Index

    Thinking Machines released Inkling 2 Small, a proprietary reasoning model that scores 31 on the Artificial Analysis Intelligence Index v5.0.1, 18 points above Inkling Small, with Apache 2.0 weights planned for a later, unconfirmed date. The 594B-parameter model (17B active) is available first through Tinker and Tinker Playground at $0.30/$1.00 per 1M input/output tokens, and it matches Gemini 3.8 Flash (high) while trailing Claude Sonnet 5.5 on coding and hallucination benchmarks. It averages about 150K output tokens per Intelligence Index task, roughly 4x Inkling Small, at $1.18 per task versus $0.20.

    Why it matters: Artificial Analysis's independent benchmark run shows where Inkling 2 Small gains over its predecessor and where it still trails Gemini 3.8 Flash and Claude Sonnet 5.5.

  3. Artificial Analysis Articles58

    Nanbeige releases Nanbeige4.3-33B, scoring 20 on the Artificial Analysis Intelligence Index

    Nanbeige has released Nanbeige4.3-33B, an open-weights dense reasoning model under Apache 2.0 that scores 20 on the Artificial Analysis Intelligence Index v5, matching Qwen3.8 27B (xhigh) at 20. The 33B model has a 524K-token context window, text-only input and output, and no confirmed public APIs, and it scores 30% on Terminal-Bench 4.0 versus 6% for Qwen3.8 27B while trailing on HLE-Diamond (7% vs. 15%). On AA-Omniscience v1.1 it records an 18% hallucination rate versus 29% for Qwen3.8 27B, with a 71% decline rate, and it uses 158K output tokens per Intelligence Index task versus 110K for Qwen3.8 27B.

October 29Thursday
  1. Mistral AI86

    Mistral releases open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter multimodal model

    Mistral AI released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that entered public preview earlier this month and was trained from scratch in Europe. The company says three additional weeks of reinforcement learning lifted it to the top of open-weight coding, with 41% on Terminal-Bench 4.0 and 48% on the Artificial Analysis Coding Agent Index while using 47% fewer tokens per task than GLM-5.3. It adds Low, Medium and High reasoning modes, scores 51% on the Artificial Analysis Cyber Index, and is priced at $0.70 input, $0.07 cached input and $2.10 output per million tokens under a modified MIT license.

    Why it matters: Mistral opens the weights of its 1-trillion-parameter flagship, pairing coding and defensive-security benchmark gains with three reasoning modes and per-token pricing.

  2. inclusionAI Models34

    inclusionAI Releases Ling-3.0-flash-Sante-fp8, an FP8-Quantized Healthcare Model

    inclusionAI released Ling-3.0-flash-Sante-fp8, an FP8-quantized version of its healthcare-specialized Ling-3.0-flash-Sante model, keeping 124B total and 5.1B active parameters per token. It matches the BF16 checkpoint's 73.9 average across healthcare benchmarks while cutting weight memory for FP8-capable hardware. The research-only model supports 256K-token contexts and defaults to thinking mode with tool calling.

  3. Google DeepMind82

    Google DeepMind announces Gemini 4 Carbon, a frontier model focused on cybersecurity and long-horizon tasks

    Google DeepMind announced Gemini 4 Carbon, its strongest model yet, with state-of-the-art cybersecurity and long-horizon capabilities, ranking #1 on the v5.0 Artificial Analysis Intelligence Index at 59. It scores 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation and 71.0% on CWE-bench for patching, and reaches #4 (1768) on WebDev Arena. Carbon launches at the same introductory price as Argon, $2 per million input tokens and $10 per million output tokens, and is first available to a subset of cyber defenders through the Fairwind Program before wider release.

    Why it matters: Gemini 4 Carbon's benchmark results in vulnerability discovery and patching show how frontier models are being positioned for cybersecurity defense work.

October 28Wednesday
  1. Artificial Analysis Articles82

    Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5, ranks second on Coding Agent Index

    Google's Gemini 4 Carbon is the new No. 5 on the Artificial Analysis Coding Agent Index. 5 (max, 62), and it leads 4 of the 9 Index evaluations including HLE-Diamond and Terminal-Bench-Science. 1 rises from 4% to 11%; the model is being rolled out to selected users and is not yet publicly available.

    Why it matters: Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5 at 59 and trails Claude Opus 5.5 by 3 points on the Coding Agent Index, with per-task cost and hallucination figures for comparison.

October 27Tuesday
  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 22Thursday
  1. Ai2 · Allen Institute for AI58

    Ai2 releases OlmoEarth v1.3 satellite embedding model with global precomputed embeddings and a browser explorer

    Ai2 released OlmoEarth v1.3, a satellite embedding model that turns a year of Sentinel-1, Sentinel-2 and Landsat time series into one 128-dimensional vector per 10-meter pixel, with the first 64 dimensions usable as a smaller embedding. Ai2 precomputed annual embeddings for the whole planet and published them on Source Cooperative, and its 128-dimensional embeddings scored highest on 20 of 27 evaluations against AlphaEarth Foundations and Tessera v2, including a new tropical crop benchmark called PLANTEUR. The release also includes the OlmoEarth Embeddings Explorer, a no-sign-in browser tool for comparing years, finding similar places and training small classifiers on the spot, with 2025, 2024 and 2023 global and 2017 to 2022 covering select countries; weights, pretraining data and code are open.

  2. inclusionAI Models58

    inclusionAI releases Ling-3.0-flash-Sante, a 124B healthcare-specialized MoE model

    inclusionAI released Ling-3.0-flash-Sante, a healthcare-specialized model built on Ling-3.0-flash with 124B total parameters and 5.1B active parameters per token, with weights on Hugging Face and ModelScope. It uses a hybrid-linear MoE architecture combining Kimi Delta Attention, gated MLA and sparse MoE layers, trains to 256K tokens, and enables thinking mode by default with tool calling through compatible serving frameworks. The company labels it a research model, not a medical device, and says outputs must not replace professional medical advice, diagnosis or treatment.

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. inclusionAI Repositories58

    inclusionAI releases Ming-3.0-tts speech synthesis models in 0.5B dense and 7.9B MoE tiny variants

    inclusionAI released Ming-3.0-tts, an open-weight multilingual speech synthesis family with two models: Ming-3.0-tts-0.5b, a 0.5B dense model, and Ming-3.0-tts-tiny, a MoE model built on Ling V3 with 7.9B total and 1.3B active parameters. Both ship with inference code, a task cookbook and benchmark tables, and support voice cloning, text-based voice design, free-form natural-language delivery control, 14 inline paralinguistic tags, emotion and speed/pitch/volume control, plus Chinese, English, German, Spanish, French, Korean, Japanese, Sichuanese and Cantonese. On the October 2026 Artificial Analysis text-to-speech leaderboards, Ming-3.0-tts ranks 1st among open-weights models on Controlled Voice, 1st overall on Pronunciation Robustness at 93.2%, and 3rd among open-weights models on Provider Voice.

  2. inclusionAI Repositories58

    inclusionAI releases Ming-3.0-asr-tiny context-aware speech recognition model

    inclusionAI released Ming-3.0-asr-tiny, a speech recognition model that pairs the Ling-tiny-v3 language model with an audio encoder for Chinese and English transcription, dialect recognition and context ASR. Dialect evaluation covers 15 test sets including Hunan, Minnan, Cantonese, Chuanyu and Shanghai, and hotwords or preceding dialogue can be supplied in the prompt to resolve names and technical terms. The repository ships Transformers and SGLang serving guides, and code and weights are released under the MIT License.

  3. 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.

September 22Tuesday
  1. Every75

    Vibe Check: GPT-6 Sol vs. Claude Opus 5.5

    Every published a Vibe Check comparing OpenAI's GPT-6 Sol and Anthropic's Claude Opus 5.5, both released the same day, to help readers decide which model to use. The comparison draws on the outlet's own experience and evaluations of both models on its daily work, and the full article is available to subscribers.

August 24Monday
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.

July 13Monday
  1. Vapi Blog58

    Vapi Humanness Index: Grok TTS scores 95 and MiniMax Speech 2.5 scores 93 against a real human at 100

    Vapi's Humanness Index, a blind crowdsourced leaderboard where listeners pick which of two cloned voices sounds more human, places xAI's Grok TTS at 95 and MiniMax Speech 2.5 at 93 against a real human recording scored at 100 after more than 11,000 votes. Every battle uses the same cloned voice and script across models, and the real human recording is included in the comparison set, so a 95 means listeners chose Grok TTS at nearly the same rate as the human on the same quote. Vapi argues naturalness is no longer the bottleneck for voice agents, since callers experience the voice rather than the reasoning behind it.

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.