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SGLang@sgl_projectAI score23AIAlex Wortega releases openjev, a family of Jev-style models at 0.8B, 2B, 4B, and 35B-A3B for reranking, grading, guardrails, and real-time game decisions. The models come with SGLang serving support, according to the post.
Black Forest Labs@bfl_aiAI score32AIBlack Forest Labs says FLUX 3 Action can control a simulated drone to carry out a specific requested action. The post offers no further details on benchmarks, availability, or implementation.
Black Forest Labs@bfl_aiAI score40AIBlack Forest Labs says FLUX 3 Action takes recent camera frames, the system's current state, and a task description to return the next 32 actions. The model also predicts how the scene will change, and runs a continuous observe-plan-act-adjust loop. It can recover from its own mistakes, according to the company.
Black Forest Labs@bfl_aiAI score40AIBlack Forest Labs says FLUX 3 Action builds on the same image, video, and audio pretraining as FLUX 3 but uses a smaller architecture. The company attributes this smaller size to more efficient representations learned through its Self-Flow research. During midtraining, the model was trained to predict actions and future frames together.
Black Forest Labs@bfl_aiAI score62AIBlack Forest Labs says its FLUX 3 Action, a single-step 7B checkpoint, outperforms every other open policy on RoboLab. It processes each second of robot motion 1.45× to 1.66× faster than Pi0.5, and uses a 2.13-second action horizon versus Pi0.5's 1 second. The company adds that its guidance-distilled checkpoint raises the state-of-the-art RoboLab success rate while running 2.85× to 3.15× faster than the previous leading open WAM.
Black Forest Labs@bfl_aiPickAI score67AIBlack Forest Labs says FLUX 3 Action is an open-weights 7B world action model that ranks first on the RoboLab benchmark. The company says it outperforms the previous best open model by 6.1 percentage points while using 56% fewer parameters and running up to 3.95x faster. The model predicts video and actions together, and the company is releasing the weights, code, fine-tuning recipe, benchmarks, and examples. It also integrated the model into Hugging Face's LeRobot with NVIDIA, with edge deployment on NVIDIA Jetson.
Why it matters: The release pairs benchmark results with the trade-off it claims to remove between world action model performance and VLA speed, which is useful context for robotics teams weighing open models.
Black Forest Labs@bfl_aiAI score30AIBlack Forest Labs has published a blog post and downloadable weights for its FLUX 3 Action model, with links to a Hugging Face collection. The post also invites readers to contact the company's robotics team.
Microsoft AI@MicrosoftAIAI score22AIMicrosoft AI reports its image model rose from 864 to 1147 Elo in nine months, a gain of 377 points per year. The company says that pace is 1.6 times the next-fastest climb on the leaderboard. The post does not name the model.
Google AI Studio@GoogleAIStudioAI score62AIGoogle introduces Gemini 3.8 Flash TTS for creative voice design and Gemini 3.8 Flash-Lite TTS for high-volume, cost-efficient speech generation. Flash TTS supports voice creation from natural language prompts across more than 100 languages and dialects, and both models are rolling out today in the Gemini API and Google AI Studio, with enterprise access coming soon via Gemini Enterprise.
Logan Kilpatrick@OfficialLoganKAI score33AIGoogle has released Gemini 3.8 Flash TTS, a text-to-speech model that costs less than its previous 3.1 Flash TTS model. The new model is accompanied by a new experience in Google AI Studio.
Logan Kilpatrick@OfficialLoganKAI score46AIGoogle introduced Gemini 3.8 Flash and Flash-Lite TTS, new state-of-the-art text-to-speech models. They add a new voice design experience, over 2,000 production-ready voices, voice replication, and support for 100 languages. The models rank first on Hume AI's voice benchmarks, and voice remixing is coming soon.
Google for Developers@googledevsAI score52AIGoogle announced two new Gemini 3.8 text-to-speech models, positioned as its most expressive yet. Gemini 3.8 Flash TTS targets creative work, letting developers use natural language to define vocal personas, cues, pacing, and dialects, while Gemini 3.8 Flash-Lite TTS is built for high-volume pipelines such as bulk audiobook production and audio dubbing. Both are available now through the Gemini API in Google AI Studio.
Google AI@GoogleAIAI score40AIGoogle is rolling out two Gemini 3.8 text-to-speech models starting today: Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS. Developers can access both in Google AI Studio and the Gemini API, while consumers get Flash TTS in NotebookLM and Flash-Lite TTS in Google Vids. Both models are coming soon to Gemini Enterprise.
Google AI@GoogleAIAI score62AIGoogle AI launched Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS, which it describes as its most expressive audio models yet. The models support custom voices across 100+ languages, 2,000+ prebuilt voices, multi-speaker conversations, line-by-line delivery control, and cues such as <laughs> and |mhm|. Google positions Flash TTS for bespoke voices in gaming, audiobooks, and podcasts, and Flash-Lite TTS for near real-time voice agents, high-volume dubbing, and bulk audio.
Google DeepMind@GoogleDeepMindAI score44AIGoogle DeepMind has introduced two text-to-speech models, Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS. Gemini 3.8 Flash TTS lets users design unique voices with distinct accents and characteristics, while Flash-Lite TTS is built for efficiency and scale, offering user-created styles or a production-ready voice library.
Google AI Studio@GoogleAIStudioAI score42AIGoogle introduced Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS, describing them as its most expressive audio generation models yet. The models are designed to help creators, developers, and enterprises build richer, more expressive audio experiences, and can be tried through the Gemini API and AI Studio.
AIGoogle DeepMind introduced Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS, text-to-speech models offering custom voice design, line-by-line performance control, and multilingual support across more than 100 languages. Flash TTS is rolling out to developers in the Gemini API and Google AI Studio and to everyone in Gemini Notebook, while Flash-Lite TTS is available to developers and in Google Vids. Voice replication requires consent verification, and generated audio carries SynthID watermarking.
Why it matters: The source details the voice design, performance direction, and consent safeguards, showing how the model covers creative and high-volume use cases with access across several Google products.
AIGoogle DeepMind's latest Gemini Audio models let developers design new vocal personas from natural language prompts, directing pacing, back channeling, and dialect shifts line by line. Developers can also recreate consistent adult voice profiles from a 30-second audio sample, with built-in consent verification, SynthID watermarking, and C2PA credentials.
ModelScope@ModelScope2022AI score44AINVIDIA's Nemotron 3 Diarization is now available on ModelScope, labeling speakers and timestamps in streaming audio for up to eight speaker slots per conversation. The 99.2M-parameter model uses an end-to-end streaming architecture built on NVIDIA's Streaming Sortformer, running on Ampere, Hopper, and Blackwell GPUs via NeMo Speech C++. It is designed to pair with existing ASR systems such as Nemotron ASR, Parakeet, Canary, or Whisper to produce speaker-attributed transcripts.
ModelScope@ModelScope2022AI score40AITeleOCR, a lightweight 1.2B vision-language model released under Apache 2.0, parses digital PDFs and warped phone photos without a separate dewarping model. It scores 96.87 overall on OmniDocBench v1.6, the highest among listed specialized VLMs, and ranks #1 in the ICDAR 2026 Sci-ImageMiner Challenge. It supports structured parsing of text, tables, formulas, layouts, and reading order, with synchronous or asynchronous vLLM inference.
Qwen@Alibaba_QwenAI score62AIAlibaba's Qwen team released Qwen-Audio-3.1, upgrading its ASR, TTS and Realtime models and adding TTS-Next and ASR-Next. The post says TTS prices fell about 70%, Realtime about 85%, and ASR up to 95%. More APIs are coming soon.
ModelScope@ModelScope2022AI score62AIXiaomi has released MiMo-V2.6 as an open model family under the MIT License, designed for large-scale reinforcement learning. MiMo-V2.6-Pro scores 46 on the Artificial Analysis Intelligence Index, with 71.9 on DeepSWE v1.1, 89.9 on Terminal-Bench 2.1, and 82.0 on OSWorld-Verified. The 1.02T-parameter MoE activates 42B parameters and supports text, image, video, and audio input with a 1M-token context.
AITencent has released a preview of its Hy Image 3.5 image generation model, which product teams across Yuanbao, WorkRally, Ima, and other services are helping refine through co-design. Tencent Cloud prices the model at USD 0.024 per 2K output image, and it supports text-to-image and image-to-image generation with up to five reference images. Tencent said an internal blind evaluation found it on par with ByteDance's Seedream 5.0 Pro and slightly better than Nano-Banana Pro and Qwen-Image-3.0 Pro.
ModelScope@ModelScope2022AI score62AIinclusionAI open-sources the Ming-Image-0.1-Design family, two complementary 6B models for visual-design workflows, under an MIT License. Design generates complete UIs, dashboards, infographics, and posters up to 2048×2048 with native transparent RGBA output, and Layer decomposes flattened graphics into independently editable RGBA layers.
Qwen@Alibaba_QwenAI score38AIAlibaba's Qwen-Image-2.1 is now the top open-source model in both the Image Edit Arena and Text-to-Image Arena, according to Arena. In the Image Edit Arena it scored 1367 points, placing #16 overall and just 3 points behind GPT-Image-1.5-high-fidelity at #15.
AIFireworks Research released Ember-1, a specialized model built on Kimi K3 that it says delivers the same quality with 40% fewer tokens. Across five industry benchmarks, Ember-1 matched K3 max quality at a fraction of the cost, and in two customer A/B tests it used about 35% fewer tokens per task. It is available as a Research Preview on Serverless, and Fireworks is also launching training support for customized models.
Why it matters: The source gives benchmark and A/B results for cutting reasoning tokens while holding quality, which bears on cost planning for coding and agent workloads.
Tri Dao@tri_daoAI score44AIMayank's Rigel, a 2.3B-parameter MoE (360M active) hybrid Mamba-2 model, was pretrained across H100, A100, V100 GPUs and TPU v5p/v6e on one codebase. The model lands within a few points of Llama-3.2-3B while using under 1% of its pretraining FLOPs. Tri Dao praised the work's engineering effort and the model's strength for its small size.
Simon Willison@simonwAI score60AIOpenAI introduced GPT-6 Sol and GPT-6 Luna, which build on advances behind GPT-6 Astra. The company says it cut API prices 50% for Sol and Luna compared with GPT-5.6 promotional pricing, passing on caching and inference efficiency gains. Simon Willison notes GPT-6 Luna costs half of GPT-5.6 Luna and calls Luna his favorite model for building product features because of its cost and speed.
Greg Brockman@gdbPickAI score81AIOpenAI introduced GPT-6 Sol and GPT-6 Luna, which it says bring much of the strength of GPT-6 Astra into faster and more affordable models. The company also reports more efficient caching and inference, with API prices 50% lower than GPT-5.6 promotional pricing.
Why it matters: The quoted announcement names specific pricing and access changes for Sol and Luna, which matter for teams weighing cost against the Astra tier.
Noam Brown@polynoamialPickAI score78AIOpenAI has released GPT-6 Sol and GPT-6 Luna, which it says build on GPT-6 Astra and offer faster, more affordable performance. API prices for Sol and Luna are 50% lower than GPT-5.6 promotional pricing, and Luna now costs $0.10 input and $0.50 output per 1M tokens. The author also notes an earlier 80% Luna price cut at the end of July, with output dropping from $6 to $0.50 within two months.
Why it matters: The source gives concrete API price cuts across two model tiers, making the cost trend across recent releases easy to track for developers.
Sherwin Wu@sherwinwuAI score46AIOpenAI's GPT-6 Luna is priced at $0.10 per 1M input tokens and $0.50 per 1M output tokens, according to Sherwin Wu. Per OpenAI Devs, Luna and GPT-6 Sol launch today with API prices 50% lower than GPT-5.6. Wu jokes that per-billion-token pricing may soon be needed.
ChatGPT@ChatGPTAI score62AIOpenAI announced GPT-6 Sol and GPT-6 Luna, rolling out today in ChatGPT Work and Codex. The rollout covers Plus, Pro, Business, Enterprise, and Edu users.
ChatGPT@ChatGPTPickAI score72AIOpenAI's official ChatGPT account says Free and Go users can try GPT-6 Luna in the desktop app, with rollout starting today. The post links to OpenAI's announcement introducing GPT-6 Sol and Luna.
Why it matters: The post names the access tier and platform for the new model, which is the detail readers need to judge whether it applies to them.
Mike Krieger@mikeykAI score32AIAnthropic says it will launch Claude Sonnet 5.5 and Haiku 5.5 in the coming weeks. The company states both models will bring many of the same improvements to performance, efficiency, and safety.
Mike Krieger@mikeykPickAI score67AIAnthropic has launched Claude Opus 5.5, the first model in its new Claude 5.5 family. According to the quoted launch post, it performs at the level of Claude Fable 5.1 for most tasks and costs 40% less to run than Opus 5. The author says it leads in coding and knowledge work and praises its writing quality.
Why it matters: The quoted launch post gives a concrete cost comparison, useful for weighing Opus 5.5 against earlier Opus and Fable 5.1 models for routine work.
Lydia Hallie ✨@lydiahallieAI score62AIOpus 5.5 is now available, and the author says it is about 30% faster and about 40% cheaper per task than Opus 5. The author describes it as feeling like Fable and invites readers to try it.
cat@_catwuAI score62AIClaude Opus 5.5 is now the default model in Claude Code and the Claude app, including Cowork, for Pro, Max, and Team plans. Anthropic is defaulting to effort medium across products, which it says is comparable to Fable 5.1 on intelligence but faster. Rate limits will go 25% further on Opus 5.5 compared to Opus 5.
Anthropic@AnthropicAIPickAI score71AIAnthropic has made Claude Opus 5.5 available today, introducing it as the first model in its new Claude 5.5 family. According to the quoted @claudeai post, it performs at the level of Claude Fable 5.1 on most tasks and costs 40% less to run than Opus 5.
Why it matters: The post gives a concrete cost comparison against Opus 5 and names the model family, helping readers gauge the trade-off between price and performance.
AIBlack Forest Labs released FLUX 3 Action, an open-weights 7B world action model that outputs robot joint commands from camera frames, robot state, and a text instruction. On the RoboLab-120 benchmark it reports 42.92% task success, ahead of Cosmos3-Nano-Policy at 36.8% and π0.5 at 28.0%. The model is fine-tuned on DROID, is distributed under the FLUX Kommunity License v.1.0, and runs in about 32 GB of GPU memory in bfloat16.
Why it matters: The model card gives a benchmark comparison, parameter counts, and an action contract, so readers can judge how it compares with existing robot policies.
AIBlack Forest Labs has published FLUX 3 Action SO-101 on Hugging Face as an open-weights 7B world action model. It takes two camera frames, the robot state, and a text instruction, then returns the next 42 actions with predicted video frames, with 32 executed at 30 Hz before replanning. The card also provides a rank-32 LoRA fine-tuning recipe for user datasets and states that the application must enforce joint velocity, force, and workspace limits.