OpenSand

OpenSand is officially live 🚀 One API. Multiple AI models. Smarter token usage. Discover OpenSand 👇 🔗 MPost: [mpost.io/opensand-debut…] 🔗 NextFin: [nextfin.ai/en/articles/op…] 🌐 opensand.ai #LLM #AI #Token

Kimi K3 achieved a massive 13 pt increase in the Artificial Analysis Intelligence Index over K2.6 but also comes with a ~3x cost increase While Kimi K3’s Cost per Intelligence Index Task ($0.94) is ~1/2 the cost of Claude Opus 4.8 (max reasoning, $1.80), it is similar in cost to GPT-5.6 Sol (max, $1.04), and is ~3x Kimi K2.6 ($0.33) and Grok 4.5 (high, $0.31)

3 years ago we started as a tiny experiment with the goal of helping you learn faster. Since then, we grew to bring audio, video, and interactivity to your sources, transitioning from a passive workspace to your true research companion. And now, notebooks have even become an entire ecosystem: you can already access them in the @GeminiApp and soon in Google Search So, with these advancements, it’s time for us to evolve once again: NotebookLM is now Gemini Notebook ✨📓 The same app you know and love isn’t going anywhere, we just have an updated name that reflects our role in Google's AI portfolio. And our mission stays exactly the same: helping you learn, faster. Thank you for believing in us— this wouldn't have been possible without your passion (and feature requests...) Big things to come (yes, even folders📂!) so stay tuned. Sincerely, The Project Tailwind team

Claude Code's /code-review now has effort levels, with the review rewritten at every one. Low effort beats other code review tools on findings at a fraction of the token cost. High effort delivers significantly higher recall when you want to go deeper. You pick the tradeoff.

Big news: Kimi-K3 by @Kimi_Moonshot is now #1 in the Frontend Code Arena with 1679 pts, surpassing Claude Fable 5. This is a 17-place jump from Kimi-k2.6 (#18 -> #1). In Frontend, Kimi-K3 ranked #1 in 6 of 7 domains: Brand & Marketing, Reference-Based Design, Data & Analytics, Consumer Product, Simulations, and Content Creation Tools, landing #2 only in Gaming behind Fable 5. The full model weights will be released by July 27. Congrats to the @Kimi_Moonshot team on this major milestone!

Thinking Machines has released Inkling, the new leading U.S. open weights model, debuting at 41 on the Artificial Analysis Intelligence Index @thinkymachines has previously released research previews of models and this is their first production language model release. The model is 975B total parameters, has 41B active parameters, and accepts text, image, and audio input modalities. The model is accessible via Thinking Machines’ Tinker platform API (256K context window) and weights are available on HuggingFace (1M context window). Key results: ➤ Inkling debuts at 41 on the Artificial Analysis Intelligence Index, making it the leading open weights release from a U.S. lab. Inkling scores 3 points higher on the Intelligence Index (41) than the previous leading U.S. open weights model, Nemotron 3 Ultra (38), and also beats Gemma 4 31B (29) and gpt-oss-120b (24) ➤ Inkling stands out on agentic performance. It scores higher than both Kimi K2.6 and DeepSeek v4 Flash on both GDPval-AA v2 and 𝜏³-Banking: Inkling scores an Elo of 1238 on GDPval-AA v2, higher than Kimi K2.6 (1190) and DeepSeek v4 Flash max (1189) and scores 24% on 𝜏³-Banking, higher than Kimi K2.6 (21%) and just above DeepSeek v4 Flash max (23%) ➤ Inkling is token efficient compared to open weights leaders. Inkling averages 25K output tokens per Intelligence Index task compared to 43K, 38K and 37K by GLM-5.2 (max), Kimi K2.6 and DeepSeek v4 Pro (max) respectively ➤ Inkling natively supports image and audio multimodal inputs, a key differentiator among open weights models. Inkling accepts text, image, and audio input modalities. Images and videos are encoded via a hierarchical patch encoder and audio via discrete token encoding, with all modalities projected into a shared hidden space and processed jointly by the decoder Additional model details: ➤ Size: 975B (41B active) parameters ➤ Input modalities: Text, image, and audio (text output) ➤ Context window: 256K tokens on Tinker, open weights model supports 1M ➤ Pricing per 1M tokens (64K context window): $1.87 input / $0.374 cached / $4.68 output ➤ Pricing per 1M tokens (256K context window): $3.74 input / $0.748 cached / $9.36 output

Another reset for our Codex and ChatGPT Work users. Actually hit 9M active users way earlier today, but then got distracted by the approximately millions of things the team is doing to keep the systems up and reliable. Should have that sweet 100% weekly usage limit back in a few minutes. Go be your productive self and close twitter. Shoo!

Search across your chats just got faster and more powerful 🔎 From the sidebar, you can search chats, projects, images, and documents in one place across web, iOS, and Android. Use filters to narrow results, then select anything to open it directly in ChatGPT.

Claude Code artifacts can now call MCP connectors, letting you build dashboards and apps that can fetch information and take actions for each viewer on demand. Available on Pro, Max, Team, and Enterprise plans. Not available on publicly-shared artifacts.

Today, we’re announcing Bonsai 27B: the first 27B-class model to run on a phone. Bonsai 27B is the new multimodal flagship of the Bonsai family. Based on Qwen3.6 27B, it brings a new capability tier to local AI: multi-step reasoning, structured tool use, long-context workflows, and coherent agentic loops. Until now, models in this class have been impractical to deploy locally. A 27B model occupies roughly 54 GB in 16-bit precision, and even a strong 4-bit build is around 18GB - too large for a phone and for most laptops. Bonsai 27B changes that. It comes in two variants: • Ternary Bonsai 27B: 5.9 GB, 1.71 effective bits per weight, optimized for laptop-class quality. • 1-bit Bonsai 27B: 3.9 GB, 1.125 effective bits per weight, optimized for phone-class footprint. Everything is open-sourced today under the Apache 2.0 license.

Alibaba's Qwen-Audio-3.0-TTS-Plus is the new leading model on the Artificial Analysis Speech Arena Leaderboard for Provider Voices, narrowly surpassing Simba 3.2 and ahead of Gemini 3.1 Flash TTS, and Sonic 3.5 Qwen-Audio-3.0-TTS-Plus is Alibaba's latest Text to Speech model, released with increased naturalness and contextually appropriate intonation. The release continues Alibaba's strong momentum across AI model releases, following recent leading launches in language, image, and video generation. Key takeaways: ➤ Quality: Qwen-Audio-3.0-TTS-Plus has an Elo score of 1,236 (+17/-17) based on 1,305 arena appearances, narrowly ahead of Simba 3.2 at 1,234 (+17/-17), with overlapping confidence intervals, and ahead of Gemini 3.1 Flash TTS at 1,214 and Sonic 3.5 at 1,207. ➤ Throughput speed: The model generates 16 characters per second, below other leading models Simba 3.2 (30.2), Gemini 3.1 Flash TTS (27), and Sonic 3.5 (120) ➤ Pricing: Qwen-Audio-3.0-TTS-Plus is priced at $27.59 per 1M characters via Alibaba Cloud Model Studio, below Sonic 3.5 ($39.00/1M), above Gemini 3.1 Flash TTS ($18.31/1M) and Simba 3.2 ($10.00/1M). See more details and listen to samples below ⬇️

7M+ weekly Codex users. 150+ updates in two months. @romainhuet catches you up on what’s new in Codex: GPT‑5.6 and Ultra Parallel work with /goal Faster computer use AppShots Inline edits Sites Codex mobile and SSH workflows PRs from review to merge

We're introducing Claude for Teachers: free access to premium Claude capabilities for verified K-12 educators in the US, with a library of teaching skills and a direct connection to evidence-based curricula, mapped to academic standards in all 50 states. claude.com/solutions/teac…

Gemma 4 is live on @cerebras, the fastest multimodal inference ever! Running on Gemma 4 31B open-weight model at a blistering 1,500+ tokens/sec. That's a 15x speedup, unlocking real-time visual and agentic loops without the GPU lag.

ChatGPT is available again on WhatsApp in the EEA, part of our work to make AI accessible in the apps people already use every day. Message the verified 1-800-CHATGPT contact to ask questions, upload images, send voice notes, create images, and use ChatGPT in many languages. Now also on Kakao in South Korea and Viber in supported markets.

Thank you to the 7M active users who are now using Codex and ChatGPT Work. We have added a banked reset to everyone's account to celebrate the milestone. You can apply the reset in the desktop app or on web and it will replenish the weekly usage for you. Have fun out there.

Google's Gemini Omni Flash debuts at #1 on the Artificial Analysis Text to Video and Image to Video Leaderboards, edging out ByteDance's Seedance 2.0 on both Gemini Omni Flash is the first model in Google's Gemini Omni family, unveiled at Google I/O in May and opened to developers in public preview on June 30. Google positions Omni as a natively multimodal model that can "create anything from any input", starting with video: it accepts text, images, and video as input, generates clips with native audio, and supports conversational editing, where prompts change a video while preserving the rest of the scene. Gemini Omni Flash generates 3 to 10 second clips at 720p and 24 FPS, in 16:9 or 9:16, with longer durations coming soon. In the Artificial Analysis Video Arena, Gemini Omni Flash debuts at #1 on both the Text to Video and Image to Video Leaderboards, narrowly ahead of ByteDance's Seedance 2.0 on each. Gemini Omni Flash is priced at $0.10 per second of generated video ($6.00 per minute), matching Veo 3.1 Fast. The rate is the same for Text to Video and Image to Video. It is available now in the Gemini API, Google AI Studio, and the Gemini Enterprise Agent Platform, in the Gemini app and Google Flow for consumers, and at no cost in YouTube Shorts and the YouTube Create app. Congratulations to @GoogleDeepMind on the release! See below for comparisons between Gemini Omni Flash and other leading models in the Artificial Analysis Video Arena 🧵

the codex pet stuff is incredible. i think labs underestimate how whimsical engineers want their coding experience to be

Updates for Codex and ChatGPT Work users. No nerfing, only good stuff! - We have landed inference optimizations and are passing down savings to all the subscriptions for GPT-5.6 Sol. That should result in around 10% more usage on its own. - We noticed that by changing the context size limit in the product to 372k for GPT-5.6 Sol, up from 272k for GPT-5.5, it resulted in more usage being charged than intended. We have reverted to 272k and will work to roll back out to 372k in the days to come. You should notice that usage drains significantly less after this change. - To understand where the extra usage was coming from, we ran some experiments where reasoning efforts were changed (referred to as juice values under the hood) and have reverted this. - There is slightly more usage of multi-agent than intended in high and xhigh reasoning effort, we are fixing this going forward. Also fixing a small other thing we noticed with auto-review where we can be more efficient. And we continue to have the 5h limit temporarily not apply. Enjoy the rest of the weekend!

@sama We start flying them next year. Maybe you can come see them if your parole officer approves. After stealing an open source AI charity, you then stole all of Apple’s phone technology! Wow. What do you plan for an encore? That’s tough to beat.

Grok 4.5 even ranks slightly above Fable, which is an incredibly good model, on some software benchmarks!