There's a question we all ask when setting up @OpenClaw: which model should I actually use?

What are your suggestions? Any preferences?

In a notable shift in the AI landscape, Apple and Google have announced a multi-year collaboration under which Apple s next generation of Apple Foundation Models will be based on Google s Gemini models and cloud technology.

According to the joint statement, these models will help power upcoming Apple Intelligence features, including a more personalized Siri, expected later this year.

New AI models pop up every week. Some developer tools like @Cursor, @Zed, and @Kilo Code let you choose between different models, while more opinionated products like @Amp and @Tonkotsu default to 1 model.

Curious what the community recommends for coding tasks? Any preferences?

Yesterday, Meta announced that they have released a new collection of AI models, Llama 4, in its Llama family.

(It consists of Llama 4 Scout, Llama 4 Maverick, and Llama 4 Behemoth.)

Historically, Open AI with its ChatGPT has been on the market for the longest period.

I ve been testing the 'Free Tier' limits of the 2026 AI landscape. While everyone swears by Claude 3.7 or GPT-5.2, I m trying to find the 'Golden Ratio' for makers on a zero-budget.

My current findings for the Office Bee MVP:

• The Brain: Gemini 3.1 Pro (via AI Studio) seems to have the highest 'Reasoning-per-Dollar' (free) for deep R&D.

• The Frontend: v0 (Free Tier) for shadcn/ui components.

• The Glue: Bolt.new for the initial scaffold.

The Challenge: Most 'free' models hallucinate complex state management in full-stack architectures.

Raycast keeps adding new models every week.

I use @Anthropic's Claude 3.7 Sonnet when coding and @OpenAI's o3-mini when speed matters. Curious what works best for the community.
Over to you! What model are you using and why?

Edit 2025-07-30: Raycast just introduced Auto Models. "Let AI choose the best model for the job at hand." Join the discussion here 

Last week, @Gemini 3. Today, @Claude by Anthropic Opus 4.5.

Embraced by the community on X, did Anthropic win the AI coding race?

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There's a lot of options for models these days. I've been using Claude 3.7 but I'm curious what's been working well for others. What model are you using and why?

Is the thinking version of @Claude by Anthropic worth the extra credit spend? Does @DeepSeek work well enough to save some credits? Is it worth trying any of the @ChatGPT by OpenAI models?

There's a lot of options for models these days. I've been using Claude 3.7 but I'm curious what's been working well for others. What model are you using and why?

Is the thinking version of @Claude by Anthropic worth the extra credit spend? Does @DeepSeek work well enough to save some credits? Is it worth trying any of the @ChatGPT by OpenAI models?

Hi Founders,

I excited to share that I have built ArchitectGBT - Find Your Perfect AI Model and now its BETA live. ArchitectGBT is an AI-powered tool that takes the guesswork out of LLM selection.

Describe your project in natural language Get instant recommendations ranked by speed, cost, and accuracy for your specific use case.

One of the biggest pain points in AI chatbots has been their forgetfulness having to repeat the same context over and over again. AI memory aims to solve this by allowing models like ChatGPT and the newly launched Gemini to retain past interactions.

But how well do these memory features work? Which AI ChatGPT or Gemini handles memory better? And more importantly, does AI memory provide more value in personal use or enterprise settings?

On their livestream today, OpenAI just released a bunch of new tools for reliably building and using AI agents. From what I can tell, this is what's new-

New APIs:

• Responses API - a new multi-modal API that builds on chat completions to allow for the next-generation of tool calling, starting with the new tools announced today.

Anyone building voice AI agents knows how hard it is to stay up-to-date with the latest text-to-speech voice models.

We spend time testing and experimenting with all of the available paid and open-source text-to-speech voice AI models and consolidated our own notes and experience testing different models into a single guide for developers evaluating multiple models.

Anyone building voice AI agents knows how hard it is to stay up-to-date with the latest text-to-speech voice models.

We spend time testing and experimenting with all of the available paid and open-source text-to-speech voice AI models and consolidated our own notes and experience testing different models into a single guide for developers evaluating multiple models.