Introducing Span-02: anytime reasoning for decisions. Reasoning models make... you pick an effort level and wait. Span-02 takes a deadline and always returns its best decision so far. It gets better the longer it thinks. With reasoning off, it beats Jev at 1/4 of the price, and runs at 1/3 the latency of decision models at its level. It can: - Answer with numbers and scores, not just probabilities and choices - See images and screens (multimodal) - Take a configurable reasoning time And it’s fully open weight!show more

Jev + SERV is actually insane. We already showed... you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.show more

Opus 5.5 works best on 'low' reasoning effort. I... ran some tests as part of a project I'm working on. Opus 5.5 on 'low' reasoning vs. 'max' was at parity on task completion but 12x lower cost! Set 'low' reasoning effort by default and increase it for the hardest problems.show more

GPT-5.1 is now available on Replicate. A smarter, more... conversational bump from GPT-5 with configurable reasoning effort. And it can count fingers.show more

You can now quickly eval GPT-5 and reasoning efforts... across your existing responses. With the built-in grader, compare responses to find the best model and reasoning effort for you. ⚡️👀show more

Crunch just ran its first decentralized peer review, in... partnership with the Eric and Wendy Schmidt Center at the Broad Institute (Eric and Wendy Schmidt Center). Until now Crunch collected thousands of pieces of code. Now the platform can also collect the reasoning behind that code, and have other Crunchers score it. In the Obesity ML Challenge, Crunchers' models ranked 4.47 million candidate gene pairs. Peer review scores the top submissions and helps decide which get tested in the lab. Only about 40 will make it. Every proposal is read and scored by 4 other Crunchers. Reviews are blind, and each score comes with the reviewer's written reasoning. 316 peer reviews in.show more

Opus 4.6 lets you set the effort level Not... every task needs high reasoning. For simpler tasks you can set it to low or medium. Claude thinks less (or skips it entirely), makes fewer tool calls, and gives more concise answers Lower effort = faster responses with fewer tokens!show more

Zero-shot video reasoning (chain-of-frames) isn’t just for Veo3 —... open-source models can understand and edit too! 🕹️ ChronoEdit brings temporal reasoning to image editing. 🔗show more

Introducing o1 web crawler 🕸️ It crawls entire websites... with OpenAI’s new o1 reasoning model and . Just state an objective and it will navigate + return the requested data in a JSON schema. Check it out:show more

what the f*ck someone just took the refusals out... of Kimi K3.1 Pro 😳 Silk Compute put it on an API and they don't store your prompts or replies -1M context -reads text, images and video -tool calling with max reasoning -works with OpenAI and Anthropic SDKs, just change the base URL -model id: kimi-k3.1-pro-uncensored -top up from $5, no subscription one of the strongest open models with no refusals and no logs. I'd put $5 on it and throw the prompts other models refuse at it try it here 👇show more

Codex users: do this right *now*. Max reasoning effort... is off by default. Luna at Max reasoning is ~ Sol Medium / Opus 5 Medium level at 1/6th the cost. Change your life today.show more

Muse Spark is now live on Venice. AI at... Meta’s open-weight reasoning model for code you’d rather keep to yourself. Point it at your repo, wire it into your agents, and run it fully private. Access privately.show more

Our open-weight reasoning models are out, and they're great!... Run them on your desktop and try them in the Codex CLI: `codex --oss`show more

With updated coding and reasoning capabilities, it’s especially strong... at building interactive web apps. What used to take 50+ prompts a year ago with 1.5 Flash (left) now takes <10 with far better quality and much more complex UI (right).show more

China is literally on 🔥 Baidu from China has... launched ERNIE 4.5 and ERNIE X1 and it’s freaking cheap . Here is everything you need to know. ERNIE 4.5 - Native multimodal and Outperforms GPT 4.5 in multiple benchmarks at just 1% of GPT 4.5 price - OpenAI GPT 4.5 – Input: $75 / 1M tokens, Output: $150 / 1M tokens; - ERNIE 4.5 – Input: $0.55 / 1M tokens, Output: $2.20 / 1M tokens ERNIE X1 - A deep thinking reasoning model with multimodal capabilities on par with DeepSeek R1 at only half the price See it in action and check out the pricing details👇 📹 source : yiyan[.]baidu[.]com 1/6 ERNIE 4.5 is a multimodal which can take Audio files as well.show more

NVIDIA Nemotron 3 Nano Omni, a new multimodal reasoning... model, is now live on Jetson AI Lab and unifies vision, audio, and language into a single reasoning loop. 🙌 Power your NemoClaws by running this model with Ollama, vLLM and other inference frameworks on NVIDIA Jetson hardware. Try it ➡️show more

We’ve added a new prompt improver to the Anthropic... Console. Take an existing prompt and Claude will automatically refine it with prompt engineering techniques like chain-of-thought reasoning.show more