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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...

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217,916 Aufrufe • vor 11 Tagen •via X (Twitter)

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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.
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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.

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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.
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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.

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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.
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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. ⚡️👀
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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.
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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!
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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.
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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.
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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.

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Can “chicken” make a base model reason? 🐔 Yes! Why? With the right first tokens, base models can match RL in reasoning. We trace this to learned training data associations and use a simple data edit to make “chicken” elicit reasoning too. blog:
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