Do LLM routers work? I have some good news for @AnthropicAI and @OpenAI: LLM routers suck. Routers are systems that, given a prompt, choose among a set of LLMs which is the best to answer. The goal is to identify the model that guarantees a high quality response at the lowest cost possible. The problem they promise to solve is simple: why call Gpt Astra on a prompt that a low-cost open source model like @deepseek_ai can answer just as well? A nightmare scenario for the big labs like OpenAi and Anthropic. Well, we applied the @AutobenchAI benchmarking methodology to 7 major routers. Result: always calling a mid-tier model like GPT-5.6 Luna beats every router we tested on both quality and cost. No exceptions. Check out the quality-vs-cost Pareto board. The light shaded zone is the bare minimum prize: better than a random pick and cheaper than a random pick. Luna is already sitting in it. Every router we tested is outside it. It's even worse. The routers that look cheap just collapsed onto one flash model and landed on the random line. The ones that look strong just bought an expensive model. 1/14 👇

Just a quick note on @openclaw after having built 11 agents in 2 multi-agent instances: Julia heads an accounting team of 5, together with Kate (reader), Fulvia (editor), Sophia (analyst), and Flavia (tester); Juno heads an executive staff of 6, together with Venus (comms), Minerva (analyst), Flora (editor), Diana (organizer), Vesta (Q&A). (the 2 teams have just started cooperating via their leaders). These agents are really amazing, but it can cost a lot of $ to get them working properly. Development, operations, and maintenance will dry your premium token budget very fast if you rely on SOTA models (and you don't want to default to budget models, because it takes one wrong call to f.up hard). Here is the key takeaway: smart LLM routing will become increasingly necessary to route the optimal model for each call, with the potential of saving up to 90% (that's because 90% of calls don't require SOTA). Tools like @clawrouter do a decent job, but they use hardcoded rules to route LLM calls. We need smarter light weight AI routers to do the job. This is what platforms like @BotScanner_AI and #AutoBench can help with. And we're working on it. Stay tuned...

Want to try a user-facing version of AutoBench to rank instantly LLM responses to your prompts? Try out our @BotScanner_AI. The platform uses AI to select the best LLM answers for each of your questions. There are still invitation codes with $3 of free credit available for those who want to test it. All you have to do is leave a comment here and follow @BotScanner_AI. We will send you the invitation code with the $3 of free credit. 5/5 end 🧵

🎅𝐌𝐞𝐫𝐫𝐲 𝐀𝐈-𝐌𝐚𝐬 𝐟𝐫𝐨𝐦 𝐀𝐮𝐭𝐨𝐁𝐞𝐧𝐜𝐡🚀 We've got two🎄treats for you: 1. AutoBench 2.0 is LIVE! Our upgraded Collective-LLM-as-a-Judge benchmarking system is more efficient and accurate than ever. 2. Run5 is OUT. Our largest generalist benchmark ever (35 models ranked). Just in time to evaluate @OpenAI GPT 5.2! 1/8👇

🚀Who's the best "AI farmer"? 🌽 Breaking News: AutoBench goes vertical. Introducing our FIRST domain-specific run: Agronomy Edition, in partnership with EVJA. We benchmarked 40 LLMs on real-world farming challenges, from crop diseases to carbon footprints. The outcome? @OpenAI dominates, but the real surprise is @MistralAI. 1/10 👇

🚨 AutoBench 1.0 – Run 4 is LIVE 📷 - 33 frontier models ranked (including GPT-5.1, Gemini 3 Pro, Grok 4.1, Kimi K2 Thinking, etc.) - 21 ranking models - 300+ fresh questions generated - 220,000+ individual rankings This is the most manipulation-resistant evaluation we’ve ever run. And yes… the winner is NOT who most people expected. 1/13

👩‍🔬AutoBench goes scientific!🎉Started 6 months ago almost as a game, then turned into business, now it's got a fancy arXiv paper to prove it's not just fun and money. Introducing the first scientific paper that validates our Collective-LLM-as-a-Judge method!🤖📜 1/12

As anticipation builds for Gemini 3 (rumored to be released by end month!), the latest big news is @AnthropicAI 's release of Claude 4.5 Haiku. 🚀 This is the new fast and "cheap" version of their 4.5 model series, following the Sonnet 4.5 release just a few weeks ago. But let's look at the "cheap" part: While it's ~3x less expensive than Sonnet, at over $1/M input tokens, this new Haiku is actually 25% more expensive than its predecessor. Even with new reasoning capabilities, this highlights a clear trend: proprietary models are steadily increasing their prices. This further widens the gap with high-performing open-source models (many of which are Chinese) that offer comparable results at a fraction of the cost. Speaking of models... you can already find Claude 4.5 Haiku on Bot Scanner, right alongside all the other leading proprietary and open-source LLMs. What? You haven't tried Bot Scanner yet? Our platform uses AI to find and select the best LLM response for your every prompt. We still have 𝐜𝐨𝐝𝐞𝐬 𝐰𝐢𝐭𝐡 $𝟑 𝐢𝐧 𝐟𝐫𝐞𝐞 𝐜𝐫𝐞𝐝𝐢𝐭 available for new users who want to test the platform. All you have to do is leave a comment below and follow @BotScanner_AI. We'll DM you an invite code with your $3 in free credit.