Claude Opus 4.7 Released with Improved Performance and Vision

My favorite thing for builders: the new effort parameter. Same model — you just choose how hard it thinks. Crank it to xhigh for long-horizon agent loops and gnarly debugging, step it down when you want latency and cost back. No more guessing at thinking token budgets... just turn the dial. A few other things that landed for API customers: • >3x image resolution (~3.7 MP) — send full-res screenshots now • Adaptive thinking now fully replaces  budget_tokens • 300k output tokens on Batch One model string change to migrate. Would love to hear what you build with it :-)

I used to think I was born too late to take advantage of the internet revolution. “If only I was an adult in the early 2000s I could have built something huge” I no longer have that mindset. 2026 is the year of the builder.

We launched Claude Opus 4.7 today. Benchmarks in the link. What actually matters: it holds the thread. Hand it a 12-thread code review backlog or a multi-day research job, walk away, come back to finished work. Not "done, pending seventeen clarifying questions." Done. For security work, that's the line between a tool you supervise and one you delegate to. The Cyber Verification Program ships alongside it. Safeguards designed in, not bolted on.

okay this is actually exciting 😄 another great drop from Anthropic the long-running tasks + self-checking thing?? i really want to try that out. if it works well, that’s gonna feel like finally not babysitting agents every few minutes also super curious about how “follows instructions better” actually feels in practice. those small improvements usually make a huge difference when you’re building stuff and the vision upgrade… higher resolution inputs sounds like it could be really fun to test on UI screenshots and docs honestly just want to throw a bunch of random real tasks at it and see what happens 😂 definitely playing with this later

Anthropic just dropped Claude Opus 4.7. A few things caught my attention. The headline is coding — and the numbers back it up. Cursor went from 58% to 70% on CursorBench. Rakuten saw 3x more production tasks resolved on SWE-Bench vs. Opus 4.6. Factory reported 10–15% lift in task success with fewer tool errors. These aren't marginal bumps. But what actually interests me is how it's behaving differently: → It verifies its own outputs before reporting back, rather than just declaring done. → It follows instructions literally — which means prompts tuned for older models may need to be rewritten. Loose phrasing that used to "just work" now produces unexpected results. → It pushes back during technical discussions instead of agreeing by default (per Replit's feedback, and matches what I'd want from a teammate). → Vision jumped to ~3.75 MP input — 3x prior models. For anyone doing computer-use agents or parsing dense diagrams, that's a real unlock. A few practical notes if you're planning to migrate: New xhigh effort tier sits between high and max — useful for tuning the reasoning/latency tradeoff on hard problems. Tokenizer changed — same input maps to ~1.0–1.35x more tokens. Worth measuring on your actual traffic before assuming cost parity. Pricing unchanged: $5/$25 per M tokens in/out. The honest take: this feels less like a marketing refresh and more like the kind of release that shifts what you can safely hand off to an agent. Going to spend the next week rebuilding a few of our harder evals around it. Anyone else testing it yet? Curious what you're seeing on long-horizon agentic workflows specifically. https://lnkd.in/gDz6uDfb #AI #MachineLearning #SoftwareEngineering #Claude #LLM

Exciting! Looks like improvements across the board, except for agentic search (but only a marginal regression). It remains to be seen what this model will mean for our token budgets. 😅

Something to note in terms of token usage (tradeoff for performance): Opus 4.7 uses a new tokenizer, contributing to its improved performance on a wide range of tasks. However, it may use roughly 1x to 1.35x as many tokens when processing text compared to previous models (up to ~35% more, varying by content). P.S. Furthermore, the new 'xhigh' effort level is recommended for coding and agentic use cases. Effort level controls how many tokens Claude uses when responding. Anthropic #Claude #Opus #AgenticAI

The smartest model on the market is now powering your store. Claude's Opus 4.7 is live in Tolstoy, meaning the AI behind your content, your ads, and your widgets just got a serious upgrade. Better thinking. Better content. Better conversion rates. Check the comments below to see it in action 👇

Anthropic launches #Claude Opus4.6 to Opus4.7 in about 2 months. Opus 4.5 was launched just 2 months before Opus 4.6. The changes look simple upgrade on the surface, but if you have built systems on previous version(.another point, how many enterprise were able build in 2 months and ship it in production ? #Enterprise #CIOs/ #CTOs need to know what is happening here. and remember upgrade doesn't mean switching models. That is too naive. #Claude already accepted these challenges. here are main differences: 1. #Prompts written for earlier models can sometimes now produce unexpected results. 2. You need to test each flow you built on existing models (if you have systems in production, which i doubt). 3. This model might be advanced, but in your situation, the model is not tested yet. While you can simply switch to new model but output can vary and your #evals might not be effective which you built for previous version. 4. Opus4.7 can consume more token(1.2x+ opus4.6), so your existing flows can become expensive. so the opex cost is rising. 5. do it again because in 2 months you will have a new model. Enterprise need to be very clear on their goal. I wrote whitepaper about 1.5 months ago, every part of the problem i mentioned then, is exactly proving itself here.

The fast pace of AI adoption can be daunting, but here's the truth: You don't need to watch the headlines everyday for your business to benefit form AI. There are still features released months ago that are helping me get clients, like Claude Cowork. But I had to learn how to: Context Engineer Prompt Shuffle Orchestrate That's what dropped 4 HOURS of my lead prep to 20 mins. And helped me close clients masha Allah. What has been your biggest struggle when it comes to adopting AI into your business? #AI #Business #SalesStrategy #MarketingStrategy #LeadGeneration