We're bringing the advisor strategy to the Claude Platform. Pair Opus as an advisor with Sonnet or Haiku as an executor, and your agents can consult Opus mid-task when they hit a hard decision. Opus returns a plan and the executor keeps running, all inside a single API request. This brings near Opus-level intelligence to your agents while keeping costs near Sonnet levels. In our evals, Sonnet with an Opus advisor scored 2.7 percentage points higher on SWE-bench Multilingual than Sonnet alone.
We're announcing GPT-4 Omni, our new flagship model which can reason across audio, vision, and text in real time.
This isn't just better chatbots — GPT-4o can literally see your screen, hear your frustration, and respond instantly without the awkward text-to-speech delays
Anthropic announced on Monday (4/20) a major long-term partnership with Amazon, committing to spend over $100 billion on AWS computing power over the next decade while securing up to 5GW of computational capacity for training and deploying Claude models. The companies are also expanding their capital partnership, with Amazon investing $5 billion upfront and potentially adding up to $20 billion more based on business milestones.
5GW is enough power to run a small city, and Anthropic just locked it up for a decade — that's scarcity economics in action
OpenAI raises $122 billion in new funding to expand frontier AI globally, invest in next-generation compute, and meet growing demand for ChatGPT, Codex, and enterprise AI.
This $122B isn't just about ChatGPT — it's OpenAI betting they can replace entire software categories with AI-first solutions
OpenAI outlines the next phase of enterprise AI, as adoption accelerates across industries with Frontier, ChatGPT Enterprise, Codex, and company-wide AI agents.
OpenAI is positioning company-wide AI agents as the new normal, not just chatbots for customer service
Tech Blogger Take
Anthropic just solved the 'smart AI is too expensive' problem. This changes everything.
Anthropic dropped something quietly brilliant today: the advisor strategy for Claude. Here's how it works — you pair their smartest model (Opus) as an 'advisor' with their faster, cheaper models (Sonnet or Haiku) as 'executors.' When your AI agent hits a tough decision mid-task, it consults Opus for a plan, then the cheaper model keeps executing. All in one API call. The result? Near-Opus intelligence at near-Sonnet prices. This isn't just a nice optimization — it's the solution to the fundamental problem that's been strangling AI products: you either get smart AI that costs too much to run at scale, or cheap AI that's too dumb for anything important. Anthropic's early results show Sonnet with an Opus advisor scored 2.7 percentage points higher on coding benchmarks than Sonnet alone. That might sound small, but in AI benchmarks, that's the difference between 'occasionally helpful' and 'actually reliable.' Every AI product manager who's been doing unit economics math on GPT-4 pricing just felt their heart skip a beat.
Action
馬上試用Choosing between expensive AI that's too costly to scale or cheap AI that makes too many mistakes
Getting near-premium intelligence at budget-friendly prices with strategic consultation only when needed
AI Analysis
Software Development
highStart experimenting with advisor-executor patterns in your CI/CD pipelines where code review quality matters more than speed
That 2.7 percentage point SWE-bench improvement might sound small, but in coding benchmarks, that's the difference between 'decent' and 'actually useful' AI assistance
Your next code review could catch bugs that would've cost you a weekend debugging in production
AI Product Development
highRedesign your agent workflows to use this advisor pattern instead of always defaulting to the most expensive model
This is basically Anthropic saying 'we figured out how to make GPT-4 pricing work for production workloads' — that's a game changer for AI product economics
You can finally build AI features that don't bankrupt your startup when they actually get used
Job Impact Analysis
AI Engineer
Role ShiftThis advisor pattern solves the classic 'smart but expensive vs fast but dumb' trade-off that's been killing AI product margins
Audit your current model usage and identify workflows where you're overpaying for intelligence you only need occasionally
DevOps Engineer
OpportunityNear-Opus intelligence for automated code analysis and deployment decisions, but at Sonnet-level costs that won't blow your infrastructure budget
Test this on your most error-prone deployment pipelines where human judgment calls currently bottleneck releases
Product Manager
OpportunityThe cost-intelligence balance finally makes sense for customer-facing AI features that need to be both smart and scalable
Revisit those AI feature ideas you shelved because the unit economics didn't work with premium models