Tasklet's CEO points to pricing as the ultimate proof of an LLM's value. Despite GPT-4o being cheaper, Anthropic's Sonnet maintains a higher price, indicating customers pay a premium for its superior performance on multi-turn agentic tasks—a value not fully captured by benchmarks.
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Instead of competing with OpenAI's mass-market ChatGPT, Anthropic focuses on the enterprise market. By prioritizing safety, reliability, and governance, it targets regulated industries like finance, legal, and healthcare, creating a defensible B2B niche as the "enterprise safety and reliability leader."
For complex, multi-turn agentic workflows, Tasklet prioritizes a model's iterative performance over standard benchmarks. Anthropic's models are chosen based on a qualitative "vibe" of being superior over long sequences of tool use, a nuance that quantitative evaluations often miss.
AI companies operate under the assumption that LLM prices will trend towards zero. This strategic bet means they intentionally de-prioritize heavy investment in cost optimization today, focusing instead on capturing the market and building features, confident that future, cheaper models will solve their margin problems for them.
While AI labs tout performance on standardized tests like math olympiads, these metrics often don't correlate with real-world usefulness or qualitative user experience. Users may prefer a model like Anthropic's Claude for its conversational style, a factor not measured by benchmarks.
In a crowded market where startups offer free or heavily subsidized AI tokens to gain users, Vercel intentionally prices its tokens at cost. They reject undercutting the market, betting instead that a superior, higher-quality product will win customers willing to pay for value.
Contrary to the trend toward multi-agent systems, Tasklet finds that one powerful agent with access to all context and tools is superior for a single user's goals. Splitting tasks among specialized agents is less effective than giving one generalist agent all information, as foundation models are already experts at everything.
OpenAI's new GDP-val benchmark evaluates models on complex, real-world knowledge work tasks, not abstract IQ tests. This pivot signifies that the true measure of AI progress is now its ability to perform economically valuable human jobs, making performance metrics directly comparable to professional output.
A key advancement in Sonnet 4.5 is its work style. Unlike past models with "grand ambitions" that would meander, this AI pragmatically breaks down large projects into small, manageable chunks. This methodical approach feels more like working with a human colleague, making it more reliable for complex tasks.
The AI value chain flows from hardware (NVIDIA) to apps, with LLM providers currently capturing most of the margin. The long-term viability of app-layer businesses depends on a competitive model layer. This competition drives down API costs, preventing model providers from having excessive pricing power and allowing apps to build sustainable businesses.
