# Claude gets more frugal and confusing with Opus 5 > Source: > Published: 2026-07-24 20:57:58+00:00 When it comes to its latest AI models, Anthropic is scaling confusion. On Friday, Claude officially got its fifth-generation Opus model. And while the best news is that it's half the cost of Fable, the bad news is that users are likely to be even more confused about which Claude models they should use. Anthropic says that Opus 5 is a "proactive" model that comes close to Fable 5's frontier intelligence. - The model hit state-of-the-art performance on coding and knowledge work evaluations, but still lags behind Mythos 5 on cybersecurity (but then again, [what model doesn't](https://www.foreignaffairs.com/china/when-china-gets-its-own-mythos)). - The model provides a significant jump in performance from its predecessor, Opus 4.8, which it [released in late May](https://www.thedeepview.com/articles/can-honesty-give-claude-opus-4-8-an-edge). Opus 5 is also much stronger at verifying its work and iterating until success. - Opus 5, however, has the same price tag as its previous generation: $5 per million input tokens and $25 per million output tokens. It's still on the expensive side, since it costs virtually the same as OpenAI's top-tier model GPT-5.6 ($5/$30 per million) and over twice as expensive as similar models from Google Gemini and xAI's Grok. Early testing by companies like Cognition, Cursor, Lovable, Zapier, Box and more found that the model outperformed competitors on things like analytical work, agentic coding tasks and debugging. Sualeh Asif, co-founder of Cursor, said in a statement that Opus 5 delivered "near Fable 5 intelligence at Opus speed and cost," offering many of the same behaviors. Additionally, Anthropic said Fable 5 is its most aligned model to date, adhering to Claude's constitution better than Opus 4.8, Sonnet 5, or Fable 5, with the lowest rates of deception and the least susceptibility to being fooled into misuse. In other words, Anthropic is making the case for Opus 5 as its safest model. It also makes sense that Anthropic is targeting cost and efficiency with Opus 5. AI costs have become a major 2026 pain point for enterprises. Offering ways to trim token budgets has come sharply into focus for tech giants like [Microsoft, Uber, Nebius and Databricks](https://www.thedeepview.com/articles/why-ai-s-tokenmaxxing-obsession-ran-out-of-steam) as the tokenmaxxing fad has sputtered out in favor of efficiency. However, a cheaper and more efficient model may be in Anthropic's favor, too, as the company has struggled to find the compute needed to run [its ultrapowerful Fable 5](https://www.thedeepview.com/articles/why-openai-s-compute-spree-now-looks-prescient), tapering access by only making it available to Max and Team Premium users, and for only 50% of standard weekly usage limits. This adds to the already confusing question of which Claude model to use. Opus used to be Anthropic's flagship tier for Claude models, representing the most powerful and most expensive performance for the hardest questions and tasks. Then came Sonnet, the workhorse with a mix of power and speed, and then Haiku for quickness and cost savings. Mythos emerged as an extra-high tier of performance, especially focused on cyber capabilities. But then it turned out to be a little too powerful and dangerous, and so Anthropic made Fable, which is basically Mythos with the cybersecurity and biological capabilities removed. All of this makes it harder to sort out which model to use, and all of the Mythos/Fable hype has made Opus feel like a mix of Sonnet and Haiku. ## Our Deeper *View* There are multiple benefits to figuring out how to serve up cheaper models. Along with the fact that everyone is cutting costs, Anthropic has been making deal after deal to try and secure the data center capacity it needs to run its compute-hungry Mythos and Fable models as it [plays catch-up to rival OpenAI](https://www.thedeepview.com/articles/why-openai-s-compute-spree-now-looks-prescient), which has been making big data center bets for the past two years. However, the bigger reason to undercut competitors on cost while keeping performance high is that eventually, cost may become the key differentiator as these models commoditize. Though the major AI labs are currently trying to position their models as distinctly better than one another, cost is likely to become a more important factor than performance, especially as these models leapfrog one another with incremental updates. Remember that there's still a huge [capability overhang](https://www.thedeepview.com/articles/openai-names-ai-s-biggest-adoption-problem) that separates what the models can do and what most people are using them for.