Claude Sonnet 5 is now generally available in GitHub Copilot. The rollout is gradual, so you may not see it in the model picker immediately, but it is live today for Copilot Pro, Pro+, Max, Business, and Enterprise subscribers. This is not just a routine model swap: Sonnet 5 is Anthropic's biggest leap in the Sonnet line in terms of agentic capability, and it lands at a moment when the entire coding assistant market is racing to make autonomous, multi-step workflows the default.
Opus-class power, Sonnet-class price
The headline story from Anthropic is the performance gap it closes. In GitHub's internal testing, Claude Sonnet 5 showed strong results across a range of coding scenarios, including particularly strong performance on CLI-style tasks. It also demonstrated excellent prompt-cache utilization and competitive latency at lower effort levels, making it a strong choice for developers who want fast, capable Sonnet-class performance in Copilot.
Anthropic describes it as their most agentic Sonnet yet, with top-tier intelligence for coding and everyday professional work. More concretely, its performance is close to Opus 4.8 and represents a notable improvement over Sonnet 4.6 -- Anthropic specifically notes that it performs much better on tasks involving reasoning, tool use, software coding, and knowledge work.
A key benchmark to know here: on one agentic coding benchmark, Sonnet 5 scores 63.2%, compared to Opus 4.8's 69.2% and Sonnet 4.6's 58.1%. That gap between Sonnet 5 and Opus 4.8 is now small enough that for most everyday tasks, you will not notice the difference -- but you will notice the price.
What makes it different from Sonnet 4.6
The biggest shift is not raw benchmark numbers, it is follow-through. Feedback from early access partners was consistent: Sonnet 5 is much more agentic than its predecessors. Testers described how it finishes complex tasks where previous Sonnet models would stop short, how it checks its own output without explicitly being asked, and how it handles sustained coding, tool use, and debugging well across messy technical contexts.
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