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This is huge.
Claude Sonnet 5 is seriously revolutionary.
A Claude model that matches up to the most powerful and intelligent models out there -- yet several times lighter, cheaper, and faster.
It literally dominated every single non-Claude model in several reputable benchmarks -- and shockingly matched up to Opus 4.8.
Only Fable 5 was significantly better in the Arena AI leaderboard — Sonnet 5 is only 10 points behind Opus 4.8 in Thinking mode.
The normal, non-mini version GPT-5.5 could only beat out Sonnet 5 at its absolute maximum thinking effort (xhigh) on the Artificial Analysis Leaderboard:
And it's not just about the massively improved intelligence density or efficiency.
Sonnet 5 also fixes a major issue that many developers have faced when dealing with Claude models.
The self-improving and autonomous ability is on another level.
All this with ultra-competitive pricing that will give us massive savings in token costs.
1. Intelligence autonomous multi-step execution and self-verification
This is one of the biggest upgrades in Sonnet 5.
It's now so much better at carrying out extended sequences of work without constant supervision.
By leaping more than 13 percentage points to hit 80.4% on Terminal-Bench 2.1, Claude Sonnet 5 represents the largest terminal-based engineering jump in the lineup's history, completely closing the gap with the flagship Opus 4.8
Previous Claude models were excellent at planning -- but they still struggled to reliably follow through on complicated workflows involving debugging, testing, and iterative refinement. Sonnet 5 is build from the ground up to stay on track.
The new and improved self-verification is on another level.
It now verifies its own work from top to bottom before returning it.
Instead of generating a fix and hoping it's correct, the model can reproduce a bug, implement a solution, test the fix, temporarily remove it to confirm the issue returns, and then restore the working version -- all without being explicitly instructed to perform each step.
Proactively fixing problems and edge cases you would never have considered.
2. 1,000,000 token context -- but somehow even better
See what enterprise-ready AI support looks like
How are leading teams getting AI support to work? We're breaking down the playbook, live July 9.