Claude Sonnet 4.6 vs Claude Opus 4.6

The Short Version

Anthropic dropped both models in early 2026, and the gap between them is smaller than you’d think — but real where it counts. Here’s the gist before we dig into the data.

Side-by-Side at a Glance

Same family, different league. Here’s how they stack up on the fundamentals before we dig into benchmarks.

Benchmark Breakdown

Numbers don’t lie — but context matters. Let’s walk through the key evals, task by task.

Full Benchmark Comparison Table

The complete picture — straight from the official Anthropic release data. Pink highlights in the original indicate the leader per eval.

Fig. 1 — Full benchmark comparison. Sonnet 4.6 (left column, outlined in orange) vs Opus 4.6 and other frontier models. Source: Anthropic, February 2026.

Winners, Eval by Eval

The Computer Use Story Belongs to Sonnet

One of Sonnet 4.6’s biggest talking points is OSWorld — 72.5%, up from Sonnet 4.5’s 61.4%. That’s a significant generational leap. And it’s only 0.2 points behind Opus 4.6 (72.7%), essentially a tie.

For context: when Anthropic launched general-purpose computer use in October 2024 with Sonnet 3.5, the OSWorld score was just 14.9%. In 16 months, that number nearly quintupled. The rate of progress here is the real story.

Fig. 2 — OSWorld and OSWorld-Verified scores for Claude Sonnet models, Oct 2024 → Feb 2026. Progression: 14.9% → 28.0% → 42.2% → 61.4% → 72.5%. Source: Anthropic.

1M Context: Same Window, Different Execution

Both models offer a 1M token context window in beta (200k standard). But effectively using that context is where Opus 4.6 shows its edge.

On MRCR v2 at the 1M token scale — a needle-in-a-haystack test — Opus 4.6 scores 76%. Sonnet 4.5 scored just 18.5% on the same benchmark, showing how dramatically things have improved. On AA-LCR (Long Context Reasoning), they’re tied at 71% — so reasoning after retrieval is comparable. The difference is in retrieval fidelity at extreme lengths.

If your pipeline depends on finding buried details across truly massive documents, Opus 4.6 is the safer bet.

Sonnet 4.6 Can Run a (Simulated) Business

Vending-Bench Arena is one of the more creative evals out there: different AI models compete to run a simulated business and generate the highest profit over a year. Sonnet 4.6 developed a genuinely interesting emergent strategy — heavy early investment in capacity, then a sharp late-stage pivot to profitability.

Fig. 3 — Vending-Bench Arena: money balance over 365 simulated days. Sonnet 4.6 (orange) invests aggressively early, then surges past Sonnet 4.5 (gray) with a late profitability pivot. Final balance: ~$5,600 vs ~$2,100. Source: Anthropic.

This particular matchup is Sonnet 4.6 vs Sonnet 4.5, but it demonstrates the kind of long-horizon planning previously associated with Opus-tier models. The capacity-investment strategy shows Sonnet 4.6 can reason about multi-stage tradeoffs in ways earlier models couldn’t sustain.

The Price-Performance Trade-Off

Opus 4.6 is objectively smarter on most raw benchmarks. But the cost delta is real — and at production scale, it compounds fast.

Both Are Among the Safest Frontier Models Out There

Anthropic ran its most comprehensive safety evaluations to date for the Claude 4.6 generation — new tests for user wellbeing, updated refusal evals, and interpretability experiments. Both models performed well.

The short version on safety: you’re not making a safety tradeoff when choosing between these two. The decision is purely about performance and cost.

Who Should Use What?

The data points to a clear answer for most use cases: Sonnet 4.6 is the right call the majority of the time. Opus 4.6 earns its premium on tasks where deeper reasoning actually changes the output quality — and where you can absorb the cost.