Anthropic released Claude Opus 4.7 today, achieving 91.3% on the SWE-Bench Verified benchmark — the most widely-cited measure of autonomous software engineering capability. The model's release also introduced native support for 200K context windows by default, extended tool-use chains, and improved citation fidelity, positioning it as the current leader among frontier LLMs for agentic enterprise deployment. On the GPQA Diamond benchmark testing graduate-level scientific reasoning, Opus 4.7 scored 76.8%, surpassing competing models on both chemistry and biology subsets.
The architectural improvements center on what Anthropic calls "deep reasoning chains" — an extended compute-on-demand mode that allows the model to self-verify answers across multi-step problems before returning output. Early access developers report the model catches its own errors roughly 40% more often than Opus 4.6, translating to meaningfully better outcomes in production coding workflows. The improvement is particularly visible on tasks requiring sustained attention: debugging 10,000-line codebases, reconciling conflicting documentation across long contexts, and synthesizing research papers with contradictory findings.
Enterprise pricing for Opus 4.7 follows a tiered model: $15 per million input tokens and $75 per million output tokens on standard inference, with cached tokens billed at $1.50 and $7.50 respectively. Anthropic also announced a batch inference endpoint offering a 50% discount for non-latency-sensitive workloads, directly targeting cost-sensitive enterprise pipelines. Several major cloud providers confirmed same-day availability via their managed AI services, eliminating the typical deployment lag that historically disadvantaged Anthropic models in enterprise procurement cycles.