Long-context reasoning
Handles up to 1 million tokens of input in a single request — roughly 750,000 words. Useful for working over entire codebases, legal contracts, or research libraries in one go.
Anthropic's flagship reasoning model, built for production AI agents that take multi-step actions and need to fail safely. Long-horizon work, structured tool-use, and the cleanest refusal behavior in the frontier tier.
Claude Opus 4.7 is the most capable model in Anthropic's Claude family, released in January 2026. It is a large language model designed for reasoning, tool-use, and long-running agentic tasks — workflows where the AI plans, calls external tools, and verifies its own work over many steps, rather than just answering single questions.
Like earlier Claude releases, Opus 4.7 focuses on safety, honesty, and graceful failure: when it doesn't know an answer, it says so; when an action looks risky, it stops and asks. This makes it a common choice for production agents in healthcare, finance, legal, and customer support — areas where wrong actions have real consequences.
It supports a 1 million-token context window, native tool-use via the Model Context Protocol (MCP), and is available through Anthropic's own API, AWS Bedrock, Google Cloud Vertex AI, and Cloudflare Workers AI.
Handles up to 1 million tokens of input in a single request — roughly 750,000 words. Useful for working over entire codebases, legal contracts, or research libraries in one go.
Calls external tools (CRMs, databases, APIs) in a structured, validated format. Supports parallel tool calls and the Model Context Protocol (MCP), so connectors stay portable when you swap models.
Plans complex tasks, executes them step by step, and verifies its own work. Tested on 30+ tool-call agent workflows without drifting off-policy.
Reads, writes, and refactors code in 30+ programming languages. Leader on the SWE-bench Verified benchmark for real-world coding tasks pulled from GitHub.
Reads and reasons over images, screenshots, charts, and diagrams. Useful for document understanding, UI testing, and image-based search — though it does not generate images.
Resolving complex tickets that span CRM lookups, billing checks, and policy decisions — with escalation only when needed.
70%tickets resolved end-to-endReading photos, policy data, and adjuster notes in one pass; routing risk-flagged claims to humans automatically.
~60saverage decision time per claimProduction coding assistants that read existing codebases, modify large diffs, and write tests — ranked first on SWE-bench Verified.
72%SWE-bench Verified pass rateReading 500-page contracts in a single context window, extracting clauses, and surfacing risks with citation grounding.
1 passentire contract in a single context windowSummarising patient charts, drafting visit notes, and flagging risks across long medical histories — with strict refusal on out-of-policy advice.
~30 minsaved per clinician per day on documentationResearching prospects across their website, news, and filings — then drafting personalised outreach with citation-backed talking points.
3–5×deeper research per account, per AEMost expensive frontier model. At $15 / $75 per million tokens, Opus 4.7 is the priciest tier. For high-volume simple workloads (basic chat, FAQs), smaller models like Claude Haiku or GPT-5 mini are 10–20× cheaper and good enough.
Slower than GPT-5 at equivalent quality. Time-to-first-token is fine for back-office agents but lags for chat UIs that need sub-second responses.
Closed-weights, no self-hosting. Opus 4.7 is not available for fine-tuning. For data sovereignty, air-gapped environments, or model customization, an open-weights model like Llama 4 Behemoth is the alternative.
No image, audio, or video output. Vision is input-only. For generative image, audio, or video tasks, you'll need a separate model like FLUX 1.1 Pro or Sora 2.
For long-horizon agentic tasks, structured tool-use, and safe refusal behaviour, Opus 4.7 generally leads. GPT-5 is faster and stronger on native multimodal generation. The right pick depends on whether you optimise for reliability or latency + modality breadth.
Opus 4.7 is priced at $15 per million input tokens and $75 per million output tokens — the frontier tier. High-volume simple workloads are far cheaper on Claude Haiku or GPT-5 mini.
It's available through Anthropic's own API, AWS Bedrock, Google Cloud Vertex AI, and Cloudflare Workers AI.
No. Opus 4.7 is closed-weights and not available for fine-tuning. For customization or self-hosting, an open-weights model such as Llama 4 Behemoth is the alternative.
Yes — its focus on honesty and graceful failure (refusing risky or out-of-policy actions) makes it a common choice for regulated industries, paired with appropriate human-in-the-loop review.
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