Claude Opus 4.7 Released with 1M Context Window

Anthropic introduced Claude Opus 4.7 on April 16, 2026. This hybrid reasoning model features a 1 million token context window. It advances capabilities in coding and AI agents. The new version delivers better results in coding, vision, and complex multi-step tasks. Users find it more thorough and consistent for challenging professional knowledge work.

Previous Model Releases

Claude Opus 4.6 came out on February 5, 2026. That version offered the highest capabilities at the time. It improved reliability and precision in coding, agents, and enterprise workflows over Opus 4.5.

Before that, Claude Opus 4.5 launched on November 24, 2025. It established new benchmarks in coding, agents, computer use, and enterprise workflows. The model marked a notable advance in AI system abilities.

Claude Opus 4.1 appeared on August 5, 2025. It served as a direct upgrade from Opus 4, providing higher performance and precision for coding and agentic tasks. It managed intricate, multi-step problems with greater rigor and detail.

The original Claude Opus 4 debuted on May 22, 2025. It led in coding, agentic search, and creative writing. Developers could run Claude Code in the background for independent long-running coding tasks.

Availability and Pricing

Pro, Max, Team, and Enterprise users on Claude can access Opus 4.7 for collaboration on complex tasks. Developers building AI solutions get it natively on the Claude Platform. It also appears in Amazon Bedrock, Google Cloud's Vertex AI, and Microsoft Foundry.

Pricing begins at $5 per million input tokens and $25 per million output tokens. Prompt caching offers up to 90% cost savings. Batch processing provides 50% savings. Use claude-opus-4-7 via the Claude API. For US-only inference, rates stand at 1.1x for input and output tokens.

Key Use Cases

Opus 4.7 suits tasks beyond prior models, especially where performance counts. It excels in professional software engineering, complex agentic workflows, and high-stakes enterprise tasks.

Adaptive thinking lets it adjust effort based on task complexity. It spends more time on hard problems and responds fast to easy ones.

In advanced coding, it produces production-ready code with little supervision. It plans carefully, sustains effort on large codebases, and corrects its own errors. Senior engineers can assign tough work confidently.

For AI agents, it runs production workflows with multi-tool tasks reliably. It plans ahead, retains memory across sessions, and advances long tasks with minimal oversight.

Enterprise workflows benefit from context carryover across sessions for multi-day projects. It handles spreadsheets, slides, and docs with professional quality.

Benchmarks position Opus 4.7 as the top generally available model for coding, agentic, and knowledge work.

Extensive testing confirms it meets Anthropic's safety, security, and reliability standards. The model card details safety results.

Customer Feedback

Customers report strong gains. Clarence Huang, VP of Technology at a financial technology platform, noted: "In early testing, we're seeing the potential for a significant leap for our developers with Claude Opus 4.7. It catches its own logical faults during the planning phase and accelerates execution, far beyond previous Claude models. As a financial technology platform serving millions of consumers and businesses at significant scale, this combination of speed and precision could be game-changing: accelerating development velocity for faster delivery of the trusted financial solutions our customers rely on every day."

Igor Ostrovsky, Co-Founder & Chief Technology Officer, said: "Anthropic has already set the standard for coding models, and Claude Opus 4.7 pushes that further in a meaningful way as the state-of-the-art model on the market. In our internal evals, it stands out not just for raw capability, but for how well it handles real-world async workflows - automations, CI/CD, and long-running tasks. It also thinks more deeply about problems and brings a more opinionated perspective, rather than simply agreeing with the user."

Caitlin Colgrove, Co-Founder and CTO at Hex, stated: "Claude Opus 4.7 is the strongest model Hex has evaluated. It correctly reports when data is missing instead of providing plausible-but-incorrect fallbacks, and it resists dissonant-data traps that even Opus 4.6 falls for. It's a more intelligent, more efficient Opus 4.6: low-effort Opus 4.7 is roughly equivalent to medium-effort Opus 4.6."

Mario Rodriguez, Chief Product Officer, mentioned: "On our 93-task coding benchmark, Claude Opus 4.7 lifted resolution by 13% over Opus 4.6, including four tasks neither Opus 4.6 nor Sonnet 4.6 could solve. Combined with faster median latency and strict instruction-following, it's particularly meaningful for complex, long-running coding workflows. It cuts the friction from those multi-step tasks so developers can stay in the flow and focus on building."

Michal Mucha, Lead AI Engineer, Applied AI, observed: "Based on our internal research-agent benchmark, Claude Opus 4.7 has the strongest efficiency baseline we've seen for multi-step work. It tied for the top overall score across our six modules at 0.715 and delivered the most consistent long-context performance of any model we tested. On General Finance, our largest module, it improved meaningfully on Opus 4.6, scoring 0.813 versus 0.767, while also showing the best disclosure and data discipline in the group. And on deductive logic, an area where Opus 4.6 struggled, Opus 4.7 is solid."

Jeff Wang, CEO, commented: "Claude Opus 4.7 extends the limit of what models can do to investigate and get tasks done. Anthropic has clearly optimized for sustained reasoning over long runs, and it shows with market-leading performance. As engineers shift from working 1:1 with agents to managing them in parallel, this is exactly the kind of frontier capability that unlocks new workflows."

Sanj Ahilan, Chief Research Officer, noted improvements in multimodal understanding: "We're seeing major improvements in Claude Opus 4.7's multimodal understanding, from reading chemical structures to interpreting complex technical diagrams. The higher resolution support is helping Solve Intelligence build best-in-class tools for life sciences patent workflows, from drafting and prosecution to infringement detection and invalidity charting."

Michele Catasta, President at Replit, said: "For Replit, Claude Opus 4.7 was an easy upgrade decision. For the work our users do every day, we observed it achieving the same quality at lower cost, more efficient and precise at tasks like analyzing logs and traces, finding bugs, and proposing fixes. Personally, I love how it pushes back during technical discussions to help me make better decisions. It really feels like a better coworker."

Niko Grupen, Head of Applied Research at Harvey, reported: "Claude Opus 4.7 demonstrates strong substantive accuracy on BigLaw Bench for Harvey, scoring 90.9% at high effort with better reasoning calibration on review tables and noticeably smarter handling of ambiguous document editing tasks. It correctly distinguishes assignment provisions from change-of-control provisions, a task that has historically challenged frontier models. Substance was consistently rated as a strength across our evaluations: correct, thorough, and well-cited."

Michael Truell, Co-Founder & CEO, stated: "Claude Opus 4.7 is a very impressive coding model, particularly for its autonomy and more creative reasoning. On CursorBench, Opus 4.7 is a meaningful jump in capabilities, clearing 70% versus Opus 4.6 at 58%."

Sarah Sachs, AI Lead, noted: "For complex multi-step workflows, Claude Opus 4.7 is a clear step up: plus 14% over Opus 4.6 at fewer tokens and a third of the tool errors. It's the first model to pass our implicit-need tests, and it keeps executing through tool failures that used to stop Opus cold. This is the reliability jump that makes Notion Agent feel like a true teammate."

Adithya Ramanathan, Head of Applied Research, said: "In our evals, we saw a double digit jump in accuracy of tool calls and planning in our core orchestrator agents. As users leverage Hebbia to plan and execute on use cases like retrieval, slide creation, or document generation, Claude Opus 4.7 shows the potential to improve agent decision making in these workflows."

Yusuke Kaji, General Manager, AI for Business at Rakuten, reported: "On Rakuten-SWE-Bench, Claude Opus 4.7 resolves 3x more production tasks than Opus 4.6, with double-digit gains in Code Quality and Test Quality. This is a meaningful lift and a clear upgrade for the engineering work our teams are shipping every day."

David Loker, VP of AI at CodeRabbit, observed: "For CodeRabbit's code review workloads, Claude Opus 4.7 is the sharpest model we've tested. Recall improved by over 10%, surfacing some of the most difficult to detect bugs in our most complex PRs, while precision remained stable despite the increased coverage. It's a bit faster than GPT-5.4 xhigh on our harness, and we're lining it up for our heaviest review work at launch."

Kay Zhu, Co-Founder & CTO at Genspark, said: "For Genspark's Super Agent, Claude Opus 4.7 nails the three production differentiators that matter most: loop resistance, consistency, and graceful error recovery. Loop resistance is the most critical. A model that loops indefinitely on 1 in 18 queries wastes compute and blocks users. Lower variance means fewer surprises in prod. And Opus 4.7 achieves the highest quality-per-tool-call ratio we've measured."

Zach Lloyd, Founder and CEO at Warp, noted: "Claude Opus 4.7 is a meaningful step up for Warp. Opus 4.6 is one of the best models out there for developers, and this model is measurably more thorough on top of that. It passed Terminal Bench tasks that prior Claude models had failed, and worked through a tricky concurrency bug Opus 4.6 couldn't crack. For us, that's the signal."

Aj Orbach, Co-Founder & CEO, praised: "Claude Opus 4.7 is the best model in the world for building dashboards and data-rich interfaces. The design taste is genuinely surprising, it makes choices I'd actually ship. It's my default daily driver now."

Ben Chan, Chief AI Officer at Quantium, said: "Claude Opus 4.7 is the most capable model we've tested at Quantium. Evaluated against leading AI models through our proprietary benchmarking solution, the biggest gains showed up where they matter most: reasoning depth, structured problem-framing, and complex technical work. Fewer corrections, faster iterations, and stronger outputs to solve the hardest problems our clients bring us."

Ben Lafferty, Senior Staff Engineer, commented: "Claude Opus 4.7 feels like a real step up in intelligence. Code quality is noticeably improved, it's cutting out the meaningless wrapper functions and fallback scaffolding that used to pile up, and fixes its own code as it goes. It's the cleanest jump we've seen since the move from Sonnet 3.7 to the Claude 4 series."

Oege de Moor, CEO at XBOW, reported: "For the computer-use work that sits at the heart of XBOW's autonomous penetration testing, the new Claude Opus 4.7 is a step change: 98.5% on our visual-acuity benchmark versus 54.5% for Opus 4.6. Our single biggest Opus pain point effectively disappeared, and that unlocks its use for a whole class of work where we couldn't use it before."

Joe Haddad, Distinguished Software Engineer at Vercel, said: "Claude Opus 4.7 is a solid upgrade with no regressions for Vercel. It's phenomenal on one-shot coding tasks, more correct and complete than Opus 4.6, and noticeably more honest about its own limits. It even does proofs on systems code before starting work, which is new behavior we haven't seen from earlier Claude models."

Leo Tchourakov, Member of Technical Staff, noted: "Claude Opus 4.7 is very strong and outperforms Opus 4.6 with a 10% to 15% lift in task success for Factory Droids, with fewer tool errors and more reliable follow-through on validation steps. It carries work all the way through instead of stopping halfway, which is exactly what enterprise engineering teams need."

Sean Ward, CEO & Co-Founder, shared: "Claude Opus 4.7 autonomously built a complete Rust text-to-speech engine from scratch, neural model, SIMD kernels, browser demo, then fed its own output through a speech recognizer to verify it matched the Python reference. Months of senior engineering, delivered autonomously. The step up from Opus 4.6 is clear, and the codebase is public."

Itamar Friedman, Co-Founder & CEO at Qodo, said: "Claude Opus 4.7 passed three TBench tasks that prior Claude models couldn't, and it's landing fixes our previous best model missed, including a race condition. It demonstrates strong precision in identifying real issues, and surfaces important findings that other models either gave up on or didn't resolve. In Qodo's real-world code review benchmark, we observed top-tier precision."

Hanlin Tang, CTO of Neural Networks at Databricks, reported: "On Databricks' OfficeQA Pro, Claude Opus 4.7 shows meaningfully stronger document reasoning, with 21% fewer errors than Opus 4.6 when working with source information. Across our agentic reasoning over data benchmarks, it is the best-performing Claude model for enterprise document analysis."

Austin Ray, Software Engineer at Ramp, observed: "For Ramp, Claude Opus 4.7 stands out in agent-team workflows. We're seeing stronger role fidelity, instruction-following, coordination, and complex reasoning, especially on engineering tasks that span tools, codebases, and debugging context. Compared with Opus 4.6, it needs much less step-by-step guidance, helping us scale the internal agent workflows our engineering teams run."

Eric Simons, CEO & Founder at Bolt, concluded: "Claude Opus 4.7 is measurably better than Opus 4.6 for Bolt's longer-running app-building work, up to 10% better in the best cases, without the regressions we've come to expect from very agentic models. It pushes the ceiling on what our users can ship in a single session."

Related on Neura Market

• AI Tools Directory
• AI Platforms
• Automation Marketplace