Claude Opus 4.7 accepts image inputs up to 3.75 megapixels, roughly four times the resolution ceiling of earlier Claude 3 vision models, according to Anthropic's official model documentation released April 16, 2026. That single spec tells you a lot. This is not a minor version bump. Opus 4.7 ships with new tokenizer architecture, a context-budget control for agentic loops, and an exclusive reasoning mode not available on lower tiers. If you are deciding whether Opus 4.7 belongs in your stack, this post breaks down every major feature, the benchmark numbers, the pricing math, and where cheaper models still win.
What Is Claude Opus 4.7?
Claude Opus 4.7 is Anthropic's highest-tier generally available model as of May 2026. It launched on April 16, 2026, sitting above Sonnet 4.6 and Haiku 4.5 in the product line.
The model is built for work where the cost of a wrong answer is high. Think legal analysis, medical literature review, dense document processing, or autonomous agents running multi-step tasks. It is not optimized for speed. Latency is higher than Sonnet at comparable settings, and the pricing reflects that.
Under the hood, Opus 4.7 ships with a new tokenizer. This matters in practice. The old Claude 3 tokenizer was inefficient on repetitive structured content like JSON, tables, and code with long variable names. The new tokenizer compresses those patterns better, which means your token count on API calls drops, even before you apply caching or batch discounts. For teams already running large Claude workloads, this alone can produce meaningful cost savings on migration.
The 1M-token context window carries over from Claude 3 Opus. Book-length documents, full codebases, and long conversation histories remain within reach without chunking.
What New Features Does Claude Opus 4.7 Introduce?
Four features define Opus 4.7. Each one targets a real gap in what earlier models could do.
High-resolution vision. The 3.75-megapixel input ceiling means the model can read dense charts, architectural drawings, engineering schematics, and fine-print legal documents without lossy downscaling. Earlier Claude 3 vision models capped at roughly 1 megapixel, which blurred small text and compressed table structures. At 3.75MP, pixel-level detail survives the encoding step. For any workflow involving scanned documents or high-DPI diagrams, this is a real functional difference, not a marketing number.
The xhigh effort level. This is the most significant new capability. As of May 2026, the xhigh effort setting is exclusive to Opus 4.7 and is not available on Sonnet or Haiku through the Anthropic API. Setting effort to xhigh instructs the model to spend more compute on reasoning before producing its final response. The tradeoff is latency: responses take longer. The gain is accuracy on hard, multi-step problems where a standard pass misses a constraint or skips a verification step. For autonomous agents, high-stakes decisions, and complex reasoning chains, xhigh trades seconds for correctness.
Task budgets for agentic loops. More on this in its own section below.
Improved tokenizer. The new tokenizer reduces token counts on code, structured data, and non-English text. For teams building multilingual pipelines or processing large JSON payloads, this is a direct cost reduction.
How Do Task Budgets and Agentic Loops Work?
Task budgets solve a real production problem. When you run an AI agent that can call tools, browse the web, or spawn sub-tasks, runaway loops are a real risk. One misunderstood instruction can send an agent into a cycle that burns thousands of tokens before a timeout fires.
Opus 4.7 introduces task budgets as a first-class API feature. You pass a maximum step count or sub-task limit at the request level. The model tracks usage against that budget and stops cleanly when it hits the ceiling, rather than continuing until an external timeout kills the process.
This integrates directly with Anthropic's tool-use API. You do not need wrapper logic in your application code to implement a circuit breaker. The budget constraint lives in the request itself.
Practical use cases are broad. Code-review agents can be capped at a fixed number of file reads per run. Data-extraction pipelines can limit sub-calls per document. Browser-control tasks can be bounded by action count. In each case, the operator gets predictable cost per run, which is a hard requirement for any production system that charges customers or reports to a finance team.
For teams building multi-agent systems, task budgets make Opus 4.7 significantly more suitable as an orchestrator. The orchestrator can spin up cheaper models for subtask execution, pass back results, and reason over them, all within a defined compute envelope.
How Does Opus 4.7 Perform on Benchmarks?
Benchmark numbers give direction, not truth. Use them to calibrate expectations, not to make final decisions.
On GPQA Diamond, the graduate-level reasoning benchmark, Opus 4.7 scores above the Claude 3 Opus baseline. The gain is attributed to better chain-of-thought calibration and the xhigh effort level, which allows the model to verify intermediate steps before committing to an answer. The improvement is most visible on multi-constraint problems where earlier models correctly solved each constraint in isolation but failed to integrate them.
On DocVQA, a document visual question-answering benchmark, Opus 4.7 shows measurable improvement over Claude 3 Opus. The higher input resolution is the primary driver. When fine text and table structure survive encoding, the model can answer questions about specific cells, footnotes, and small-print clauses that were simply invisible at lower resolutions.
On coding evaluations including HumanEval and SWE-bench variants, Opus 4.7 is competitive with frontier peers. It does not dominate. Codex GPT-5.4 remains strong on pure synthesis tasks. Where Opus 4.7 differentiates is on multi-file reasoning and tasks that require understanding context across a large codebase, which plays to its tokenizer efficiency and long-context strengths.
One honest caveat: at standard effort, latency is higher than Sonnet 4.6. If your benchmark is tokens per second on a batch of simple tasks, Sonnet wins. Opus 4.7 is optimized for accuracy on hard problems, not throughput on easy ones.
Claude Opus 4.7 Pricing and API Access
The list price is $5 per million input tokens and $25 per million output tokens through the Anthropic API. As of May 2026, this positions Opus 4.7 at the same cost tier as other frontier models competing for complex enterprise workloads. You can compare this across the current model landscape in the 8 Best AI Models in 2026 comparison.
The list rate is not the effective rate for most production workloads. Two mechanisms reduce actual cost significantly.
Prompt caching stores frequently reused content, such as a system prompt or a large document, and charges a reduced rate on cache hits. For workflows where the same context block appears across hundreds of requests, the savings compound quickly.
Batch processing applies when you submit large volumes of requests for asynchronous processing rather than real-time response. Anthropic's batch API prices this below the synchronous rate, making high-volume document processing materially cheaper.
On deployment, Opus 4.7 is available through the Anthropic API directly, Amazon Bedrock, and Google Cloud Vertex AI. Teams with data residency requirements or existing cloud spend commitments can run Opus 4.7 within their current cloud agreements without routing traffic through Anthropic's infrastructure.
Who Should Use Claude Opus 4.7 vs. Cheaper Tiers?
The right model is the cheapest one that solves your problem reliably. Opus 4.7 is the right answer when output quality or reasoning depth is the binding constraint, not cost.
Use Opus 4.7 for:
- Legal and compliance analysis. Clause-level document review, contract comparison across jurisdictions, regulatory mapping. Wrong answers here carry real liability.
- Medical literature review. Synthesizing studies with conflicting findings, extracting effect sizes from dense tables, reasoning across multiple papers.
- Complex agent orchestration. When an agent is making decisions that trigger downstream actions with real consequences, you want the most accurate model at the decision layer.
- Dense document processing. Scanned PDFs, financial reports with multi-column layouts, engineering drawings where detail is critical.
Stick with Sonnet 4.6 or Haiku 4.5 for:
- Real-time chat and customer support where latency matters more than depth.
- Content moderation at scale where you need high throughput and the task is pattern recognition, not nuanced reasoning.
- Autocomplete and draft assistance where speed creates the user value.
- Subtask execution inside a multi-agent pipeline where an Opus orchestrator handles final reasoning.
The most cost-efficient architecture for serious multi-agent systems runs Opus 4.7 at the orchestrator layer and uses Haiku 4.5 or Sonnet 4.6 for subtask execution. The orchestrator handles planning, constraint checking, and final synthesis. The cheaper models handle the volume work. This pattern is covered in depth in How to Build a Solo Agency AI Stack for Multiple Clients and in the 5 Claude Automation Workflows That Survived Six Months post.
For teams newer to the Claude model family, How to Learn Claude AI from Scratch in 2026 gives a practical ramp from zero to production. And if you are evaluating Opus 4.7 against non-Anthropic options, 5 Best ChatGPT Alternatives in 2026 That Actually Work has an honest side-by-side.
Claude Opus 4.7 is not an incremental refresh. The combination of 3.75-megapixel vision, the xhigh effort level, task budgets for agentic loops, and a new tokenizer adds up to a model built for workflows where getting the answer right matters more than getting it fast. For teams running autonomous agents, processing dense documents, or handling tasks that previously required human review, Opus 4.7 is the model to evaluate first. For everything else, the cheaper tiers still win on economics.
FAQ
What is Claude Opus 4.7?
Claude Opus 4.7 is Anthropic's most capable generally available language model as of April 2026. It supports text and high-resolution image inputs up to 3.75 megapixels, a 1M-token context window, and a new xhigh effort level that allocates more compute to reasoning before the model responds. It is designed for complex tasks like multi-step agent workflows, dense document analysis, and graduate-level reasoning rather than everyday chat or high-volume classification.
How much does Claude Opus 4.7 cost?
Anthropic prices Claude Opus 4.7 at $5 per million input tokens and $25 per million output tokens on the standard API. Prompt caching and batch processing can reduce effective costs significantly for long-context or high-volume use cases. The model is also available through Amazon Bedrock and Google Cloud Vertex AI, where pricing may differ based on cloud provider agreements and committed use discounts.
What is the context window of Claude Opus 4.7?
Claude Opus 4.7 has a 1M-token context window, the same as its predecessor Claude 3 Opus. At roughly 750,000 words, that is enough to ingest entire codebases, lengthy legal contracts, or book-length documents in a single API call. The practical limit is cost and latency: at $5 per million input tokens, filling the full context is expensive, so most production workloads use retrieval or chunking strategies to stay within a cost-efficient range.
What is the xhigh effort level in Claude Opus 4.7?
The xhigh effort level is a new inference parameter introduced with Opus 4.7. When set, it instructs the model to allocate additional compute to internal reasoning before producing a response. The trade-off is higher latency and cost per output token compared to the default effort setting. It is most useful for tasks where accuracy is critical and latency is acceptable, such as scientific analysis, complex code generation, or multi-step problem solving.
Is Claude Opus 4.7 better than GPT-4o?
It depends on the task. Claude Opus 4.7 has measurable advantages in high-resolution document understanding (due to its 3.75MP vision input), very long context handling, and agentic task management through task budgets. GPT-4o has competitive reasoning benchmarks and broader ecosystem integrations. For most teams, the practical choice comes down to which API fits existing infrastructure, which model performs better on your specific eval set, and which pricing structure suits your volume.