Claude Opus 4.7 launched on April 16, 2026 as Anthropic's newest generally available flagship for complex reasoning and coding. If you work with agents, code generation, or multimodal workflows, this release is worth paying attention to.
What is new in Opus 4.7
- Stronger coding and agentic execution, especially on long multi-step tasks that previously needed closer supervision.
- Better instruction following, which improves reliability but also means old prompts may need retuning.
- Higher-fidelity vision support with images up to 2,576 pixels on the long edge, useful for dense screenshots and technical diagrams.
- New effort level xhigh between high and max for finer reasoning-versus-latency control.
- Task budgets in public beta on Claude Platform to guide token spend on longer autonomous runs.
- Same API pricing as Opus 4.6: $5 per million input tokens and $25 per million output tokens, with availability across Claude API, Bedrock, Vertex AI, and Microsoft Foundry.
Behavior changes to know (sourced)
Anthropic's official launch and best-practices notes describe several default behavior changes in Opus 4.7 compared with earlier Opus versions.
- Instruction following is more literal, so prompts tuned for older models can behave differently and should be re-tested.
- Response length is more calibrated to task complexity, with shorter output on simple requests and longer output for open-ended analysis.
- The model tends to call tools less often by default and may reason more before acting, unless tool-use guidance is explicit.
- The model is more selective about spawning subagents unless delegation and parallel fan-out are clearly requested.
Sources (factual claims):
Session structure tactics from Anthropic
In Anthropic's Opus 4.7 Claude Code best-practices post, these tactics are emphasized for better quality and token efficiency in interactive sessions.
Define the full task in turn one
Include intent, constraints, acceptance criteria, and relevant file locations up front instead of revealing context gradually.
Reduce back-and-forth turns
Batch questions and required context in fewer user turns because each additional turn can add reasoning overhead.
Use auto mode when trust is established
For long-running tasks where guardrails are clear, auto mode can reduce cycle time by minimizing interruptions.
Set completion notifications
Use hook-based completion notifications so long tasks can finish asynchronously without constant manual checking.
Adaptive thinking controls (prompt-level)
Opus 4.7 uses adaptive thinking. Anthropic suggests nudging the model through prompt wording when you want more depth or more speed.
- For deeper reasoning: Ask it to think carefully and step-by-step before responding on harder problems.
- For speed: Ask it to prioritize a direct response and avoid deep thinking unless necessary.
Effort level decision matrix
The matrix below summarizes Anthropic guidance for choosing effort levels in Claude Code workflows.
| Effort level | When to use | Tradeoff |
|---|---|---|
| low | Tightly scoped or latency-sensitive tasks. | Lowest cost and fastest responses, but reduced depth on harder tasks. |
| medium | Cost-sensitive coding and straightforward implementation loops. | Good throughput with moderate reasoning depth. |
| high | Balanced coding work or multiple concurrent sessions. | Better quality than medium with more token spend. |
| xhigh (default) | Most agentic coding, design decisions, and complex refactors. | Best practical balance of intelligence and cost for most serious tasks. |
| max | Hardest intelligence-sensitive tasks and targeted evaluation runs. | Highest quality ceiling with diminishing returns and highest token usage. |
Token impact scenarios
Anthropic flags three migration-sensitive token factors: tokenizer updates, higher-effort reasoning, and high-resolution vision inputs.
| Scenario | Token impact | Practical action |
|---|---|---|
| Migration of existing prompts from Opus 4.6 | Equivalent text can map to roughly 1.0x to 1.35x tokens depending on content. | Benchmark real traffic before full rollout and adjust budget thresholds. |
| Long coding sessions at high/xhigh/max effort | Later turns can produce more output tokens due to deeper reasoning. | Use high or xhigh first, then reserve max for only the hardest steps. |
| Vision workflows with dense screenshots and diagrams | Higher-resolution image understanding increases token usage when fine detail is processed. | Downsample images when fine-grained detail is not required. |
Sources (factual claims):
Migration notes before upgrading
- Expect token usage shifts: Opus 4.7 uses an updated tokenizer and equivalent text can map to roughly 1.0x to 1.35x tokens depending on content.
- At higher effort levels, the model can emit more output tokens, so set effort intentionally for your workload.
- Anthropic flags API breaking changes versus Opus 4.6, so review the migration guide and test on real traffic before full rollout.
My experience using Opus 4.7 in Claude Code
After testing Opus 4.7 in Claude Code, I found it excellent for critical thinking and planning mode. It structures hard problems clearly, catches assumptions, and produces stronger implementation plans than most models.
The tradeoff is token speed. Opus 4.7 can consume tokens quickly during long sessions, especially when effort is high and the task spans multiple tool calls.
- Use Opus 4.7 for planning mode: architecture decisions, task breakdowns, debugging strategy, and risk analysis.
- Switch to Sonnet 4.6 for execution mode: finishing tasks, iterative edits, and lower-cost completion loops.
Where Opus 4.7 shines the most
- Critical planning for complex refactors across multiple services.
- Design reviews where tradeoffs, risks, and edge cases matter.
- Root-cause analysis for bugs that require long context and hypothesis testing.
- Agent orchestration prompts where quality of planning directly affects execution.
Practical model playbook I use
- Start in Opus 4.7 with planning prompts: ask for architecture, milestones, risks, and rollback paths.
- Lock the plan into a checklist before writing code.
- Switch to Sonnet 4.6 for implementation loops: code edits, test fixes, and task completion.
- Return to Opus 4.7 only for blockers that need deep reasoning.
- Keep prompts concise in both models to reduce unnecessary token burn.
Quick checklist before you start
- Decide first: planning session or execution session.
- Set effort intentionally; avoid max unless the task truly requires it.
- Track token usage every few turns during long runs.
- Use Sonnet 4.6 as default finisher for stable throughput.
Teams are moving fast on Opus 4.7 because it pairs stronger quality with practical controls for cost and reliability. This is the right time to benchmark it in your own stack.