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How I Combine Opus 4.7 and Sonnet 4.6 in Claude Code
April 20267 min read
Image source: Anthropic - Introducing Claude Opus 4.7
Use Opus 4.7 for planning mode — architecture decisions, task breakdowns, debugging strategy, and risk analysis — then switch to Sonnet 4.6 for execution mode, which covers iterative edits, test fixes, and lower-cost completion loops. Return to Opus 4.7 only when a blocker requires deep reasoning. This two-model approach balances Opus 4.7's stronger planning quality against Sonnet 4.6's cost efficiency and throughput.
The xhigh effort level sits between high and max, and Anthropic recommends it as the default for most agentic coding work, design decisions, and complex refactors. It provides the best practical balance of intelligence and cost for serious tasks, without the diminishing returns and highest token usage that come with max. Reserve max only for the hardest intelligence-sensitive tasks and targeted evaluation runs.
Opus 4.7's instruction following is more literal than earlier Opus versions, which means prompts tuned for older models can behave differently and should be re-tested before full rollout. Additionally, the updated tokenizer means equivalent text can map to roughly 1.0x to 1.35x more tokens depending on content. Anthropic also flags API breaking changes versus Opus 4.6, so reviewing the migration guide and testing on real traffic is recommended before switching fully.
Anthropic recommends defining the full task — including intent, constraints, acceptance criteria, and relevant file locations — in the very first turn rather than revealing context gradually. Batching questions into fewer user turns reduces reasoning overhead, and using auto mode for long tasks with clear guardrails minimizes interruptions. Hook-based completion notifications allow long tasks to finish asynchronously without constant manual checking.
Opus 4.7 supports higher-fidelity vision with images up to 2,576 pixels on the long edge, which is useful for dense screenshots and technical diagrams. However, higher-resolution image understanding increases token usage when fine detail is processed, so Anthropic recommends downsampling images when fine-grained detail is not actually required. This is one of three migration-sensitive token factors flagged alongside tokenizer updates and higher-effort reasoning.
Image source: Anthropic - Introducing Claude Opus 4.7
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.
Anthropic's official launch and best-practices notes describe several default behavior changes in Opus 4.7 compared with earlier Opus versions.
Sources (factual claims):
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.
Opus 4.7 uses adaptive thinking. Anthropic suggests nudging the model through prompt wording when you want more depth or more speed.
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. |
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):
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.
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.