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Start with medium. It is Claude Opus 5.5’s default and the setting Anthropic recommends as the baseline for your own evaluations. Move down to low for straightforward, cost-sensitive work; move up to high, xhigh, or max only when your task measurements show that deeper reasoning improves the result enough to justify additional tokens and latency.
Opus 5.5 has five effort levels: low, medium, high, xhigh, and max. Effort changes how much reasoning, tool use, and self-checking Claude can apply. Adaptive thinking remains enabled at every level, so effort—not a thinking on/off switch—is the main control over reasoning depth and request consumption.
What effort controls in Claude Opus 5.5
Effort is a budget for the work Claude performs before and during its visible answer. A higher setting gives the model more room to analyze requirements, plan, call tools, inspect results, revise code, and verify its own work. A lower setting generally finishes with fewer thinking tokens and less waiting, but it can give up some capability on ambiguous or multi-step tasks.
The setting does not guarantee a particular number of tokens, tool calls, or seconds. A simple request may finish quickly at any level, while a difficult request can consume substantially more at a higher level. Treat effort as a quality-and-resource control, not as a fixed accuracy mode.
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Adaptive thinking is always active
Opus 5.5 uses adaptive thinking at all five levels. You cannot disable thinking with a separate toggle. Choosing low still permits reasoning; it simply applies a tighter budget than high, xhigh, or max.
Medium is the starting point
Anthropic’s 2026 effort documentation identifies medium as the default for Opus 5.5. Its prompting guidance says to start at medium and test several levels against your own evaluations instead of carrying over the setting used for Claude Opus 5. The older model’s preferred setting is not a reliable predictor of the new model’s best setting.
Claude Opus 5.5 effort levels compared
| Level | Best fit | Expected resource profile | How to use it |
|---|---|---|---|
low |
Simple tasks, rapid iteration, and cost-sensitive workloads | Lowest reasoning budget among the five; usually less latency and token use | Use when mistakes are inexpensive and a quick answer is more valuable than exhaustive checking |
medium |
General-purpose coding, analysis, and knowledge work | Balanced quality, latency, and consumption; the Opus 5.5 default | Make this your baseline, then compare other levels on representative tasks |
high |
Difficult coding, analysis, and agentic work | More reasoning and verification, with higher token use and latency expected | Choose it when medium produces avoidable errors or incomplete plans |
xhigh |
Long-running coding and agentic tasks, especially work lasting more than 30 minutes | Extended reasoning capacity; potentially substantial additional consumption and wait time | Reserve it for tasks where your evaluation demonstrates a meaningful quality gain |
max |
The hardest tasks where capability is more important than spend | Deepest analysis and no effort constraint on token spending; highest potential latency | Use selectively, and verify that the improvement justifies the cost |
What the published comparisons indicate
Anthropic reports that low comes close to Claude Opus 5 at high on several coding evaluations, at much lower cost. It also reports that Opus 5.5 at medium matches or exceeds Opus 5 at high on coding and knowledge-work evaluations. Those are Anthropic’s evaluations, not a universal ranking of every workload. Results can change with the prompt, tools, context, token cap, and success criteria.
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Routine questions and transformations: low
Summaries of supplied text, formatting changes, straightforward extraction, simple classification, and short drafts are good candidates for low. These jobs have clear acceptance criteria and little benefit from extended planning. If a reviewer can cheaply catch an occasional omission, the lower latency and consumption usually matter more than maximum reasoning depth.
Everyday development and analysis: medium
Use medium for normal feature work, code explanations, document analysis, test generation, and research synthesis. It gives you a stable reference point and is the setting Anthropic selected as the default. Keep it until your measurements show a repeatable reason to change.
Complex debugging and multi-step changes: high
Switch to high when the task spans several modules, requires interpreting unfamiliar code, involves subtle edge cases, or needs a plan followed by implementation and verification. Higher effort is particularly useful when Claude can run tools and must inspect their output before deciding what to do next.
Long-running agents: xhigh
xhigh is intended for extended coding and agentic sessions, with Anthropic highlighting work that lasts longer than 30 minutes. It is not automatically the right choice for every large prompt. If your agent spends most of its time waiting on external systems or performing repetitive operations, extra reasoning budget may not improve the outcome.
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max provides the deepest analysis and places no constraint on token spending. Reserve it for high-consequence or exceptionally difficult tasks—such as a complex migration plan, a difficult bug with expensive failure, or a long autonomous workflow—where you have evidence that lower levels fail. Review the output and tool actions even at max; effort does not make an unverified answer infallible.
How to benchmark effort levels fairly
An effort comparison is useful only when the levels face the same job under the same conditions. Anthropic recommends an effort sweep on your own evaluations rather than relying on a generic leaderboard.
- Assemble representative tasks. Include the coding, analysis, and agentic cases that matter to your product. Add both ordinary jobs and known failure cases; a single impressive prompt is not enough.
- Freeze the inputs. Use identical prompts, system instructions, context files, tool definitions, model version, temperature or other sampling controls, and success criteria for every run. Keep external data and tool state consistent.
- Set a fair output cap. The
max_tokenslimit covers thinking tokens as well as visible response text. If the cap is too low, high-effort runs can be cut off before they finish. Anthropic’s prompting guidance reports that128,000—the model maximum—worked well for long agentic coding turns in its testing; use a limit appropriate to your workload, but leave enough headroom for the highest levels. - Run each task at all relevant levels. At minimum, compare
low,medium, andhigh. Addxhighandmaxwhen the task is long-running or high consequence. Repeat runs when outputs are stochastic so one lucky answer does not decide the result. - Score outcomes, not impressions. Record whether the task succeeded, which errors occurred, whether tests passed, and how much human correction was needed. For agents, check the final artifact and every consequential tool action.
- Measure resource use. Capture total tokens, visible output tokens, thinking tokens when exposed by your interface, latency, number of tool calls, retries, and billed cost. A quality gain that costs several times more may not be worthwhile.
- Choose a policy. Select the lowest level that meets your quality threshold on the workload. Keep a higher-level fallback for failures instead of paying the maximum cost on every request.
A practical scorecard
| Metric | What to record | Why it matters |
|---|---|---|
| Task success | Pass/fail against an objective rubric, tests, or acceptance checklist | Prevents a fluent but incorrect answer from winning |
| Error severity | Count and impact of factual, coding, safety, or tool-use errors | Shows whether extra effort reduces costly failures |
| Latency | Time to first useful output and total completion time | Important for interactive users and time-limited agents |
| Consumption | Input, thinking, and output tokens, plus billed cost | Reveals the financial trade-off between levels |
| Tool behavior | Number, success rate, and usefulness of tool calls | Detects unproductive extra investigation at higher effort |
| Human rework | Edits, retries, or review minutes after the model finishes | A lower initial cost may be false economy if correction is large |
Token limits, context, and caching details
Leave room for thinking
Because the output cap includes hidden thinking as well as the visible response, a request can stop early even when the visible answer is short. This is most likely at high, xhigh, and max. Raise max_tokens when you see truncation, incomplete tool loops, or a missing final answer. Opus 5.5’s documented model maximum is 128,000 output tokens.
Large context is not a reason to use max
The model overview documents a 1-million-token context window. That capacity lets you supply large codebases or document collections, but context size and effort are different controls. A huge input may still be easy to transform at low, while a small but logically difficult bug may need high. Evaluate the combination you actually deploy.
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Changing top-level effort between requests invalidates the prompt cache. Anthropic’s prompting guidance notes that changing effort per message can preserve the cache for later turns. If repeated requests share a large stable prefix, design your client so experiments do not unnecessarily discard that cached context, and verify the behavior in the API surface you use.
Best Value
Common mistakes when choosing an effort level
- Assuming max is always best: More reasoning can add latency and cost without improving a simple result.
- Reusing a Claude Opus 5 setting: Anthropic specifically advises testing Opus 5.5 afresh; model generations can have different quality-cost curves.
- Benchmarking only one prompt: A level can excel on one task and underperform on another. Use a workload set with objective grading.
- Starving high-effort runs of tokens: A low cap can make the comparison measure truncation rather than capability.
- Judging only the final prose: For tool-using agents, inspect code changes, commands, retrieved evidence, and side effects.
- Ignoring review cost: Include human correction and retries in the economics, not just the model invoice.
A simple deployment strategy
- Default production traffic to
mediumwhile you establish baseline quality and cost. - Route clearly bounded, low-risk transformations to
lowafter they pass your acceptance tests. - Escalate failed or ambiguous requests to
high, preserving the original context and recording the reason for escalation. - Use
xhighfor proven long-running workflows andmaxfor a small, monitored class of critical tasks. - Re-run the evaluation suite after changing prompts, tools, context size, model versions, or token limits; an effort choice is not permanent.
What is—and is not—established by published testing
Anthropic’s published comparisons support two specific conclusions: Opus 5.5 at medium matched or exceeded Opus 5 at high on its coding and knowledge-work evaluations, and low came close to Opus 5 at high on several coding evaluations at lower cost. They do not establish a universal accuracy percentage, a fixed cost multiplier, or a guaranteed latency difference from low through max.
There is no single effort level that wins every workload. Your prompts, tools, context, cap, concurrency, and definition of success determine the useful trade-off. The defensible method is to measure those variables on the work you actually run.
Quick Recap
Recommended defaults at a glance
| Situation | Starting level | Escalate when |
|---|---|---|
| Fast, low-risk transformation | low |
Errors require more correction than the saved latency or cost |
| General application traffic | medium |
Representative tests show recurring planning or verification failures |
| Complex coding or analysis | high |
The task remains incomplete or unreliable despite adequate context and token headroom |
| Agent runs over 30 minutes | xhigh |
Your evaluation demonstrates a measurable gain over high |
| Critical, hardest cases | max |
Only when the measured quality gain is worth unrestricted token spending and delay |
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