Effort

The effort parameter lets you control how many tokens Claude spends when responding to requests. You can trade off between response thoroughness and token efficiency with a single model. The top-level effort parameter is available on all supported models with no beta header required. Per-message effort is in beta.

Set the effort level

Set output_config.effort on the request. The following example runs one request at medium effort and prints the response text.

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-5",
    max_tokens=4096,
    messages=[
        {
            "role": "user",
            "content": "Analyze the trade-offs between microservices and monolithic architectures",
        }
    ],
    output_config={"effort": "medium"},
)

for block in response.content:
    if block.type == "text":
        print(block.text)

How effort works

By default, Claude uses high effort, spending as many tokens as needed for excellent results. You can raise the effort level to max for the absolute highest capability, or lower it to be more conservative with token usage, optimizing for speed and cost while accepting some reduction in capability.

The effort parameter affects all tokens in the response, including:

• Text responses and explanations
• Tool calls and function arguments
• Thinking (when active)

Because effort applies to every output token, it works whether or not thinking is enabled. Lower effort also means fewer and terser tool calls.

Effort levels

Not every model that supports max supports xhigh.

The per-model recommendations that follow override this table where they differ.

Recommended effort levels for Claude Fable 5.1

Claude Fable 5.1 supports all five effort levels. Start with high, the default. Step up to xhigh or max for the most capability-sensitive agentic and coding work, and step down to medium or low for routine or latency-sensitive work once your evals show quality holds. At high and above, set a large max_tokens. It's a hard limit on total output (thinking plus response text). The same recommendations apply to Claude Mythos 5.1. See Prompting Claude Fable 5.1.

Claude Fable 5.1 also supports changing effort mid-conversation with a per-message output_config, which preserves the prompt cache.

Recommended effort levels for Claude Fable 5

Effort is the primary control for trading off intelligence, latency, and cost on Claude Fable 5. Start with high, the default, for most tasks, use xhigh for the most capability-sensitive workloads, and step down to medium or low for routine work. Lower effort settings on Claude Fable 5 still perform well and often exceed xhigh performance on prior models. At high and xhigh, set a large max_tokens. It's a hard limit on total output (thinking plus response text). See Cost control.

Reduce effort if a task completes but takes longer than necessary, or if you want a faster, more interactive working style. The same recommendations apply to Claude Mythos 5. For fuller guidance, see Prompting Claude Fable 5.

Recommended effort levels for Claude Opus 5

Claude Opus 5 supports all five effort levels. Start with high, the default, and adjust based on your evals: step up to xhigh for demanding coding and agentic work, or to max when a task justifies unconstrained token spending, and use low and medium liberally as your primary control for token cost and response time wherever your evals show quality holds. If you carried effort settings over from an earlier model, run a fresh effort sweep on your evals rather than reusing them.

Effort controls thinking volume, not visible response length: on Claude Opus 5, changing effort does not reliably shorten responses, so prompt for length instead.

The API default is high. Set effort explicitly to use a different level. The value you pass overrides the default.

On Claude Opus 5, thinking cannot be disabled at xhigh or max effort: requests that set thinking: {"type": "disabled"} at those levels return a 400 error. See Effort with thinking.

When running Claude Opus 5 at xhigh or max effort, set a large max_tokens so the model has room to think and act across subagents and tool calls. Starting at 64k tokens and tuning from there is a reasonable default.

Claude Opus 5 also supports changing effort mid-conversation with a per-message output_config, which preserves the prompt cache.

Recommended effort levels for Claude Opus 4.8

The guidance for Claude Opus 4.7 also applies to Claude Opus 4.8. Start with xhigh for coding and agentic use cases, use high for most other intelligence-sensitive workloads, and step down to medium or low only when you've measured that the lower level holds quality on your evals.

The API default is high. Set effort explicitly to use a different level. The value you pass overrides the default.

When running Claude Opus 4.8 at xhigh or max effort, set a large max_tokens so the model has room to think and act across subagents and tool calls. Starting at 64k tokens and tuning from there is a reasonable default.

Recommended effort levels for Claude Opus 4.7

Start with xhigh for coding and agentic use cases, and use high as the minimum for most intelligence-sensitive workloads. Step down to medium for cost-sensitive workloads, or up to max only when your evals show measurable headroom at xhigh.

The API default is high. To use xhigh, set effort explicitly. The value you pass overrides the default.

Claude Opus 4.7 also respects effort levels more strictly than Claude Opus 4.6, especially at low and medium. At lower effort levels, the model scopes its work to what was asked rather than doing more than requested. If you observe shallow reasoning on complex problems with Claude Opus 4.7, raise effort rather than prompting around it. If you must keep effort low for latency, add targeted guidance like "This task involves multistep reasoning. Think carefully before responding."

When running Claude Opus 4.7 at xhigh or max effort, set a large max_tokens so the model has room to think and act across subagents and tool calls. Starting at 64k tokens and tuning from there is a reasonable default.

Recommended effort levels for Claude Sonnet 5

Claude Sonnet 5 defaults to high effort on the Claude API and Claude Code.

• High effort (default): Suitable for complex reasoning, coding, and agentic tasks where quality matters more than speed or cost.
• Xhigh effort: For the hardest coding and agentic tasks. See Prompting Claude Sonnet 5.
• Medium effort: Cost-saving step-down from the default. Comparable to Claude Sonnet 4.6 at high effort.
• Low effort: For high-volume or latency-sensitive workloads. Suitable for chat and non-coding use cases where faster turnaround is prioritized.
• Max effort: For tasks requiring the absolute highest capability with no constraints on token spending.

Recommended effort levels for Claude Sonnet 4.6

Sonnet 4.6 defaults to high effort. Explicitly set effort when using Sonnet 4.6 to avoid unexpected latency:

• Medium effort (recommended default): Best balance of speed, cost, and performance for most applications. Suitable for agentic coding, tool-heavy workflows, and code generation.
• Low effort: For high-volume or latency-sensitive workloads. Suitable for chat and non-coding use cases where faster turnaround is prioritized.
• High effort: For complex reasoning and tasks where quality matters more than speed or cost.
• Max effort: For tasks requiring the absolute highest capability with no constraints on token spending.

Effort with tool use

When using tools, the effort parameter affects both the explanations around tool calls and the tool calls themselves. Lower effort levels tend to:

• Combine multiple operations into fewer tool calls
• Make fewer tool calls
• Proceed directly to action without preamble
• Use terse confirmation messages after completion

Higher effort levels may:

• Make more tool calls
• Explain the plan before taking action
• Provide detailed summaries of changes
• Include more comprehensive code comments

Effort with thinking

The thinking parameter controls whether Claude thinks in thinking blocks before answering; the effort parameter controls how much work Claude puts into the whole response, which in adaptive mode includes how often and how deeply it thinks. Don't pass adaptive as an effort value: adaptive is a thinking mode, not an effort level.

At higher effort levels, Claude thinks on most requests and at greater length. At lower levels, it can skip thinking entirely for simpler problems. See Thinking and effort for full guidance on how the two controls work together.

On Claude Opus 4.5, the only extended-thinking-only model that supports effort, it works alongside budget_tokens: set the effort level for your task, then set the thinking token budget based on how much reasoning depth the task needs.

For per-model thinking availability, see the per-model configuration table. Effort works with or without thinking. See How effort works.

Change effort mid-conversation

You can run later turns of a conversation at a different effort level in two ways. On Claude Fable 5.1, Claude Mythos 5.1, and Claude Opus 5, use a per-message effort change, which keeps the prompt cache. On other models, set a new top-level value on the next request, which starts the cache over.

Per-message effort (beta)

Per-message effort is in beta and requires the beta header mid-conversation-output-config-2026-07-01. Models without per-message effort, including Claude Fable 5, return a 400 error: output_config.effort requires a model that supports per-turn effort; this model does not.

Add a role: "system" message with empty content and the new level in output_config.effort. The new level takes effect from the next user turn and holds until a later message changes it. Everything before that message is unchanged, so the cached prefix still matches.

The following example starts at high, then drops to low for a routine follow-up:

client = anthropic.Anthropic()

response = client.beta.messages.create(
    model="claude-fable-5-1",
    max_tokens=4096,
    output_config={"effort": "high"},
    messages=[
        {
            "role": "user",
            "content": "Plan a migration from SQLite to PostgreSQL in three short steps.",
        },
        {
            "role": "assistant",
            "content": "1. Export the SQLite data. 2. Create the PostgreSQL schema. 3. Import the data and verify row counts.",
        },
        # Effort-only system message: the new level takes effect from the next user turn.
        {"role": "system", "content": [], "output_config": {"effort": "low"}},
        {"role": "user", "content": "Summarize the plan in one sentence."},
    ],
    betas=["mid-conversation-output-config-2026-07-01"],
)

for block in response.content:
    if block.type == "text":
        print(block.text)

An effort-only system message carries no text, so the placement rules for mid-conversation system messages don't apply. It can appear anywhere in messages, including as the first entry or between an assistant turn and the next user turn. Values are the level names (low, medium, high, xhigh, and max).

On Claude Fable 5.1, prefer this form over changing the top-level value between requests. A top-level change restarts the cache and also steers the model less reliably: its earlier replies were written at the previous level, and it tends to stay consistent with them.

Top-level effort on the next request

The top-level output_config.effort applies to the whole request. To run a later part of a conversation at a different level, set the new value on the next request. Because top-level effort shapes the rendered prompt, changing it between requests doesn't preserve cached prefixes from earlier turns. If you rely on prompt caching across a long session and your model doesn't support per-message effort, pick an effort level at the start and keep it constant.

Best practices

1. Set effort explicitly: The API defaults to high, but the right starting point depends on your model and workload.
2. Use low for speed-sensitive or simple tasks: When latency matters or tasks are straightforward, low effort can significantly reduce response times and costs.
3. Test your use case: The impact of effort levels varies by task type. Evaluate performance on your specific use cases before deploying.
4. Consider dynamic effort: Adjust effort based on task complexity. Simple queries may warrant low effort while agentic coding and complex reasoning benefit from high effort. See the next item before varying it within one conversation.
5. Hold top-level effort constant within cached conversations: Changing the top-level effort value between requests invalidates prompt caching, so vary it across workloads rather than within a conversation that relies on cache hits. On models that support it, use a per-message effort change instead, which preserves the cache. See Thinking and prompt caching.

Next steps

Compatibility