Claude Opus 5 introduced a feature that fundamentally changes how developers interact with large language models: 5-level effort control. Rather than getting one fixed level of reasoning depth for every query, you can now dial the model’s thinking intensity up or down depending on your task. This guide breaks down each level, explains the cost and speed implications, and gives you practical examples so you can pick the right setting every time.
What Are Effort Levels?
When Anthropic released Claude Opus 5 on July 24, 2026, one of the standout features was granular effort control. Thinking is on by default in Opus 5, but you can now set the effort parameter to one of five levels: min, low, medium, high, or max. Each level controls how much internal reasoning the model performs before generating its response.
This is not just a cosmetic toggle. Higher effort means more thinking tokens are consumed internally, which increases both latency and cost. Lower effort means faster, cheaper responses but with less thorough reasoning. The key insight is that not every task needs maximum brainpower, and now you have the control to match model effort to task complexity.
For a full overview of all Opus 5 capabilities, check out our Claude Opus 5 complete guide.
The Five Effort Levels Explained
Min Effort
Min effort is the fastest and cheapest setting. The model performs minimal internal reasoning before responding. Think of it as “gut reaction” mode.
Best for:
- Simple lookups and factual questions
- Text formatting and reformatting
- Basic string manipulation
- Classification tasks with clear categories
- Quick yes/no decisions
Example use case: You are building a chatbot that needs to classify user intent into predefined categories like “billing,” “technical support,” or “general inquiry.” Min effort handles this reliably because the task requires pattern matching, not deep reasoning.
Speed: Roughly 4 to 5x faster than max effort for short outputs.
Cost impact: Minimal thinking tokens consumed. You pay close to the base rate of $5 per million input tokens and $25 per million output tokens.
Low Effort
Low effort adds a thin layer of reasoning. The model briefly considers the problem before responding but does not explore multiple solution paths.
Best for:
- Simple code generation (boilerplate, CRUD operations)
- Summarization of straightforward text
- Translation tasks
- Light editing and proofreading
- Answering questions with obvious answers that benefit from a sanity check
Example use case: Generating a standard Express.js route handler. The pattern is well established, and the model only needs minimal reasoning to adapt the template to your specific endpoint.
Speed: About 3x faster than max effort.
Cost impact: Slightly more thinking tokens than min, but still well below the midpoint.
Medium Effort
Medium is the balanced default that works well for most general-purpose tasks. The model reasons through the problem methodically but does not exhaustively explore every angle.
Best for:
- Most coding tasks (feature implementation, refactoring)
- Writing and content generation
- Data analysis with moderate complexity
- API design decisions
- Code review and suggestions
Example use case: Implementing a new feature in your application. The model considers the existing codebase context, thinks through the implementation approach, and produces well-structured code. For coding tasks specifically, you might want to compare how this performs against other models in our best AI coding tools 2026 roundup.
Speed: Baseline reference speed.
Cost impact: Moderate thinking token usage. A solid middle ground between speed and quality.
High Effort
High effort engages deeper reasoning. The model considers multiple approaches, evaluates tradeoffs, and verifies its own work more thoroughly.
Best for:
- Complex algorithmic problems
- Architecture and system design
- Debugging subtle issues
- Multi-step mathematical reasoning
- Security analysis and code auditing
- Tasks where correctness is critical
Example use case: Debugging a race condition in a concurrent system. The model needs to mentally simulate multiple execution paths, consider timing issues, and verify that its proposed fix does not introduce new problems.
Speed: About 2x slower than medium.
Cost impact: Significantly more thinking tokens. Expect 2 to 3x the cost of medium effort for complex tasks.
Max Effort
Max effort is the model running at full capacity. It exhaustively explores the problem space, considers edge cases, verifies its reasoning, and double-checks its conclusions. This is where Opus 5 achieves its headline benchmark scores like 43.3% on Frontier-Bench.
Best for:
- Novel research problems
- Extremely complex multi-step reasoning
- Tasks where you previously needed multiple iterations
- Competition-level mathematics
- Critical code that must be correct on the first try
- Comprehensive security audits
Example use case: Solving a complex distributed systems design problem where you need the model to consider consistency guarantees, failure modes, network partitions, and performance characteristics simultaneously. For understanding how max effort compares to other frontier models, see our Opus 5 vs Fable 5 comparison.
Speed: The slowest setting. Can be 4 to 5x slower than min effort.
Cost impact: Maximum thinking token consumption. Can be 3 to 5x the cost of min effort depending on task complexity.
Cost Implications in Detail
The base pricing for Claude Opus 5 is $5 per million input tokens and $25 per million output tokens. However, effort levels affect how many thinking tokens the model generates internally.
Here is a rough cost multiplier guide relative to min effort:
| Effort Level | Cost Multiplier | Typical Thinking Tokens |
|---|---|---|
| Min | 1x | Very few |
| Low | 1.2 to 1.5x | Hundreds |
| Medium | 1.5 to 2x | Thousands |
| High | 2 to 3x | Tens of thousands |
| Max | 3 to 5x | Can reach hundreds of thousands |
These multipliers vary based on task complexity. A simple question at max effort will not cost 5x because there is only so much the model can reason about. But a complex problem at max effort can generate substantial thinking tokens.
If you want to combine effort control with Fast mode, remember that Fast mode doubles the per-token cost ($10/$50) while providing 2.5x speed. Using max effort with Fast mode gives you the deepest reasoning at the highest speed, but at premium cost.
For a broader view of how these costs compare across providers, check our AI API pricing comparison for 2026.
Speed Differences
Response latency varies significantly across effort levels. Here are approximate time-to-first-token and total generation times for a moderately complex coding task (generating a 200-line function):
| Effort Level | Time to First Token | Total Generation Time |
|---|---|---|
| Min | Under 1 second | 3 to 5 seconds |
| Low | 1 to 2 seconds | 5 to 8 seconds |
| Medium | 2 to 4 seconds | 8 to 15 seconds |
| High | 4 to 8 seconds | 15 to 30 seconds |
| Max | 8 to 20 seconds | 30 to 60 seconds |
These numbers are approximate and depend on server load, input length, and task complexity. The 1M context window means that with large inputs, all timings increase proportionally.
Practical Strategy: Matching Effort to Task
The most cost-effective approach is to use a tiered strategy:
- Start with medium for new or unfamiliar tasks
- Drop to low or min once you confirm the task is straightforward
- Escalate to high or max only when medium produces incomplete or incorrect results
This mirrors how you would work with a human contractor. You do not pay for their deepest analysis on a trivial formatting task, but you absolutely want their full attention on critical architecture decisions.
For Coding Workflows
If you use Claude Opus 5 through Claude Code, consider this mapping:
- Min/Low: Generating boilerplate, writing tests for simple functions, formatting
- Medium: Standard feature implementation, refactoring, most code review
- High: Complex debugging, performance optimization, security-sensitive code
- Max: Architecture decisions, novel algorithm design, critical system components
For Agent Workflows
When building agents with Opus 5, effort levels become even more important because agents make many sequential calls. A common pattern is:
- Use low effort for routine tool-calling decisions
- Use medium for synthesizing information from multiple tool results
- Escalate to high or max for final decision-making or when the agent detects it is stuck
This keeps agent loop costs manageable while preserving reasoning quality where it matters most.
API Implementation
Setting effort levels in the API is straightforward:
import anthropic
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-opus-5",
max_tokens=16000,
thinking={
"type": "enabled",
"budget_tokens": 10000,
"effort": "high"
},
messages=[
{"role": "user", "content": "Design a distributed cache invalidation system"}
]
)The effort parameter accepts: min, low, medium, high, or max. You can also combine it with budget_tokens to set an absolute cap on thinking token usage regardless of effort level.
Comparing Effort Levels to Other Models
An interesting comparison point: Claude Opus 5 at medium effort often outperforms Claude Opus 4.8 at its fixed (always-on) reasoning level. And Opus 5 at max effort is where it achieves the benchmark scores that double Opus 4.8’s performance on Frontier-Bench.
Similarly, Claude Sonnet 5 can be thought of as roughly equivalent to Opus 5 at low to medium effort for many tasks, but at a lower base cost. The difference is that Sonnet 5 cannot scale up to the reasoning depth that Opus 5 high and max provide.
Common Mistakes to Avoid
-
Always using max: This wastes money and time on simple tasks. The model will still think deeply even when there is nothing complex to reason about.
-
Always using min: You miss out on the model’s strongest capability. If you only need min effort, you might be better served by a cheaper model like Sonnet 5.
-
Not adjusting based on results: If medium gives you wrong answers, escalate. If max gives you the same answer as medium, you can safely drop down.
-
Ignoring the speed tradeoff: In interactive applications, users notice latency. Use min or low for real-time interactions and reserve high/max for background processing.
FAQ
Does effort level affect output quality for simple tasks?
For truly simple tasks like classification or formatting, there is minimal quality difference between effort levels. The model arrives at the same answer regardless of how much it thinks. Save your budget for complex tasks.
Can I change effort level mid-conversation?
Yes. Each API call can specify a different effort level. This is particularly useful in agent loops where you want low effort for routine steps and high effort for critical decisions.
Does max effort guarantee correct answers?
No. Max effort maximizes the model’s reasoning depth, but it is not infallible. Complex problems may still require multiple attempts or human verification. However, max effort significantly reduces error rates compared to lower levels.
How do effort levels interact with Fast mode?
They are independent controls. You can use any effort level with or without Fast mode. Fast mode ($10/$50 at 2.5x speed) affects generation speed, while effort levels affect reasoning depth. Using max effort with Fast mode gives you deep reasoning at higher speed but at premium cost.
Should I always use the highest effort I can afford?
No. Higher effort is not always better. For simple tasks, excessive reasoning can occasionally lead to overthinking, where the model second-guesses an obviously correct answer. Match effort to task complexity for best results.
How do effort levels compare to setting a thinking budget?
Effort levels are a qualitative control (how deeply to reason), while budget_tokens is a quantitative cap (maximum tokens to spend on thinking). You can use both together: set effort to high but cap the budget to prevent runaway costs on unexpectedly complex inputs.