Reasoning Effort Parameter Comparison: Claude vs GPT vs Gemini

What “Reasoning Effort” Means Across Providers

All three parameters answer the same underlying question — how much internal computation should the model spend before it responds — but they answer it at different scopes. Claude’s effort is the broadest: it governs the entire response, including tool calls and explanatory text, not just thinking. GPT-5.6’s reasoning.effort governs reasoning-token generation specifically, and is combined with a separate reasoning.mode switch for “pro” runs that do more model work on hard tasks. Gemini’s thinking_level governs only the model’s internal “thinking process” step before it produces output.

In practice this means the same level name — “medium,” say — does not imply the same behavior on two different platforms, and none of the three vendors intend it to.

Claude’s Effort Parameter

Claude’s effort parameter is set through output_config.effort in a Messages API request, and it is available on all currently supported Claude models with no beta header required. By default, Claude uses high effort — setting effort to "high" produces exactly the same behavior as omitting the parameter entirely.

A detail that trips up teams moving between Claude model generations: recommended defaults shift per model. Opus 4.8, Opus 5, and Sonnet 5 all default to high effort on the API and in Claude Code, but the recommended starting point differs — Opus 4.7 and 4.8 guidance suggests starting at xhigh for coding and agentic work, while Sonnet 5 guidance suggests staying at the high default for most tasks and reserving xhigh for the hardest coding problems.

Changing effort mid-conversation invalidates cached prompt prefixes, because effort shapes how the prompt itself is rendered. If a long session relies on prompt caching, pick one effort level at the start and hold it constant rather than adjusting it turn to turn. This is the first data point in our reasoning effort parameter comparison worth flagging before you migrate a cached workflow.

import anthropic

client = anthropic.Anthropic()

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

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

GPT-5.6’s Reasoning.effort

GPT-5.6 exposes reasoning depth through reasoning.effort in the Responses API, with six named levels: none, low, medium, high, xhigh, and max. If the parameter is omitted, GPT-5.6 defaults to medium in both its standard and “pro” reasoning modes. The none setting is distinct from the other providers’ lowest tiers — it behaves like a non-reasoning model rather than a lightly-reasoning one, which makes it the closest thing to a true “reasoning off” switch among the three providers.

Reasoning mode (reasoning.mode: "standard" or "pro") is a separate axis from effort: mode selects how much overall model work goes into producing a final answer, while effort controls reasoning depth within that mode.

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-5.6",
    input="Summarize the trade-offs between microservices and monoliths.",
    reasoning={"effort": "medium"},
)

print(response.output_text)

Gemini’s Thinking_level

Gemini 3-series models control reasoning depth through thinking_level, which replaced the older numeric thinking_budget parameter. Gemini 3 and Gemini 3 Flash default to HIGH, allowing the model more tokens for complex, multi-step reasoning; Gemini 3 Flash also introduced MINIMAL and MEDIUM levels for throughput-optimized and balanced use cases respectively. As of Gemini 3.5 Flash, Google’s own migration guidance explicitly recommends replacing thinking_budget with thinking_level across all Gemini 3.x models, and default effort on that model shifted from high to medium.

You cannot use thinking_level and the legacy thinking_budget parameter in the same request — Google’s API returns a 400 error if both are set. Any code still passing thinking_budget needs to migrate before combining it with newer Gemini 3.x features.

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3-pro-preview",
    contents="Summarize the trade-offs between microservices and monoliths.",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="high")
    ),
)

print(response.text)

Side-by-Side Comparison

This is the core of the reasoning effort parameter comparison: the table below lines up the three parameters directly. Because level names overlap (“medium,” “high”) without meaning the same thing, treat this as a translation reference rather than a one-to-one mapping.

*Level availability varies by specific Gemini 3.x model; check the model page before assuming a level is supported.

Migration Pitfalls

The three gotchas above are the ones most likely to break a working integration when a team ports a workload from one provider to another. Any reasoning effort parameter comparison used for a real migration should also check the following:

1. Don’t assume “medium” means the same thing twice. Verify quality and latency on your own evaluation set after any cross-provider migration rather than reusing a level name.
2. Check tool-calling interactions separately. Lower effort levels on Claude reduce the number of tool calls made; GPT-5.6’s tool-calling restriction at non-“none” effort is a hard API constraint, not just a quality tradeoff.
3. Re-check defaults on every model upgrade. Anthropic, OpenAI, and Google have all changed default effort/level behavior within a single model family in 2026 — a default that held on one version isn’t guaranteed on the next.

Choosing a Level for Your Workload

There is no single “equivalent” setting across all three APIs. The practical takeaway from this reasoning effort parameter comparison is to treat effort/reasoning/thinking level as a per-provider tuning knob, verified against your own evaluation set, rather than a value you carry over unchanged.

FAQ

Sources

• Claude Platform Docs — Effort — official parameter reference, levels, and per-model defaults
• OpenAI API — Reasoning models guide — official reference for reasoning.effort and reasoning.mode
• OpenAI API — Model guidance (GPT-5.6) — confirms GPT-5.6’s six effort levels
• Google AI for Developers — Gemini thinking — official thinking_level reference
• Google AI for Developers — What’s new in Gemini 3.5 Flash — confirms thinking_budget deprecation and default change

This reasoning effort parameter comparison synthesizes official API documentation from Anthropic, OpenAI, and Google, current as of August 8, 2026. All three providers have changed default reasoning-effort behavior more than once within 2026; parameter names, levels, and defaults should be re-verified against the linked vendor documentation before relying on them in production, and this page is scheduled for quarterly review.