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OpenAI · REASONING EFFORT GUIDE

GPT-5.6 Sol
How much thinking?

Compare Low vs Medium vs High vs Extra high vs Max where tested. Find the effort setting that fits your task, and see how much extra reasoning changes the result.

SAME MODEL. DIFFERENT THINKING.

Choose a thinking level for GPT-5.6 Sol

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Hex DataBench v1.1

Published blended DataBench score, on a 0–100 scale. Judge-based analytical task performance; not a general intelligence or guaranteed success rate.

Analytics in the Hex agent harness · Leaderboard updated: 2026-09-22 · Reviewed 2026-10-15Check the source ↗
SUGGESTED SETTING TO TESTMedium

Lowest measured cost within 1.0 points of the best tested analytics score.

39.7 / 100 · $0.6131 / source task
Exact best1.0 pts10 pts

Set 0 for the highest score. Increase the gap to explore cheaper or lighter options.

THE EFFORT CURVE

Does more thinking help?

● Near-best pickOrange = score decreased

025507510029.7Low39.7Medium38.0High40.3Xhigh
025507510029.7Low39.7Medium38.0High40.3Xhigh

Scores out of 100 · Full 0–100 scale · tap a point to select mode B. Cyan band: within 1.0 points of the best tested score.

WHAT YOU SPEND

Cost of more effort

Measured average USD per benchmark task.

Low$0.3694
MediumNear-best pick$0.6131
High$0.7485
Extra high$0.9831

Measured in this study with its particular tools and setup. Your task mix, tools, caching and endpoint can change the result.

Score difference · B minus A+0.7 pts

Small gap · B scored higher

Measured cost · B / A1.60×

Same benchmark workload

Median duration · B / A1.57×

Measured with the study’s testing setup

We describe gaps under 1 point as small, 1–under 5 as noticeable, and 5 or more as large. These are reading aids, not proof that a difference would hold up in repeated tests. Equal scores can still hide different mistakes.

WHERE MORE SCORED LOWER

When more thinking scored lower

Medium → High-1.7 pts39.7 → 38.0 on analytics score

These drops can reflect the tasks, variation between runs, testing tools or errors. A lower score alone does not prove the model was “overthinking.”
EVERY TESTED MODE

Results for each tested setting

4 modes · untested settings are left blank

Low

Tested setting
29.7 / 100

-10.0 pts vs Medium

$0.3694 / source task

76 s median duration

Medium

Suggested setting
39.7 / 100

0.0 pts vs Medium

$0.6131 / source task

134 s median duration

High

A better tradeoff is available
38.0 / 100

-1.7 pts vs Medium

$0.7485 / source task

173 s median duration

Extra high

Tested setting
40.3 / 100

+0.7 pts vs Medium

$0.9831 / source task

210 s median duration

Full benchmark table & exact configuration IDs

Swipe inside the table for all measures. Figures from different study selections are not directly comparable.

GPT-5.6 Sol · Hex DataBench v1.1 · Analytics in the Hex agent harness
EffortAnalytics scoreCost / taskMedian secondsOutput tokensExact source ID
Low29.7$0.369476.2—GPT-5.6 Sol · Low
Medium39.7$0.6131134.3—GPT-5.6 Sol · Medium
High38.0$0.7485173.3—GPT-5.6 Sol · High
Extra high40.3$0.9831210.2—GPT-5.6 Sol · XHigh
SCALE THE OBSERVED COST

Effort cost difference calculator

What would A versus B cost if you repeated this benchmark’s average workload?

A · Medium$613.09
B · Extra high$983.08
Difference$369.99more with B

A source-workload scenario using average measured USD costs. Source prices, caching and tools are already reflected in those observed costs. Your agent’s costs may differ.

YOUR WORKLOAD

Reasoning-token cost calculator

Estimate the extra bill from thinking tokens. The starting token counts are editable examples, not measurements of GPT-5.6 Sol. Rates start from the current model snapshot.

A · Medium$80estimated monthly cost
B · Extra high$160estimated monthly cost

Mode B costs $80 more per month with these assumptions.

This estimate uses the token counts and prices you enter. Excludes retries, tools, caching, long-context premiums, hosting and taxes. Includes thinking in output billing; do not also include it in “visible output.” Check endpoint limits separately. Changing mode does not automatically predict tokens or quality.

READ ACROSS THE EVIDENCE

GPT-5.6 Sol effort benchmarks at a glance

Each card compares settings tested together in one study. Scores from different benchmarks or question sets measure different work and are not averaged together.

Leaderboard updated · 2026-09-22

Analytics · DataBench v1.1

Published blended DataBench score, on a 0–100 scale. Judge-based analytical task performance; not a general intelligence or guaranteed success rate.

Low
29.7 · $0.3694 / task
Medium · pick
39.7 · $0.6131 / task
High
38.0 · $0.7485 / task
Extra high
40.3 · $0.9831 / task

Near-best pick: Medium within 1 point of the best score. Highest-to-lowest score gap: 10.7 points.

Explore this study ↗

Original source ↗

All results and the exact settings tested

A dash means the study did not report that result. This table compares only the versions and settings tested on the same questions in this study.

GPT-5.6 Sol · Analytics · DataBench v1.1 · Analytics in the Hex agent harness
Effort / budgetAnalytics scoreMeasured USD / taskMedian seconds / taskExact source configuration
Low29.7$0.369476.2GPT-5.6 Sol · Low
Medium39.7$0.6131134.3GPT-5.6 Sol · Medium
High38.0$0.7485173.3GPT-5.6 Sol · High
Extra high40.3$0.9831210.2GPT-5.6 Sol · XHigh

Download this study’s data (CSV) ↓

EXPLORATORY CASE STUDY

Five efforts · one arithmetic prompt

Correct answers in three repeats of one prompt: count ordered integer pairs from 1 to 60 whose product is a perfect square; expected answer 158. One miss changes the score by 33.3 points. This is not a general reasoning benchmark.

Low · pick
100.0
Medium
100.0
High
100.0
Extra high
100.0
Max
100.0

Near-best pick: Low within 1 point of the best score. Highest-to-lowest score gap: 0.0 points.

Explore this study ↗

Original source ↗

All results and the exact settings tested

A dash means the study did not report that result. This table compares only the versions and settings tested on the same questions in this study.

GPT-5.6 Sol · Five efforts · one arithmetic prompt · One fixed arithmetic prompt × 3 repeats · Codex Pro · concurrency 3
Effort / budgetArithmetic runs correctMedian seconds / taskAverage output tokensExact source configuration
Low100.014.3710gpt-5.6-sol / low / reasoning prompt
Medium100.022.01,090gpt-5.6-sol / medium / reasoning prompt
High100.022.81,126gpt-5.6-sol / high / reasoning prompt
Extra high100.022.11,224gpt-5.6-sol / xhigh / reasoning prompt
Max100.024.51,498gpt-5.6-sol / max / reasoning prompt

Download this study’s data (CSV) ↓

Data: Joonlab · Codex subscription study. CC BY 4.0 ↗. AI Agent Store aggregates the published rows and adapts their labels. One arithmetic question and three repeats per setting cannot establish general accuracy. The displayed output and reasoning tokens are means of raw runs; durations are medians. All five Sol6.1 settings answered this question correctly, so extra reasoning showed no accuracy gain on this example. These observations predate Ultrafast and do not compare service tiers.

Reasoning effort, explained

Which GPT-5.6 Sol thinking level is most efficient?

Medium is the lowest-cost option within 1 point of the best analytics score in Hex DataBench v1.1. The tested score range is 10.7 points. This allows a score up to 1 point below the best result in the first listed study. Change the study, task and allowed score gap above to explore another comparison. It is a shortlist for testing your own workload.

Which GPT-5.6 Sol effort settings are compared?

Low, Medium, High, Extra high, Max appear in the collected studies. Each chart only includes modes tested together in that study. These published labels do not establish that every endpoint or application exposes every setting.

Does more effort improve GPT-5.6 Sol?

In Hex DataBench v1.1, Medium to High lowers the observed score by 1.7 points. The overall tested range is 10.7 points. These are observed differences, not proof that extra thinking caused the decline.

How much more does high effort cost for GPT-5.6 Sol?

Use the A/B selector and measured-cost calculator when the source reports dollar costs. For your own agent, enter average input, visible output and thinking tokens in the token calculator. Higher effort is adaptive, so the label itself cannot tell you the bill. Missing costs stay missing.

Keep comparing

AI Agent Store · Model comparisons · Data reviewed 2026-10-15Sources, limitations & corrections ↗