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OpenAI · REASONING EFFORT GUIDE

GPT-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.

Find GPT-6 Sol’s useful effort.

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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 September 23, 2026Check the source ↗
YOUR NEAR-BEST PICKExtra high

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

61.3 / 100 · $0.5817 / 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

025507510050.3Low51.3Medium54.7High61.3Xhigh57.0Max
025507510050.3Low51.3Medium54.7High61.3Xhigh57.0Max

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$0.1748
Medium$0.2708
High$0.3874
Extra highNear-best pick$0.5817
Max$0.9845

Within this benchmark and harness. Your task mix, tools, caching and endpoint can change the result.

Score difference · B minus A-4.3 pts

Noticeable gap · B scored lower

Measured cost · B / A1.69×

Same benchmark workload

Median duration · B / A1.89×

Measured in the source harness

Gap labels are editorial: under 1 point is small, 1–under 5 noticeable, 5+ large. They do not establish statistical significance. A tied score does not prove equivalent behavior.

WHERE MORE SCORED LOWER

Watch these effort increases.

Extra highMax-4.3 pts61.357.0 on analytics score

Observed reversals within this sweep. They can reflect task fit, sampling, harness behavior or errors; the score alone does not prove “overthinking.”
EVERY TESTED MODE

The numbers behind the choice

5 modes · missing levels are not interpolated

Low

Tested mode
50.3 / 100

-11.0 pts vs Extra high

$0.1748 / source task

98 s median duration

Medium

Tested mode
51.3 / 100

-10.0 pts vs Extra high

$0.2708 / source task

146 s median duration

High

Tested mode
54.7 / 100

-6.7 pts vs Extra high

$0.3874 / source task

238 s median duration

Extra high

Near-best pick
61.3 / 100

0.0 pts vs Extra high

$0.5817 / source task

327 s median duration

Max

Another mode trades better
57.0 / 100

-4.3 pts vs Extra high

$0.9845 / source task

618 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-6 Sol · Hex DataBench v1.1 · Analytics in the Hex agent harness
EffortAnalytics scoreCost / taskMedian secondsOutput tokensExact source ID
Low50.3$0.174897.6GPT-6 Sol · Low
Medium51.3$0.2708146.2GPT-6 Sol · Medium
High54.7$0.3874238.0GPT-6 Sol · High
Extra high61.3$0.5817326.7GPT-6 Sol · XHigh
Max57.0$0.9845617.5GPT-6 Sol · Max
SCALE THE OBSERVED COST

Effort cost difference calculator

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

A · Extra high$581.65
B · Max$984.49
Difference$402.83more 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-6 Sol. Rates start from our September 23 model snapshot.

A · Extra high$40estimated monthly cost
B · Max$80estimated monthly cost

Mode B costs $40 more per month with these assumptions.

Standard text-token arithmetic. 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-6 Sol effort benchmarks at a glance

Each card is its own comparable sweep. 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
50.3 · $0.1748 / task
Medium
51.3 · $0.2708 / task
High
54.7 · $0.3874 / task
Extra high · pick
61.3 · $0.5817 / task
Max
57.0 · $0.9845 / task

Near-best pick: Extra high at 1-point tolerance. Score spread: 11.0 points.

Explore this sweep ↗

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Reasoning effort, explained

Which GPT-6 Sol thinking level is most efficient?

Extra high is the lowest-cost option within 1 point of the best analytics score in Hex DataBench v1.1. Max trails the best tested score by 4.3 points. This uses a 1-point practical tolerance in the first listed study. Change the evidence, task measure and tolerance above to get a recommendation for another benchmark. It is a shortlist for testing your own workload.

Which GPT-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-6 Sol?

In Hex DataBench v1.1, Extra high to Max lowers the observed score by 4.3 points. The overall tested range is 11.0 points. These are observed differences, not proof that extra thinking caused the decline.

How much more does high effort cost for GPT-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.

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AI Agent Store · Model intelligence · Snapshot 2026-09-23Sources, limitations & corrections ↗