AI Agent StoreLow
Tested mode0.0 pts vs Low
$0.0131 / source task
61 s median duration
OpenAI · REASONING EFFORT GUIDEGPT-5.6 Luna
GPT-5.6 Luna
How much thinking?
Compare Low vs Medium vs High vs Extra high 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-5.6 Luna’s useful effort.
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.
35.3 / 100 · $0.0396 / source taskExact 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
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$0.0131
Medium$0.0185
High$0.0307
Extra highNear-best pick$0.0396
Within this benchmark and harness. Your task mix, tools, caching and endpoint can change the result.
Score difference · B minus A+16.3 pts
Large gap · B scored higher
Measured cost · B / A3.02×
Same benchmark workload
Median duration · B / A2.37×
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.
EVERY TESTED MODE
4 modes · missing levels are not interpolatedThe numbers behind the choice
19.0 / 100
Medium
Tested mode+5.0 pts vs Low
$0.0185 / source task
79 s median duration
High
Tested mode+12.3 pts vs Low
$0.0307 / source task
118 s median duration
Extra high
Near-best pick+16.3 pts vs Low
$0.0396 / source task
145 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.
| Effort | Analytics score | Cost / task | Median seconds | Output tokens | Exact source ID |
|---|---|---|---|---|---|
| Low | 19.0 | $0.0131 | 61.3 | — | GPT-5.6 Luna · Low |
| Medium | 24.0 | $0.0185 | 78.8 | — | GPT-5.6 Luna · Medium |
| High | 31.3 | $0.0307 | 118.1 | — | GPT-5.6 Luna · High |
| Extra high | 35.3 | $0.0396 | 145.3 | — | GPT-5.6 Luna · XHigh |
SCALE THE OBSERVED COST
Effort cost difference calculator
What would A versus B cost if you repeated this benchmark’s average workload?
A · Low$13.14
B · Extra high$39.64
Difference$26.5more 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 Luna. Rates start from our September 23 model snapshot.
A · Low$4.4estimated monthly cost
B · Extra high$9.2estimated monthly cost
Mode B costs $4.8 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.
GPT-5.6 Luna 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.
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
- 19.0 · $0.0131 / task
- Medium
- 24.0 · $0.0185 / task
- High
- 31.3 · $0.0307 / task
- Extra high · pick
- 35.3 · $0.0396 / task
Near-best pick: Extra high at 1-point tolerance. Score spread: 16.3 points.
Explore this sweep ↗Reasoning effort, explained
Which GPT-5.6 Luna 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. The tested score range is 16.3 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-5.6 Luna effort settings are compared?
Low, Medium, High, Extra high 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 Luna?
In the first listed Hex DataBench v1.1 sweep, the tested score range is 16.3 points and no adjacent effort increase lowers the score. This does not guarantee a gain on every task or in another harness.
How much more does high effort cost for GPT-5.6 Luna?
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.