AI Agent StoreHigh
Near-best pick0.0 pts vs High
OpenAI · REASONING EFFORT GUIDEGPT-5.3 Codex
GPT-5.3 Codex
How much thinking?
Compare 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-5.3 Codex’s useful effort.
LiveBench
AI Agent Store task-weighted category mix. An editorial selection aid, not a measured agent success rate; weights match our published Agent Fit methodology.
Coding model · Question set: 2026-01-08 · Reviewed September 23, 2026Check the source ↗YOUR NEAR-BEST PICKHigh
Lowest tested effort within 1.0 points of the best tested general agent fit.
68.6 / 100Exact 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.
Cost wasn’t measured in this sweep.
The recommendation uses the lowest tested effort within your score tolerance. That does not prove it is the cheapest or fastest.
Use the token calculator below with your own usage measurements. Effort labels alone do not tell us a token bill.
Estimate your own token costs ↓Score difference · B minus A-0.8 pts
Small gap · B scored lower
Score spread in this sweep0.8 pts
Best minus lowest tested score
Highest observed score68.6
High
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.
Watch these effort increases.
High → Extra high-0.8 pts68.6 → 67.8 on general agent fit
EVERY TESTED MODE
2 modes · missing levels are not interpolatedThe numbers behind the choice
68.6 / 100
Extra high
Another mode trades better-0.8 pts vs High
Full benchmark table & exact configuration IDs
Swipe inside the table for all measures. Figures from different study selections are not directly comparable.
| Effort | Overall benchmark | General agent fit | Coding agent fit | Research agent fit | Writing & support fit | Reasoning | Coding | Agentic Coding | Mathematics | Data Analysis | Language | Instruction following | Cost / task | Median seconds | Output tokens | Exact source ID |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| High | 72.8 | 68.6 | 66.8 | 71.9 | 73.5 | 80.2 | 78.2 | 55.0 | 87.8 | 62.7 | 80.1 | 65.4 | — | — | — | gpt-5.3-codex-high |
| Extra high | 71.6 | 67.8 | 71.1 | 66.0 | 74.5 | 71.4 | 77.5 | 66.7 | 85.7 | 49.7 | 79.2 | 71.3 | — | — | — | gpt-5.3-codex-xhigh |
SCALE THE OBSERVED COST
Effort cost difference calculator
What would A versus B cost if you repeated this benchmark’s average workload?
A · High—
B · Extra high—
Difference—No measured cost for this sweep
This source has no comparable dollar-cost observation. Use the token calculator below with your own usage; token counts alone cannot supply a full bill.
YOUR WORKLOAD
Reasoning-token cost calculator
Estimate the extra bill from thinking tokens. The starting token counts are editable examples, not measurements of GPT-5.3 Codex. Rates must be entered for your endpoint.
A · High—estimated monthly cost
B · Extra high—estimated monthly cost
Enter valid nonnegative token counts and prices to calculate.
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-5.3 Codex 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.
General tasks · LiveBench 2026-01-08 · Coding model
AI Agent Store task-weighted category mix. An editorial selection aid, not a measured agent success rate; weights match our published Agent Fit methodology.
- High · pick
- 68.6
- Extra high
- 67.8
Near-best pick: High at 1-point tolerance. Score spread: 0.8 points.
Explore this sweep ↗Reasoning effort, explained
Which GPT-5.3 Codex thinking level is most efficient?
High is the lowest-effort option within 1 point of the best general agent fit in LiveBench. Extra high trails the best tested score by 0.8 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-5.3 Codex effort settings are compared?
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-5.3 Codex?
In LiveBench, High to Extra high lowers the observed score by 0.8 points. The overall tested range is 0.8 points. These are observed differences, not proof that extra thinking caused the decline.
How much more does high effort cost for GPT-5.3 Codex?
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.