AI Agent StoreHigh
Suggested setting0.0 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.
Choose a thinking level for GPT-5.3 Codex
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 2026-10-16Check the source ↗SUGGESTED SETTING TO TESTHigh
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.
This study has no comparable cost data.
The recommendation uses the lowest tested effort within your allowed score gap. 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
Highest minus lowest score in this study0.8 pts
Best minus lowest tested score
Highest observed score68.6
High
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.
When more thinking scored lower
High → Extra high-0.8 pts68.6 → 67.8 on general agent fit
EVERY TESTED MODE
2 modes · untested settings are left blankResults for each tested setting
68.6 / 100
Extra high
A better tradeoff is available-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—This study did not report comparable costs
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.
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.
GPT-5.3 Codex 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.
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 within 1 point of the best score. Highest-to-lowest score gap: 0.8 points.
Explore this study ↗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.
| Effort / budget | Overall benchmark | General agent fit | Coding agent fit | Research agent fit | Writing & support fit | Reasoning | Coding | Agentic Coding | Mathematics | Data Analysis | Language | Instruction following | Exact source configuration |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
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 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-5.3 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.