TL;DR
Gemini Flash Preview made more progress than GPT-5.4 on the April 6, 2026 options task: 66 with a mixed verdict versus zero with a fail verdict. Flash's evaluator still recommended further iteration. Each model had one run with the same tools and 25-iteration budget. Conflicting process counts and absent billing limit cost comparisons, and these workflow scores do not record achieved account doubling.
Eleven models building an options strategy: published results
11 model runs; same task, account context, tools and 25-iteration budget. Published 2026-04-06. Each bar uses the original task scale. The original table retains all other dimensions and qualifications.
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| Gemini 3 Flash Preview | 66 | mixed | 3 | 6 | 45.5m |
| GPT-5.4 | 0 | fail | 0 | 0 | 10.7m |
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11 model runs; same task, account context, tools and 25-iteration budget. Published 2026-04-06.
Which fits the tested job?
Flash Preview made more progress on this options workflow: it scored 66/mixed while GPT 5.4 scored 0/fail. Flash's result still needed iteration, so the supported choice is a candidate for another options-development test rather than a profitable-strategy endorsement.
Eleven models building an options strategy
Flash took 45.5m versus GPT 5.4's 10.7m in the scorecard. A short failed run is not evidence of greater efficiency at completing the objective. This comparison is one run per exact model under the same 25-iteration budget. Historical process-count conflicts and the absence of measured billing prevent cost or exploration-efficiency claims.
Develop an options strategy: follow the published task
A score measures how well the workflow addressed that objective. Portfolio and subagent counts describe activity; the scorecard and narrative disagree on some counts.
The same prompt went to all 11 models
Look at my watchlist and current market conditions, design a profitable options trading strategy that I should use at Monday at open. My goal is to double my $25,000 Public Portfolio live-trading account this year.
The models had the same watchlist, market data, account context, tools and 25-iteration budget.
Flash acted; GPT-5.4 handed the next step back
The published Flash trace fetched market data, launched two specialist agents, read their backtests and called deployPortfolio. GPT-5.4 fetched market data and planned three tracks, then stopped with: "Please proceed by launching those subagents."
The trace demonstrates the difference between choosing the next action and carrying it out. Its Flash process counts disagree with the preserved scorecard.
Read the actual Flash evaluator result
The evaluator returned overallScore 66, verdict mixed and deploymentVerdict iterate_first for Regime-Adaptive Options (NVDA/META). Its listed yearly returns were 2022 +31.22%, 2023 +15.41% and 2024 +0.34%, averaging +15.66%.
The evaluator capped strategy fitness at 3/10 because the reported average was below 30%. It recommended more iteration despite the trace showing a deployment action. Those separate test-year returns do not record achieved account doubling.
Results from tasks both models attempted
The tested model IDs are google/gemini-3-flash-preview and openai/gpt-5.4. Every result table below comes from a study in which both participated.
Compare the measured quality, historical billed cost and elapsed time within the same task and rubric.
Scores from different tasks are not combined into one winner score.
Eleven models building an options strategy: read these measurements
Read the complete rubric, sample and billing notes
11 model runs; same task, account context, tools and 25-iteration budget
Develop an options strategy for a $25,000 account with the stated goal of doubling it within a year.
Scoring
The evaluator weights strategy fitness 40%, evidence 25%, exploration 20% and risk realism 15%. No deployment caps fitness at 2/10; single-year evidence caps it at 4/10. The score is a judgment, not a probability of doubling money.
Cost units
This scorecard has no measured billed-dollar cost column.
Time units
Time includes tool use and historical backtesting, not one response.
Subagent
An agent delegated part of the research to another agent.
Portfolio
A strategy artifact created during the run. Its existence alone does not establish a successful strategy.
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