TL;DR
Gemini 3 Flash Preview scored 66 with a mixed verdict on the April 2026 options-development task, versus Claude Sonnet 4.6's 50 and weak verdict. Sonnet finished in 32 minutes; Flash took 45.5. The same task, tools and 25-iteration budget were used, but each model had one run. Flash's higher score still required more iteration, and the table publishes no dollar-cost comparison or verified live investment returns.
Eleven models building an options strategy: published results
Same task, tools and 25-iteration budget; one run per model. Flash took 45.5 minutes and received a mixed verdict; Sonnet took 32 minutes and received a weak verdict. These evaluator scores are not investment returns.
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| Gemini 3 Flash Preview | 66 | mixed | 3 | 6 | 45.5m |
| Claude Sonnet 4.6 | 50 | weak | 2 | 5 | 32m |
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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 scored higher on the April options objective, but its 66/100 verdict was still mixed. Sonnet completed the workflow faster with a lower 50/100 score. Neither one-run result establishes a strategy suitable for live deployment.
Eleven models building an options strategy
The preserved scorecard shows Flash 66/mixed in 45.5m and Sonnet 50/weak in 32m. The task rewarded strategy fitness, evidence, exploration and risk realism; it did not measure a model's general reasoning quality or verified live returns. Process-count disagreements remain unresolved, so subagent and portfolio totals are not a tie-breaker. No measured dollar-cost comparison is published.
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 anthropic/claude-sonnet-4.6. 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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