Evidence for this exact version
The results apply to the exact model ID above. Newer versions need their own evaluation. Scores with different grading scales are kept separate, and a model result alone does not evaluate a complete trading agent.
The provider name identifies the model family. The tools and harness around the model determine what it can do.
Use these results to choose a test
Start with the study closest to your task. Compare this version with the other models in that study using the same rubric and cost units. A strong result on planning does not establish stock-screening or coding quality.
Before adopting a version, try representative inputs in your own harness. Check failure cases and valid outputs alongside score, elapsed time and billed cost. The tables below describe the published runs; they are not current price quotes or live-return forecasts.
Eleven models building an options strategy
Gemini 3.1 Flash Lite recorded 53 for score in the 2026-04-06 publication. 11 model runs; same task, account context, tools and 25-iteration budget.
Study-wide context: Gemini 3 Flash Preview has the highest published score, 66/100. The evaluator still calls that mixed and asks for more iteration; no result proves the account-doubling objective.
The publication presents one run per model in this comparison. It does not establish seed-to-seed uncertainty or a general model-quality ranking.
The scorecard and narrative disagree on some subagent/portfolio counts and on how consistently the selected Flash strategy was positive across regimes. The table preserves the published scorecard; conflicting process counts and regime claims are not resolved by inference.
The article describes historical simulations and deployment actions. This study page does not assert independently verified live account returns, achieved doubling, or a deployment recommendation.
| Score | 53 |
| Verdict | weak |
| Subagents | 0 |
| Portfolios | 6 |
| Time | 5.3m |
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Planning models on 29 frozen conversations
Gemini 3.1 Flash Lite recorded 0.586 for score in the 2026-08-08 publication. 16-model campaign, 29 frozen conversations; 14 model rows shown in the public table.
Study-wide context: GPT 5.6 Luna was the published cost-oriented selection among similar-quality planning candidates. Muse Spark 1.1 had the highest raw mean; those are different claims.
The public table omits two poolside arms for space. This snapshot contains the 14 published rows, not a reconstruction of their private outputs.
The top raw score is Muse Spark 1.1 at 0.759. GPT 5.6 Luna scored 0.731 at a reported $0.0012 per decision; the published selection favored cost among similar-quality candidates, rather than claiming the highest score.
The source reports a paired comparison with Opus as inconclusive. This page does not turn small raw gaps into a confident quality winner.
| Score | 0.586 |
| +/- SE | 0.066 |
| $ / decision | $0.0031 |
| p50 | 1.8s |
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