TL;DR
Gemini 3.6 Flash led the published August 2026 stock-query comparison with a 0.841 blended score and 86.4% success across 22 questions. Its average workflow took 26.6 seconds. The same version recorded a 0.662 planning score and an 80.1 next-action score on separate tasks. Those scales and timings differ, and the model was not the published cost-oriented choice for every agent role.
- Exact tested ID
- google/gemini-3.6-flash
- Published studies
- 3
- Provider
Planning models on 29 frozen conversations: compare the tested outputs
Tick: recorded mean score. Whisker: mean ± one recorded standard error (SE), not a confidence interval. Overlap alone does not establish a ranking.
16-model campaign, 29 frozen conversations; 14 model rows shown in the public table. Published 2026-08-08. Values retain their original units and grading scale.
Show the table · 2 rows, 5 columns
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| google/gemini-3.6-flash | 0.662 | 0.070 | $0.0387 | 8.5s |
| meta/muse-spark-1.1 | 0.759 | 0.079 | $0.0229 | 9.6s |
No matching rows. Clear the filter to see all records.
16-model campaign, 29 frozen conversations; 14 model rows shown in the public table. Published 2026-08-08. Values retain their original units and grading scale.
google/gemini-3.6-flash was tested on 2026-08-08. 16-model campaign, 29 frozen conversations; 14 model rows shown in the public table. Its row and the other published arm use the same task and measurement units.
Next-action execution model bakeoff: compare the tested outputs
23-model campaign; 69 frozen decisions × 3 samples; 12 published model rows. Published 2026-08-08. Values retain their original units and grading scale.
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| google/gemini-3.6-flash | 80.1 | 100.0% | $24.21 | 7.7s |
| openai/gpt-5.6-luna-pro | 91.7 | 100.0% | $25.05 | 9.9s |
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23-model campaign; 69 frozen decisions × 3 samples; 12 published model rows. Published 2026-08-08. Values retain their original units and grading scale.
google/gemini-3.6-flash was tested on 2026-08-08. 23-model campaign; 69 frozen decisions × 3 samples; 12 published model rows. Its row and the other published arm below use the same task and measurement units.
Models answering 22 stock questions with SQL: compare the tested outputs
Original units preserved. Average score is the historical blended measure; this table is not a new run. The unchanged table retains the other reported dimensions. These scores describe the tested task rather than investment returns.
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| google/gemini-3.6-flash | 0.841 | 1.00 | 86.4% | 26.6s |
| google/gemini-3-flash-preview | 0.791 | 1.00 | 81.8% | 18.9s |
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Original units preserved. Average score is the historical blended measure; this table is not a new run.
google/gemini-3.6-flash was tested on 2026-08-08. 22 natural-language questions; 6 published model rows. Its row and the other published arm below use the same task and measurement units.
What this version helps you choose
This version led the stock-query blended score and success rate. The same model was not the published cost-oriented choice for planning or next-action execution.
Route a request and plan the work: follow the published task
A useful planner chooses the right kind of work before doing it. The complete customer conversations and system prompts remain private.
Published task summary
An incoming request and its existing conversation.
Choose whether to write a plan, call a catalog tool, ask for clarification or continue an existing agent.
Read what came back
A routing decision and plan. Every model receives the same saved conversation; the test compares that decision before fresh research begins.
Replay 29 frozen conversations across the model arms, then compare graded plan scores, billing and response time.
Planning models on 29 frozen conversations
For routing and planning, Gemini 3.6 Flash recorded a mean score of 0.662 with standard error 0.070. Historical billing was $0.0387 per decision, and median response time was 8.5s. Compare that cost and response time with the other models in the same 29-conversation study; the mean alone does not establish a decisive quality advantage.
Show the table · 4 rows, 2 columns
| Published metric | Value |
|---|---|
| Score | 0.662 |
| +/- SE | 0.070 |
| $ / decision | $0.0387 |
| p50 | 8.5s |
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Planning models on 29 frozen conversations: read these measurements
Read the complete rubric, sample and billing notes
16-model campaign, 29 frozen conversations; 14 model rows shown in the public table
Route an incoming request: write a plan, call a catalog tool, ask for clarification or continue an existing agent.
Scoring
Top planning means were close at this sample size. The source's paired comparison with Opus was inconclusive.
Cost units
Cost is provider-billed dollars per decision. Prefix reuse can make it differ from list price multiplied by every input token.
Time units
p50 is median latency in seconds, not the duration of complete research.
Frozen conversation
A saved input kept identical across model arms so their decisions can be compared.
Standard error (SE)
An estimate of uncertainty around the sample mean. A small mean difference needs more evidence than the ranking alone.
Next-action execution model bakeoff
For the next allowed action, Gemini 3.6 Flash recorded a judge mean of 80.1 and produced valid structured output 100.0% of the time. Historical billing was $24.21 per 1,000 decisions; production median response time was 7.7s. Decision quality, readable output and operating cost answer different questions.
Show the table · 4 rows, 2 columns
| Published metric | Value |
|---|---|
| Mean score | 80.1 |
| Schema valid | 100.0% |
| $ / 1k decisions | $24.21 |
| Prod p50 | 7.7s |
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Choose the next allowed agent action: follow the published task
A well-formed action can still be the wrong decision. Keep output validity and judged decision quality separate.
Published task summary
The existing plan, conversation and results returned by tools.
Choose one next allowed action, or finish with an answer when the evidence is sufficient.
Read what came back
A structured action. The replay records the proposed action without executing its tools.
Replay 69 saved decisions three times each. Check the required output structure separately from the judge score.
Next-action execution model bakeoff: read these measurements
Read the complete rubric, sample and billing notes
23-model campaign; 69 frozen decisions × 3 samples; 12 published model rows
Choose one next action in an unfinished trading-agent workflow. This replay measures individual decisions, not a fresh complete trading run.
Scoring
Mean action score comes from the judge. Schema validity is the share of actions matching the required output structure; a valid structure can still contain the wrong action. Switching judges reduced the Luna-versus-DeepSeek score gap from 13.9 to 1.5 points.
Cost units
Cost uses historical provider billing per 1,000 decisions. Reused prefixes earned cache discounts; the cached-token split was not retained.
Time units
Prod p50 is median production response latency, separate from replay quality and complete agent wall time.
Models answering 22 stock questions with SQL
On 22 stock questions, Gemini 3.6 Flash recorded a blended score of 0.841, success rate 86.4% and average workflow time 26.6s. The blended score includes zeros for non-answers; it is different from correctness among answers that reached grading. The example below explains the data task without attributing its answer to this model.
Show the table · 4 rows, 2 columns
| Published metric | Value |
|---|---|
| Average score | 0.841 |
| Median | 1.00 |
| Success rate | 86.4% |
| Avg execution | 26.6s |
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Answer a stock question with a data query: follow the published task
This published example explains the task. It is not an identified scored fixture or an answer attributed to any one model. Stored P/E and price divided by the displayed EPS are different fields in its query.
Published worked example, without model attribution
What were the trailing P/E ratios for TSLA and GOOGL on 2025-05-21?
Translate the question into DuckDB SQL, a query over the financial data, then inspect the returned rows and explain the answer.
Read what came back
The original example reported GOOGL at $168.56 with stored P/E 18.46, and TSLA at $334.62 with stored P/E 169.17. Both rows show 2025-05-21.
Inspect both tickers, the requested date and four distinct fiscal quarters of earnings. The original EPS query did not deduplicate fiscal periods, and the correction has not been rerun.
Models answering 22 stock questions with SQL: read these measurements
Read the complete rubric, sample and billing notes
22 natural-language questions; 6 published model rows
Answer financial questions by writing and executing DuckDB SQL against the stock data lake.
Scoring
The average is a blended score across all question cells, including zeros for clarification or execution failure. Success rate and graded-only correctness answer different questions; none measures investment returns.
Cost units
The published screening table has no per-model cost column.
Time units
Average execution time covers the SQL-answer workflow, not the executor study's production p50.
Graded-only mean
The mean among answers that reached grading. Luna recorded 0.931 across 13 graded cells; nine ungraded cells lowered its blended score to 0.550.
TTM earnings / P/E
Trailing twelve-month earnings cover four fiscal quarters. Price-to-earnings relates price to earnings; the published query reads a stored P/E field rather than recomputing it from the displayed EPS.
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