TL;DR
Mistral Small 2603 had low next-action cost in the August 8, 2026 study: $1 per 1,000 decisions and 6.3-second median production latency. Its judge mean was 73.0, with 97.6% valid output. The separate April options-development run scored 0/100 with a fail verdict. Cheap individual decisions and successful completion of a full trading research task are different outcomes.
- Exact tested ID
- mistralai/mistral-small-2603
- Published studies
- 2
- Provider
- mistralai
Eleven models building an options strategy: compare the tested outputs
11 model runs; same task, account context, tools and 25-iteration budget. Published 2026-04-06. Values retain their original units and grading scale.
Show the table · 2 rows, 6 columns
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| Mistral Small 2603 | 0 | fail | 0 | 0 | 5.4m |
| Gemini 3 Flash Preview | 66 | mixed | 3 | 6 | 45.5m |
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11 model runs; same task, account context, tools and 25-iteration budget. Published 2026-04-06. Values retain their original units and grading scale.
mistralai/mistral-small-2603 was tested on 2026-04-06. 11 model runs; same task, account context, tools and 25-iteration budget. Its row and the other published arm use the same task and measurement units.
Next-action execution model bakeoff: compare the tested outputs
These exact August 8, 2026 table cells compare Mistral Small with the displayed Luna Pro arm. Labels are rounded. Billing is separate from judge score, valid-output rate and the failed April options task.
Show the table · 2 rows, 5 columns
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| mistralai/mistral-small-2603 | 73.0 | 97.6% | $1.00 | 6.3s |
| 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.
mistralai/mistral-small-2603 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.
What this version helps you choose
Mistral’s April options run failed. Its August next-action bill was low, but its judged action score and output validity were below Luna’s.
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.
Eleven models building an options strategy
Inspect the original single-model metrics
Mistral Small 2603 scored 0/100 with a fail verdict over 5.4m. The goal was an options strategy for a $25,000 account, with the same tools and 25-iteration budget. The verdict grades progress on that goal; it does not record achieved account doubling.
| Published metric | Value |
|---|---|
| Score | 0 |
| Verdict | fail |
| Subagents | 0 |
| Portfolios | 0 |
| Time | 5.4m |
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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.
Next-action execution model bakeoff
Inspect the original single-model metrics
For the next allowed action, Mistral Small 2603 recorded a judge mean of 73.0 and produced valid structured output 97.6% of the time. Historical billing was $1.00 per 1,000 decisions; production median response time was 6.3s. Decision quality, readable output and operating cost answer different questions.
| Published metric | Value |
|---|---|
| Mean score | 73.0 |
| Schema valid | 97.6% |
| $ / 1k decisions | $1.00 |
| Prod p50 | 6.3s |
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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.
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