GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Rebalance to target weights with whole shares and highest-cost-first tax lots
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.05
- Wall time
- 51s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Rebalance to target weights with whole shares and highest-cost-first tax lots `input.json` is a **synthetic** portfolio: cash, prices, target weights and the tax lots held. Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` plans the rebalancing trades and their tax effect. Nothing is traded; this only computes a plan. We re-run it on fresh inputs, so do not hard-code anything. ## Input `as_of` (ISO date), `cash`, `prices` (symbol -> price), `targets` (symbol -> weight; weights sum to 1; a held symbol missing from `targets` has target 0; a target symbol may have no lots), `lots` (each `{"id", "symbol", "shares", "cost_per_share", "acquired"}`), `short_term_rate`, `long_term_rate`. ## Rules 1. Use `decimal.Decimal` for money (convert numbers with `Decimal(str(x))`); round with ROUND_HALF_UP to cents when writing. 2. total_value = cash + sum over symbols of (shares held * price). Current value and target value (= weight * total_value) of each symbol are computed once, before any trade. 3. Work through symbols in alphabetical order. **Sells first:** if current > target, sell floor((current - target) / price) shares (never more than held). Then **buys**, again alphabetically: if target > current, buy floor(min(target - current, cash available now) / price) shares, where cash available includes all sale proceeds and decreases with each buy. No fees. 4. Lots for a sale are used highest `cost_per_share` first, ties by earlier `acquired`, then by `id`; a lot can be partly sold. 5. A lot slice's gain = (price - cost_per_share) * shares sold. It is long-term if (as_of - acquired) in days is greater than 365, otherwise short-term. 6. realized_short_term and realized_long_term are the sums of gains (can be negative). estimated_tax = max(realized_short_term, 0) * short_term_rate + max(realized_long_term, 0) * long_term_rate (no netting between the two). 7. weights_after[symbol] = shares held after trades * price / total_value (6 decimals), for every symbol that is held or targeted. ## Output `answer.json`: ``` {"total_value": ..., "trades": [{"symbol", "side": "sell"|"buy", "shares"}, ...] sorted by symbol then side, "lot_sales": [{"lot_id", "shares", "gain", "term": "short"|"long"}, ...] in the order lots are used (symbols alphabetically), "realized_short_term": ..., "realized_long_term": ..., "estimated_tax": ..., "cash_after": ..., "weights_after": {symbol: weight}} ``` Only trades with at least 1 share are listed. ## Scoring Hidden checks (partial credit = fraction passed; all must pass), money within 0.011, weights within 0.000001, shares and labels exact: `trades` (with total_value), `lot_selection`, `gains_and_tax`, `cash_and_weights` on the given input; `fresh_run_1` and `fresh_run_2_sell_out` (a symbol with target 0 must be sold out; lots held exactly 365 and 366 days appear) on fresh inputs. Each run has a 60-second limit.
Sells before buys with cash carried, floor to whole shares, lot order with ties, the 365-vs-366-day holding boundary, a held symbol with no target, and no netting in the tax estimate. Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).
These are synthetic test cases for comparing AI models, not financial advice.
Success criteria: 1. trades 2. lot_selection 3. gains_and_tax 4. cash_and_weights 5. fresh_run_1 6. fresh_run_2_sell_out
Setup notes: Use Case Scout batch9 case j12-rebalance-tax-lots (offline fixture). Prepared with batch9/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `python score.py WORKDIR KEYDIR`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 6/6. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 5 (1 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.05
- Wall time
- 51s
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on | 83,808 | 2,158 | $0.05 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking on) via
Codex CLI 0.163.0 (codex exec --json), cloud. - It counts as a success only if it meets every success criterion: 1. trades 2. lot_selection 3. gains_and_tax 4. cash_and_weights 5. fresh_run_1 6. fresh_run_2_sell_out
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
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Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
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{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the input, write `solve.py`, and check the whole-share and tax-lot rules with synthetic cases."}}
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{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'input.json' -g 'solve.py' -g '\"'! .git'\"'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/107_j12/work\ninput.json\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json'", "aggregated_output": "{\n \"as_of\": \"2026-10-19\",\n \"cash\": 13493.04,\n \"prices\": {\n \"AAA\": 308.78,\n \"EMK\": 130.09,\n \"GLD\": 238.03,\n \"TIPS\": 354.24\n },\n \"targets\": {\n \"AAA\": 0.42,\n \"EMK\": 0.118,\n \"GLD\": 0.233,\n \"TIPS\": 0.229\n },\n \"lots\": [\n {\n \"id\": \"L001\",\n \"symbol\": \"AAA\",\n \"shares\": 198,\n \"cost_per_share\": 441.15,\n \"acquired\": \"2025-10-18\"\n },\n {\n \"id\": \"L002\",\n \"symbol\": \"AAA\",\n \"shares\": 42,\n \"cost_per_share\": 305.68,\n \"acquired\": \"2023-07-28\"\n },\n {\n \"id\": \"L003\",\n \"symbol\": \"EMK\",\n \"shares\": 164,\n \"cost_per_share\": 143.97,\n \"acquired\": \"2025-10-19\"\n },\n {\n \"id\": \"L004\",\n \"symbol\": \"GLD\",\n \"shares\": 239,\n \"cost_per_share\": 199.04,\n \"acquired\": \"2025-10-18\"\n },\n {\n \"id\": \"L005\",\n \"symbol\": \"GLD\",\n \"shares\": 277,\n \"cost_per_share\": 203.06,\n \"acquired\": \"2025-10-19\"\n },\n {\n \"id\": \"L006\",\n \"symbol\": \"GLD\",\n \"shares\": 152,\n \"cost_per_share\": 199.58,\n \"acquired\": \"2025-10-19\"\n },\n {\n \"id\": \"L007\",\n \"symbol\": \"AAA\",\n \"shares\": 198,\n \"cost_per_share\": 441.15,\n \"acquired\": \"2026-07-11\"\n }\n ],\n \"short_term_rate\": 0.32,\n \"long_term_rate\": 0.15,\n \"note\": \"Synthetic portfolio and flat illustrative tax rates given for this exercise.\"\n}", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/107_j12/work/solve.py", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/107_j12/work/solve.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python solve.py input.json answer.json && cat answer.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python solve.py input.json answer.json && cat answer.json'", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc 'python3 solve.py input.json answer.json && cat answer.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc 'python3 solve.py input.json answer.json && cat answer.json'", "aggregated_output": "{\n \"total_value\": 329077.48,\n \"trades\": [\n {\n \"symbol\": \"AAA\",\n \"side\": \"buy\",\n \"shares\": 9\n },\n {\n \"symbol\": \"EMK\",\n \"side\": \"buy\",\n \"shares\": 134\n },\n {\n \"symbol\": \"GLD\",\n \"side\": \"sell\",\n \"shares\": 345\n },\n {\n \"symbol\": \"TIPS\",\n \"side\": \"buy\",\n \"shares\": 212\n }\n ],\n \"lot_sales\": [\n {\n \"lot_id\": \"L005\",\n \"shares\": 277,\n \"gain\": 9686.69,\n \"term\": \"short\"\n },\n {\n \"lot_id\": \"L006\",\n \"shares\": 68,\n \"gain\": 2614.6,\n \"term\": \"short\"\n }\n ],\n \"realized_short_term\": 12301.29,\n \"realized_long_term\": 0.0,\n \"estimated_tax\": 3936.41,\n \"cash_after\": 303.43,\n \"weights_after\": {\n \"AAA\": 0.419429,\n \"EMK\": 0.117805,\n \"GLD\": 0.233634,\n \"TIPS\": 0.22821\n }\n}\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nfrom solve import plan\nx = {'as_of':'2026-10-19','cash':0,'prices':{'A':10,'B':10,'C':10},'targets':{'B':0.5,'C':0.5},'lots':[{'id':'later','symbol':'A','shares':2,'cost_per_share':12,'acquired':'2025-10-19'},{'id':'early','symbol':'A','shares':2,'cost_per_share':12,'acquired':'2025-10-18'},{'id':'cheap','symbol':'A','shares':1,'cost_per_share':5,'acquired':'2024-01-01'}],'short_term_rate':0.3,'long_term_rate':0.15}\nr=plan(x)\nassert r['trades']==[{'symbol':'A','side':'sell','shares':5},{'symbol':'B','side':'buy','shares':2},{'symbol':'C','side':'buy','shares':2}]\nassert r['lot_sales']==[{'lot_id':'early','shares':2,'gain':-4.0,'term':'long'},{'lot_id':'later','shares':2,'gain':-4.0,'term':'short'},{'lot_id':'cheap','shares':1,'gain':5.0,'term':'long'}]\nassert r['estimated_tax']==0.15 and r['cash_after']==10 and r['weights_after']=={'A':0.0,'B':0.4,'C':0.4}\nx['targets']={'A':0.5,'B':0.5}\nr=plan(x)\nassert r['lot_sales']==[{'lot_id':'early','shares':2,'gain':-4.0,'term':'long'}]\nassert r['cash_after']==0 and r['weights_after']=={'A':0.6,'B':0.4}\nprint('Passed sell-out, 365/366-day boundary, highest-cost ties, partial sale, cash, weights, and tax assertions.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nfrom solve import plan\nx = {'as_of':'2026-10-19','cash':0,'prices':{'A':10,'B':10,'C':10},'targets':{'B':0.5,'C':0.5},'lots':[{'id':'later','symbol':'A','shares':2,'cost_per_share':12,'acquired':'2025-10-19'},{'id':'early','symbol':'A','shares':2,'cost_per_share':12,'acquired':'2025-10-18'},{'id':'cheap','symbol':'A','shares':1,'cost_per_share':5,'acquired':'2024-01-01'}],'short_term_rate':0.3,'long_term_rate':0.15}\nr=plan(x)\nassert r['trades']==[{'symbol':'A','side':'sell','shares':5},{'symbol':'B','side':'buy','shares':2},{'symbol':'C','side':'buy','shares':2}]\nassert r['lot_sales']==[{'lot_id':'early','shares':2,'gain':-4.0,'term':'long'},{'lot_id':'later','shares':2,'gain':-4.0,'term':'short'},{'lot_id':'cheap','shares':1,'gain':5.0,'term':'long'}]\nassert r['estimated_tax']==0.15 and r['cash_after']==10 and r['weights_after']=={'A':0.0,'B':0.4,'C':0.4}\nx['targets']={'A':0.5,'B':0.5}\nr=plan(x)\nassert r['lot_sales']==[{'lot_id':'early','shares':2,'gain':-4.0,'term':'long'}]\nassert r['cash_after']==0 and r['weights_after']=={'A':0.6,'B':0.4}\nprint('Passed sell-out, 365/366-day boundary, highest-cost ties, partial sale, cash, weights, and tax assertions.')\nPY\"", "aggregated_output": "Passed sell-out, 365/366-day boundary, highest-cost ties, partial sale, cash, weights, and tax assertions.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "agent_message", "text": "Created [solve.py](solve.py) and generated [answer.json](answer.json).\n\nVerified sell-outs, lot ordering, partial sales, 365/366-day tax treatment, cash, and weights.\n\nFor the supplied input: estimated tax **$3,936.41**; cash remaining **$303.43**.\n\nRun with `python3 solve.py input.json answer.json` (`python` is unavailable here)."}}
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