The scores of AI models are all created through specialized efforts, so I compared the audit results of Fable5, Grok4.5, and Kimi K3 for the same trading system. Let's start with the conclusion: Fable5: Best suited as a system level master audit model Kimi: Best suited as a specialized review model for code defects and consistency Grok: It is most suitable as a model for code sorting and scheme divergence, and is not suitable for determining policy modifications separately Best combination: Fable5 comprehensive review+Krok 4.5 code sorting+K3 code review Specific details: Fable5: The strongest system level judgment ability The biggest advantage of Fable5 is not that it reads more code than the other two models, but that it can: Code rules; sizing snapshot； intent ledger； Actual block statistics; Current asset head room; SELL/REDEEM reflux path; Put it into the same causal framework. It uses several crucial real-time indicators: ADD accounts for approximately 43% of the newly added funds in the past 7 days; 84% of funds have been deployed; ETH, SOL, XRP headroom are 0; The main blockage in the past 40 cycles is: blocked_capital efficiency=47 blocked_asset_cap=28 deployment cap=0 runway=0 This allows it to distinguish: A certain mechanism may theoretically limit funds and The current real market is truly limiting the mechanism of funds. In the end, it arrived at: ADD is important for the flow of funds, but the current turnover is mainly due to the recycling end, asset cap, and efficiency filtering, rather than ADD admission itself. This is the judgment closest to the production system audit requirements among the three models. weakness Fable5 still has some over inference: Describing ADD as keeping funds locked in for a longer period of time, in fact, the remaining TTE of ADD is usually shorter than ENTRY; Describing the total holdings of over cap assets as approximately $382 that can be "unlocked directly" without distinguishing between total holdings, excess portion, and tradable portion; Consider redeem_1ag_days=2 in the model as the actual payment delay; The inference that the $5 position is almost not constrained by the daily profit margin per dollar is incorrect, as this indicator has already been normalized by funds; 2-lot minimum ENTRY recommendation may result in systematic loss of coverage. Therefore, Fable5 has the best system direction judgment, but specific numbers and financial indicators still need to be double checked. The most suitable role PRIMARY_SYSTEM_REVIEWER LIVE_OPERATIONAL_DIAGNOSIS CHANGE_PRIORITY_DECISION CROSS_MODULE_ROOT_CAUSE_ANALYSIS 2. Kimi: The strongest ability to detect code defects Kimi's restoration of code structure is quite accurate: Fixed ADD frequency and interval have been retired; ADD adopts the target map model; ENTRY 60%, ADD up to 100%; Allocator is the ultimate authority on quantity; Style is only used for diagnosis; Cash, concentration shock、 Depth jointly restricts orders. More importantly, Kimi identified specific issues that the other two models did not explicitly address: shared_deployable_pool() Read account_stnap ["capital"] ["deployable_cash"] But this field may not have been actually written in → Go back to free_cash The funding caliber of the strategy layer and allocator layer may not be consistent It also discovered: Contract writing debounce takes 60 seconds, code/configuration takes 30 seconds; The annotation cycle is 16 minutes, and the actual loop is 600 seconds. These are typical advantages of static auditing, field tracking, and contract consistency checks. weakness Kimi's reasoning in capital efficiency and transaction semantics is weaker than its code checking ability. A typical error is: ADD prices are higher, so the marginal edge/day is inevitably worse. This ignores the fact that the remaining holding time is also shortened. Higher ask does not necessarily mean lower edge/day. It also believes that: 60/40 will leave the remaining funds idle for a long time; Increasing the entry share will improve turnover; CONFIRMATION-NO should be tightened; Increasing single market soft cap will improve portfolio turnover. These conclusions lack support from real candidate competition, actual block attribution, and counterfactual allocation data. The most suitable role STATIC_CODE_AUDITOR SCHEMA_AND_FIELD_FLOW_CHECKER CONTRACT_IMPLEMENTATION_DIFF LOCALIZED_BUG_DISCOVERY Kimi is very suitable for answering: Is there any field not written in the code, default value rollback, inconsistency between documentation and implementation, or whether a certain gate is actually effective? ” But it is not suitable to answer separately: How should trading strategies and capital allocation be changed 3. Grok: The most complete code review, but the easiest to over design Grok provides the most detailed organization of the entire ADD path: Admission criteria for each level; risk latch； REDUCE reentry cooldown； Price bands; fingerprint； emergency cap； market target； ENTRY/ADD gap； Cash, concentration, shock, and depth constraints of allocator; Rating and continuity of ADD and ENTRY; The impact of SELL/REDEEM on cash recovery. It provides a very clear summary of the current code execution model: Can it be decided by the head room; Discretize the amount from target gap to lot; ADD style only explains labels. Therefore, Grok performs well in quickly understanding an unfamiliar complex system. weakness The biggest problem with Grok is: Quickly jump from 'discovering a possible mechanism side effect' to 'suggesting modifying strategies'. It proposed a large number of unverified changes: TIME_TOPUP cooling; ADD 1.5 times edge/day threshold; Ask ≥ 0.97 is limited to 1 lot; Reduce peak target; Increase entry share; Only partially fill the gap at a time; Weakening continuity; Reduce the weight of TTE confirmation. These suggestions may seem reasonable on the surface, but there are three issues: Without first proving that these mechanisms actually caused losses; Is it better to not quantify the ENTRY squeezed out by ADD; Possible reintroduction of previously fixed low ADD recall and leader fidelity bias. Grok is good at generating complete optimization spaces, but it is easy to: POSSIBLE SIDE EFFECT Upgrade to: CONFIRMED ROOT CAUSE Further upgrade to: SHOULD CHANGE PRODUCTION LOGIC This is the most dangerous tendency in the audit of production trading systems. The most suitable role SYSTEM_MAPPING CODE_AND_CONFIG_EXPLANATION HYPOTHESIS_GENERATION DESIGN_OPTION_ENUMERATION Not suitable as the only: PRODUCTION_CHANGE_APPROVER ROOT_CAUSE_FINAL_AUTHORITY STRATEGY_SEMANTICS_GATEKEEPER Typical thinking patterns of three models Grok discovery mechanism → Inferring possible side effects → Generate multiple optimizations → Suggestion for modification Advantages: Wide coverage and diverse ideas. Risk: Over design, assuming rapid upgrades. Kimi Tracking codes and fields → Find inconsistent implementation → Find local defects → Attempt to derive strategy improvements from defects Advantages: Strong code problem localization. Risk: Partial correctness does not necessarily mean that the system conclusion is correct. Fable5 Understanding code → Read operational data → Find the actual binding constraint → Distinguish between primary and secondary causes → Sort by actual earnings Advantages: closest to production and operation thinking. Risk: Excessive assertions may still be made regarding the meaning and amount of individual indicators.