The Problem That Single-Model Thinking Creates
The most common mistake in AI-powered investment research is treating model selection as a one-time decision. Pick the “best” model, pipe everything through it, done. This approach fails for the same reason that a single analyst cannot be the best at macro analysis, credit assessment, quantitative modeling, and trade execution simultaneously. Different tasks have different cognitive profiles, and the right tool varies accordingly.
At KSINQ, we run a multi-model architecture where the routing decision — which model handles which task — is itself an investment in analytical quality. This article explains that routing logic.
The KSINQ Model Roster
Claude (Anthropic) — Core reasoning and analysis. As detailed in the previous article, Claude handles the tasks that require structured analytical reasoning across long, bilingual documents: thesis construction, evidence chain building, adversarial stress-testing, and cross-language synthesis. These are the highest-stakes cognitive tasks in our workflow, and we allocate our most capable reasoning model to them.
OpenAI o-series (o3 / o4-mini) — Mathematical and quantitative reasoning. When we need to model option payoff structures, run scenario analysis with explicit probability distributions, or verify quantitative claims in research reports, we route to OpenAI’s reasoning-optimized models. The o-series excels at multi-step mathematical reasoning where each step must be logically validated — a requirement that differs meaningfully from the natural language reasoning at which Claude excels. A concrete example: when our risk manager needs to evaluate the asymmetry ratio of a proposed position — “if the thesis is right, the payoff is 4x; if it’s wrong, the loss is capped at 1x” — the mathematical verification of that payoff structure goes through the o-series.
GPT-4.1 — Multimodal parsing. Financial research involves visual data that text-only models cannot process: earnings presentation slides with embedded charts, scanned regulatory filings, shipping manifest images from trade channels, and satellite imagery of commodity storage facilities. GPT-4o handles these multimodal inputs, extracting structured data from visual sources that then feeds into Claude’s reasoning pipeline. This is not a reasoning task — it is a parsing task — and the model that is best at vision-to-text extraction is not necessarily the same model that is best at reasoning over that text.
Google Gemini — Triage and preprocessing. Not every task requires a frontier model. Initial news filtering, basic summarization of low-priority sources, translation of routine documents, and metadata extraction are all tasks where a capable but less expensive model performs adequately. We route these through Google Gemini, which offers strong performance at lower cost with generous context windows. This is an engineering maturity decision: concentrating our budget on the analytical tasks where model quality directly impacts investment outcomes, while using efficient alternatives for preprocessing.
The Routing Logic
The model selection is not random and not manual. We have defined task categories with explicit routing rules.
- Structured analytical reasoning (thesis building, evidence chains, adversarial review) → Claude
- Quantitative verification and mathematical modeling → OpenAI o-series
- Visual data extraction → GPT-4.1
- Preprocessing, triage, and routine extraction → Google Gemini
The routing happens at the workflow level, not the conversation level. A single research process may invoke three or four models in sequence: Gemini for initial news triage, GPT-4.1 for parsing visual data from a shipping report, Claude for building the analytical thesis, and o-series for verifying the quantitative risk assessment. The output of each stage feeds into the next. The human researcher reviews the final synthesis, not the intermediate routing.
Why This Matters for Investment Quality
The multi-model approach is not a technical luxury. It is a direct investment in analytical quality. When you force a reasoning-optimized model to do quantitative computation, you get slower and less reliable results. When you force a vision model to do long-form analytical reasoning, you get shallow analysis. When you use a frontier model for routine preprocessing, you burn budget that could be allocated to the tasks where model quality makes a material difference.
The analogy from our own Triple-Perspective Framework is precise: just as we do not ask the fundamental-analysis lens to do the risk-assessment lens’s job, we do not ask the reasoning model to do the quantitative model’s job. Specialization at the model level mirrors specialization at the analytical level. Both improve outcomes.