Model Selector for Finance

How to use it

1. Pick the task type, latency budget, monthly cost budget, context-size need and quality sensitivity from the five dropdowns.
2. Read the recommended model and its estimated monthly spend at the reference workload (6,000 input and 1,200 output tokens per call, 3,000 calls a month).
3. Check the top three cards and the full ranked list. A model that fails a hard gate (cost, latency or context) ranks below every model that passes.
4. Use the per-axis table to compare published input and output prices per 1M tokens, context windows and which gates each model passes.
5. Treat the ranking as a shortlist: it uses published prices and vendor positioning only, so confirm quality on your own eval set before switching.

The method, step by step

Select an LLM for a finance task by defining the task type, the latency budget, the cost budget, the required context window, and how sensitive the task is to quality, then ranking candidate models against those constraints and confirming the choice on your own evaluation set. Public benchmarks are a starting filter, not a decision; the model that wins on your task and data is the one to ship.

1. Define the task precisely

   Name the task in concrete terms: extracting fields from a filing, summarizing an earnings call, classifying a headline's sentiment, or reasoning over a multi-step research question. Different tasks reward different model strengths, and a model excellent at long-context reasoning may be overkill and overpriced for a classification step. The task definition is the first filter, because it rules out whole categories of model before any benchmark matters.

   Split a complex pipeline into distinct tasks and select per task. One model rarely is the right choice for every step of a finance loop.

2. Set the latency and cost budgets

   Decide how fast a response must come back and how much a call may cost. A live signal or user-facing step has a tight latency budget and rules out slow models; an overnight batch job tolerates latency and should optimize for cost. The cost budget per call, scaled by volume, often matters more than raw capability, since a small quality gain rarely justifies a large cost difference at high volume. These two budgets narrow the field sharply.

   Latency-tolerant work can use batch processing and cheaper models. Reserve the fast, expensive options for the steps where a person or a trade is actually waiting.

3. Match the context window to the input

   Confirm the model's context window comfortably holds your largest input plus the prompt and any retrieved passages. A 10-K is a large input, and a model whose context cannot hold it forces either chunking or truncation, each with its own cost. Conversely, paying for a huge context window you never fill is waste. Size the context to the task's real input distribution, including the long tail of large filings, not the average case.

   Size the context to your largest realistic input, not the median. The occasional oversized filing is exactly where a too-small context window fails silently.

4. Weigh quality sensitivity against cost

   Decide how much a quality error costs in this specific task. A figure that feeds an automated trade demands the strongest model and heavy verification; a draft summary a human reviews tolerates a cheaper model. The right choice maximizes quality where errors are expensive and saves cost where they are cheap. This is a per-task judgment, not a global one, which is why the same pipeline may use a flagship model for one step and a budget model for another.

   Spend capability where an error is costly and save it where an error is cheap. Uniformly using the best model everywhere overpays for the steps that do not need it.

5. Confirm on your own evaluation set

   Public benchmarks filter candidates but do not decide, because they rarely match your task, your data, or your prompt. Run the shortlisted models on your own representative evaluation set with known answers, and rank them on accuracy, faithfulness, cost, and latency for your actual inputs. The model that wins on your data is the one to ship. Turn this evaluation into a regression suite so future model updates are tested the same way.

   Benchmark leaderboards measure a different task than yours. The only ranking that matters is the one on your own inputs and your own prompt.

   Prompt Regression Tester

Sources: Holistic Evaluation of Language Models (HELM) (Liang et al., Stanford CRFM (2022)) ; Models Overview (Anthropic)

Mistakes that undo the result

Questions people ask

Related tools

• Calculators Token-Cost Optimizer

  Compute the dollar cost of a trading research loop across Claude, GPT and Gemini: prompt length × model × retries × call volume → cost per idea and per validated trade.

• Calculators Financial Document Token Estimator

  Paste a 10-K, 10-Q, 8-K or earnings transcript and see token count + one-pass extraction cost across ten frontier LLMs, with cache-hit toggle.

• Calculators Batch vs Real-Time Cost Calculator

  Jobs per day, tokens per job, model, deadline — get real-time vs batch cost side-by-side with savings estimate and batch-eligibility flag. Based on vendor-published batch pricing.

Articles

• 10 min read Financial QA LLM Benchmarks 2026: FinanceBench & Fin-RATE

  Financial QA LLM benchmarks 2026: FinQA, FinanceBench, DocFinQA, and Fin-RATE scores, plus whole-filing read costs on the current model lineup.

• 9 min read Best LLM for Financial Analysis & Stock Analysis 2026

  Best LLM for financial analysis 2026 is task-tiered: a budget model for extraction, a 1M-context model for synthesis, a frontier model for hard reasoning.

• 7 min read Claude vs GPT vs Gemini for Financial Analysis 2026

  Claude vs GPT vs Gemini for financial analysis: current API list prices, cached-input rates, context windows and the cost of processing one 10-K, by tier.

Use it from code

The same calculation as a JavaScript module you can import. It runs where you import it, with no request, key or rate limit.

import { compute } from "https://aifinhub.io/engines/model-selector-finance.js";

Input and output contract and the guide for agents.