Model selection is an architecture decision
Stop Asking “Which LLM Is Best?” – Ask Which Model Fits This Workload Last quarter I was elbow‑deep in a fintech product that had to turn noisy bank statements into clean expense reports. My first instinct was to grab the “biggest” LLM, fire‑off GPT‑4, Claude‑2, Gemini‑1.5 and hope it would magically understand every column, currency and weird abbreviation. After a weekend of latency checks, cost…
Last quarter, a fintech project required transforming messy bank statements into tidy expense reports. The initial approach was to use the most powerful LLMs available, but this proved inefficient. Latency checks, cost comparisons, and error logs revealed that even the top-performing models on leaderboards often failed to meet the critical KPI of cost per successful parsing.
To address this, the author shifted focus from LLMs as toys to seeing them as replaceable services. Key factors were identified: capability (financial jargon and tables), reasoning ability (multi-step extraction without hallucinations), latency (ideal UI response time of 300 ms), context length (statements often exceeding 10,000 tokens), modality (PDF images needed OCR), cost per call, and version tracking of the deployed model.
A small benchmark was created mirroring the actual workflow: feed a real statement, request JSON line items, then measure success (JSON parsing accuracy and total match). The benchmark was run against three services: OpenAI's gpt‑3.5‑turbo‑16k, AWS Bedrock's Claude‑3 Opus, and Google's Gemini‑1.5‑Flash. Results showed:
- Accuracy: gpt‑3.5‑turbo (92%), Claude‑3 Opus (95%), Gemini‑Flash (88%)
- Cost per 1,000 tokens: gpt‑3.5‑turbo ($0.012), Claude‑3 Opus ($0.018), Gemini‑Flash ($0.006)
- Latency: gpt‑3.5‑turbo (420 ms), Claude‑3 Opus (280 ms), Gemini‑Flash (150 ms)
Calculating cost per successful task (cost per call divided by success rate) showed that Gemini‑Flash, despite lower raw accuracy, became the most cost-effective choice at $0.007 per task. Version-tracking the specific model (e.g., gpt-3.5-turbo-0613) is crucial, as subsequent updates may alter performance metrics.
In another project, the author switched from Claude‑2 to Claude‑3 Sonnet, achieving a 30% latency reduction and a 5% reasoning improvement, reducing cost per task from $0.014 to $0.009. The takeaway is to treat LLM selection as an engineering decision backed by data—using a checklist and a cost-per-successful-task chart rather than relying on bragging rights from leaderboard rankings.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
