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GPT-6.1 Terra leads cost-effective LLMs for banking intent classification

By PulseAugur Editorial · [1 sources] ·

A recent analysis compared ten LLMs on the Banking77 intent classification task, finding GPT-6.1 Terra to be the most cost-effective model that meets a 70% success threshold. While GPT-6.1 Terra achieved an 84% pass rate at $0.000727 per task, it was slightly outperformed by GPT-6.1 Sol at 86%. GPT-6 Luna and Claude Haiku 5.5 were significantly cheaper but did not meet the performance baseline set by GPT-6.1 Sol. The study emphasized that simply looking at token price can be misleading due to factors like retry costs and the accuracy of label classification. AI

IMPACT Provides cost-performance benchmarks for LLM selection in classification tasks.

RANK_REASON Analysis of LLM performance and cost on a specific benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GPT-6.1 Terra leads cost-effective LLMs for banking intent classification

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30 / 100
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Tool
Analysis of LLM performance and cost on a specific benchmark. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, infra
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High
Clearly on-topic for AI-industry coverage.
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Breaking (< 6h)
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Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Grain64 ·

    Cheapest LLM for 77-intent banking classification: GPT-6.1 Terra at $0.000727 a success

    <p>On 150 BANKING77 messages, GPT-6.1 Terra is the cheapest of ten models that clears a 70% pass bar and can be shown to match the baseline, GPT-6.1 Sol. It passes 84.0% of the tasks against 86.0% for Sol, at $0.000727 per successful task against $0.001272: 42.8% less.</p> <p>Two…

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