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# System prepended metadata

title: 'Choosing the Best AI Model for RFQ Automation: A Practical Comparison of ChatGPT, Gemini, Claude, and Grok'

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# Choosing the Best AI Model for RFQ Automation: A Practical Comparison of ChatGPT, Gemini, Claude, and Grok

## Introduction: Why RFQ Automation Is Still Hard

Matching line items from supplier RFQs to internal product catalogs looks straightforward on paper. In reality, procurement teams wrestle with inconsistent terminology, incomplete descriptions, missing part numbers, and supplier-specific naming conventions. The result is slow processing, frequent rework, and avoidable quoting errors that ripple through sales and operations.

Large language models (LLMs) promise to ease this burden. They can interpret messy natural language and infer which internal catalog items best match vague or poorly formatted RFQ requests. But procurement leaders face a practical question: among today’s leading AI models, which ones actually perform well for this specific task, and which ones only shine in demos?

To answer that, we ran a controlled **[AI benchmark](https://www.businesswaretech.com/intelligent-document-processing-benchmark)** comparing five leading LLMs on real RFQ item-matching tasks. The goal was not to test abstract language ability, but to measure how these systems behave under realistic procurement conditions.

This article breaks down the results, explains what the metrics mean in day-to-day workflows, and offers concrete recommendations for choosing the right model based on accuracy goals, review effort, and cost.

## How the Benchmark Was Designed

### Models Evaluated

We tested five production-grade language models from different vendors:





* GPT-5
* GPT-5 Nano
* Gemini 2.5 Pro
* Claude Sonnet 4
* Grok 4

Together, these represent a mix of high-capability frontier models and lower-cost variants commonly considered for large-scale automation.

## The RFQ Matching Task

The evaluation used six real RFQ documents containing a total of 85 individual requested items. For each RFQ line item, the model was asked to select the correct match from a pre-filtered list of candidate catalog items.

Each model received up to 500 candidate products per RFQ, produced by a separate retrieval step. This setup reflects how AI is typically deployed in procurement systems: the retrieval layer narrows down the search space, and the LLM performs the final reasoning and ranking.

The core question was simple: does the correct catalog item appear in the model’s top recommendations?

## Scoring Metrics

Performance was measured using three complementary ranking metrics:





Hit Rate@5 – How often the correct item appears anywhere in the top five suggestions.



MRR (Mean Reciprocal Rank) – How high the correct item appears in the ranking, with higher scores favoring top-ranked hits.



nDCG@5 – A position-weighted score that rewards placing the correct item near the top of the list.

Together, these metrics reflect both recall (did the model include the right answer?) and ranking quality (did it place the right answer at the top?).

## Results: How Each Model Performed

The benchmark revealed clear behavioral differences between models:

![Screenshot 2026-02-09 162557](https://hackmd.io/_uploads/rJfITVwv-e.png)







## Key Takeaways





**Best recall:** GPT-5 Nano achieved the highest Hit Rate@5, meaning it most often included the correct product somewhere in its shortlist. Claude Sonnet 4 also performed strongly on recall.



**Best ranking precision:** Gemini 2.5 Pro scored highest on MRR and nDCG@5, indicating it was more likely to rank the correct item near the top.



**Balanced performance:** GPT-5 offered a strong compromise between recall and ranking quality.



**Cost-effective options:** GPT-5 Nano and Grok 4 delivered competitive results at significantly lower cost than larger models.

These differences matter because procurement workflows vary. Some teams want to ensure the right item appears in the shortlist for a human to verify. Others want to trust the model’s top recommendation with minimal review.

## What RFQ Processing with LLMs Really Involves

### 1. Different Models Fit Different Workflow Designs

There is no single “best” model in isolation. The right choice depends on whether your workflow is designed around human verification or near-automated pass-through decisions.





**Recall-first workflows:** If operators review shortlists, models like GPT-5 Nano and Claude Sonnet 4 are valuable because they surface the correct match more frequently.



**Precision-first workflows:** If the top recommendation is pushed directly into quoting or ERP systems, Gemini 2.5 Pro and GPT-5 reduce downstream corrections by ranking the correct item higher.

This trade-off, breadth versus precision, should guide model selection more than raw benchmark scores.

### 2. Inference Costs Vary by Orders of Magnitude

Average cost per RFQ highlights how quickly AI expenses can scale:


![fghgfhgfhgfhgfhghfgh](https://hackmd.io/_uploads/SJGC64vPZx.png)




GPT-5 Nano combines strong recall with very low cost, making it attractive for high-volume environments. Grok 4 is the cheapest option, suitable for exploratory or non-critical matching. Claude Sonnet 4, while accurate in recall, carries the highest cost and is best reserved for use cases where coverage is more important than budget.

### 3. Retrieval Is the Silent Performance Killer

Across all models, around 21% of ground-truth items never appeared in the candidate lists provided to the models. This limitation stems from the retrieval layer, not the LLMs themselves.

In practice, this means no model can recover items that were never retrieved in the first place. Improving search and filtering pipelines often delivers larger gains than switching between top-tier models. Put differently: AI reasoning cannot compensate for poor upstream retrieval.

## What This Means for Procurement Workflows

### Faster Reviews with High Recall

High Hit Rate@5 translates directly into time savings. When the correct product appears in the shortlist more often, procurement specialists spend less time manually searching catalogs and reconciling ambiguous RFQ descriptions. This speeds up quote generation and reduces decision fatigue.

### Fewer Corrections with Better Ranking

Higher MRR and nDCG scores mean fewer incorrect top picks. For semi-automated workflows, this reduces the need for overrides and lowers the risk of wrong items being passed downstream into pricing and fulfillment systems.

### Scalable Economics

Cost differences become decisive at scale. For example, processing 10,000 RFQs per day with GPT-5 Nano costs roughly $849, while doing the same with Claude Sonnet 4 would exceed $16,000 daily. The performance gap does not justify that 19x cost difference in many operational settings.

## Recommendations by Scenario





**Human-in-the-loop review:** GPT-5 Nano is the most cost-efficient option with excellent recall. Claude Sonnet 4 is viable when broader language coverage justifies the higher cost.



**Automated matching with minimal review:** Gemini 2.5 Pro or GPT-5 are better suited due to stronger ranking accuracy.



**Cost-sensitive deployments:** Start with Grok 4 or GPT-5 Nano for large volumes and non-critical flows.



**Balanced accuracy and spend:** GPT-5 offers a reasonable middle ground between ranking quality and operational cost.

## Conclusion: Model Choice Is Only Half the Story

Automating RFQ item matching is one of the highest-impact opportunities in procurement. The benchmark shows that modern LLMs can significantly reduce manual effort, but their strengths differ: some excel at ensuring the right answer is “in the room,” others at confidently picking the right answer first.

Just as important as model choice is the quality of your retrieval pipeline. Since a fifth of correct items were never presented to the models at all, improving search and candidate filtering can unlock immediate gains without changing the LLM.

In practice, the biggest performance improvements rarely come from choosing the most powerful model on paper. They come from aligning model behavior with workflow design and pairing the right LLM with a robust retrieval and validation layer.