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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →To reduce LLM costs for a product catalog, test small models on a representative sample of your own records and compare cost per accepted record—not token prices alone. Score summarization, classification and JSON extraction separately, then include retries, validation and any human correction in the cost.
Why a catalog-specific comparison matters
A model that is inexpensive per token may still cost more in production if it produces incorrect labels, unusable summaries or invalid data that must be retried or fixed. Conversely, a more capable candidate may be economical if it gets more records accepted on the first pass. Provider descriptions and general benchmarks can help identify candidates, but they do not establish which model will perform best on your catalog.
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Keep the three common catalog tasks distinct. A fluent summary is not proof of accurate classification, and valid JSON is not proof that its values are correct.
- Summarization: Does the summary preserve useful, supported product facts without inventing details?
- Classification: Does the record receive the right category or label, especially when the product is ambiguous or information is sparse?
- Extraction: Are the requested attributes present, correct and represented in the required format?
How to compare small models on your catalog
- Choose a representative evaluation set. Include ordinary records, missing attributes, sparse or malformed input, edge cases and ambiguous classifications. Keep the set separate from the records you use to tune prompts where practical.
- Define what counts as accepted. Write expected labels and field values, summary requirements and the JSON schema before running candidates. Specify how to treat missing values, nulls, unsupported claims and partial answers.
- Hold the test conditions steady. Give each candidate the same input fields, task instructions, output-token budget and acceptance rules. Record the exact model identifier or snapshot and any relevant API settings.
- Measure each task independently. Report summary usefulness and factuality, classification accuracy by label, and extraction precision, recall or exact match at field level—whichever criteria fit the task. Do not combine unlike tasks into a single quality score.
- Measure validity separately from correctness. Record whether the response parses and conforms to required keys, types and allowed values, then assess whether the values themselves are right.
- Calculate actual cost per accepted record. Use the token usage from the run, add retries and applicable tool charges, and include review or correction costs when they are part of your workflow. Report sample size, test date, candidate identifiers, price tier and results by task.
Compare token prices without mistaking them for per-record costs
The following are provider-published USD rates per million tokens. OpenAI’s GPT-4o Mini model page and Google’s Gemini API pricing page were accessed October 7, 2026. Google figures below are for the standard paid tier; region, account eligibility and pricing terms can affect what applies to a deployment.
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| Candidate | Input tokens | Cached input tokens | Output tokens | Price basis |
|---|---|---|---|---|
| GPT-4o Mini | $0.15 per million | $0.075 per million | $0.60 per million | OpenAI model-page rates; the page also presents separate Batch API pricing. OpenAI model documentation |
| Gemini 3.5 Flash-Lite | $0.30 per million | Not stated in the cited standard-rate figures | $2.50 per million | Google standard paid tier. Google Gemini API pricing |
| Gemini 3.1 Flash-Lite | $0.25 per million | Not stated in the cited standard-rate figures | $1.50 per million | Google standard paid tier. Google Gemini API pricing |
These rates are not quotes for processing a record. For a basic estimate, calculate (input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate) for each request, using the applicable cached-input rate when it applies. Sum requests, retries and applicable tool charges, then divide by the number of records that meet your acceptance rules. For a fuller operating cost, add orchestration, storage, evaluation and human review where applicable. OpenAI explains token billing and built-in tool charges on its API pricing page.
Batch rates are distinct from standard rates. Google’s pricing page also describes rates scheduled to change on January 1, 2027; check that page for the applicable figures and effective terms rather than treating a future schedule as the current rate. Recheck both providers’ pricing and model availability before committing to a production estimate.
Which small models are reasonable candidates?
GPT-4o Mini
OpenAI presents GPT-4o Mini as a fast, affordable small model for focused tasks, with examples including intent classification and tag generation. Its documentation lists structured-output support, making it a reasonable candidate to test for catalog work—not a proven winner for a particular catalog. The model page also lists a snapshot and tier-dependent rate limits, so verify the identifier and limits for the account and implementation you plan to use. OpenAI’s GPT-4o Mini documentation
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Gemini Flash-Lite models
Google identifies high-throughput document processing as an intended use for Gemini 3.5 Flash-Lite. Its model card reports provider-generated results across coding, agentic, knowledge-work, chart and long-context benchmarks as of July 2026. Those benchmark categories are not a direct comparison of product summaries, catalog labels or extracted fields, so they cannot substitute for your evaluation set. Google DeepMind’s Gemini 3.5 Flash-Lite model card
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Make JSON validity and extracted data quality separate checks
Structured-output support can help constrain response format, but it does not guarantee that a model has selected the right product attribute or value. For extraction, validate both the response structure and the meaning of each field.
- Schema checks: required keys, data types, allowed values and whether missing information should be represented as null or omitted.
- Field checks: whether each value is supported by the source record and matches the expected attribute.
- Failure checks: malformed responses, missing keys, unsupported claims and values that pass parsing but fail your correctness rules.
Track the schema pass rate and field-level correctness as different outcomes. This makes it possible to see whether a candidate needs better formatting controls, a clearer prompt or a different model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use routing to limit expensive retries and reviews
A practical design is to send routine records to the least expensive candidate that clears pre-set quality and validity thresholds. Validate each response mechanically, then route failures or low-confidence cases to a retry, a stronger candidate or a human reviewer. Define the escalation rules before rollout, and measure their effects on acceptance rate, latency and cost.
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This routing pattern is a design recommendation, not a guaranteed saving. Its results depend on your records, thresholds, model behavior and implementation; measure it on your workload before relying on projected savings.
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Account for reliability and deployment fit
Token price is only one part of the decision. Measure latency, rate limits, timeout and retry frequency, and whether your intended workload qualifies for batch processing. Also check input modality, context needs, privacy and data-handling requirements, deployment constraints, and model snapshot stability.
Google’s Gemini 3.5 Flash-Lite model card warns that the model may hallucinate and may occasionally slow down or time out. Those limitations do not quantify catalog-specific error rates, but they make output checks and failure handling important parts of a production plan. Model-card limitations and intended uses
Choose from measured results, not a universal “best”
The cited provider materials establish candidate capabilities, published token rates and broad benchmark information; they do not provide an apples-to-apples catalog benchmark across summarization, classification and JSON extraction. No single candidate can therefore be called the best for every catalog on that evidence alone. Select the model that meets your task-specific acceptance thresholds at an acceptable measured cost and reliability level, and revisit the comparison when prices, model versions or catalog requirements change.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
