GPT-4.1 nano is a sensible first model to evaluate for routine classification: OpenAI explicitly positions it for classification and lists low token rates. But there is no established universal winner for classification or extraction. Test it against an alternative such as Gemini 3.1 Flash-Lite using your own representative data, then choose by the cost and reliability of accepted results—not token price alone.
Which low-cost model should you try first?
GPT-4.1 nano for classification-focused workflows
OpenAI calls GPT-4.1 nano “ideal for tasks like classification or autocompletion.” That is the provider’s product positioning, not an independent finding that it will outperform alternatives on your data. Its listed rates make it a practical candidate when requests are simple, repetitive, and high-volume. OpenAI’s GPT-4.1 launch announcement lists $0.10 per million input tokens and $0.40 per million output tokens.
Include an alternative in your evaluation
Gemini 3.1 Flash-Lite is a useful price comparison, while GPT-4.1 mini is another option when you want to test a model OpenAI describes as strong in instruction following and tool calling. None of those descriptions or published rates establishes which model will deliver the best classification or extraction accuracy for your application.
How do the listed token prices compare?
The table shows provider-listed rates checked October 7, 2026. They are not a complete estimate of an application’s bill: endpoint, region, service mode, caching, and actual token use can change costs. Confirm the rate for the exact endpoint and mode you plan to use.
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#1 Best Overall
| Model | Input per 1 million tokens | Cached input per 1 million tokens | Output per 1 million tokens | Source |
|---|---|---|---|---|
| GPT-4.1 nano | $0.10 | $0.025 | $0.40 | OpenAI launch announcement |
| GPT-4.1 mini | $0.40 | $0.10 | $1.60 | OpenAI model documentation |
| Gemini 3.1 Flash-Lite | $0.25 | Not stated in the cited model card | $1.50 | Google DeepMind model card |
| Gemini 3.5 Flash-Lite | $0.30 | Not stated in the cited model card | $2.50 | Google DeepMind model card |
Google Cloud’s pricing table distinguishes regions and service modes, including lower Flex or Batch rates for eligible models. Do not assume a model-card rate applies to every Google Cloud endpoint or mode.
How should you compare models for your API task?
Build a fixed evaluation set from the kinds of inputs your application actually receives. Use the same prompt, examples, output schema, and decoding settings where the providers allow them. Include both ordinary and difficult cases so a model is not selected based only on easy examples.
Rank #2
- This refurbished product is tested and certified to work properly. The product will have minor blemishes and/or light scratches. The refurbishing process includes functionality testing, basic cleaning, inspection, and repackaging. The product ships with all relevant accessories, and may arrive in a generic box.
- Choose a task-specific quality measure. For classification, measure exact-label accuracy or another metric that reflects the cost of different mistakes. For extraction, check field-level correctness, including whether required values are omitted or invented.
- Test difficult inputs. Include ambiguous labels, missing fields, long inputs, and malformed source text. Check whether the model follows the schema and how often responses need downstream repair.
- Measure operating performance. Record input and output tokens, cached input where applicable, retries, and total cost per accepted record. Measure median and tail latency at expected concurrency, not just a single response.
- Check practical constraints. Confirm input and output limits, provider availability, endpoint location, and whether the provider’s data-handling terms meet your requirements.
- Record the exact setup. Keep model identifiers, prompts, settings, endpoint, and prices with the evaluation results. Provider catalogs and rates change, so a result is useful only if you can identify what you tested.
Compare cost per accepted result rather than price per token. A cheaper call may cost more in practice if it produces incorrect or invalid outputs that require retries, repair, or human review.
When is a larger model worth testing?
Test GPT-4.1 mini if your evaluation suggests the simpler candidate struggles with instructions, tool calling, or the task’s context demands. OpenAI lists a context window of 1,047,576 tokens and a maximum output of 32,768 tokens for GPT-4.1 mini. Those capacity figures do not by themselves demonstrate better classification or extraction accuracy; measure whether the model improves your results enough to justify its higher listed rates.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesFor uncertain or high-impact cases, consider routing only those cases to a stronger model if your measurements show a benefit. Keep the routing rule and added operational complexity in the cost calculation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the available evidence does—and does not—show
The cited provider materials establish product descriptions and published pricing, not a directly comparable, independent classification or extraction result across these candidates. Google’s model card includes benchmark comparisons, but unrelated benchmark scores do not establish superiority on your specific routine API task. Model choice therefore depends on your data, error costs, output requirements, and the endpoint you will use.
Rank #4
Prices and model availability are volatile. The rates above were checked October 7, 2026; verify the linked provider documentation before making a cost commitment.
Quick Recap
Best Value
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.
