Is the cheapest model always the best choice for data extraction?

No. GPT-6 Luna at $45/mo is cheapest for this workload, but cost alone doesn't tell you about context window limits, schema complexity handling, or output reliability. For straightforward field extraction from clean documents, budget models are usually sufficient — but check context window (GPT-4o mini is capped at 128K while most budget peers offer 1M) before assuming the cheapest option fits your documents.

Does output price matter for extraction workloads?

Less than for chat applications, since outputs are typically short JSON objects, but it's not irrelevant. Models like Gemini 2.5 Flash ($0.3 in/$2.5 out) or Claude Haiku 4.5 ($1 in/$5 out) have low input prices but much higher output prices, which pushes their total cost for this 300M-in/30M-out workload to $165/mo and $450/mo respectively — well above input-cheap, output-cheap options like GPT-6 Luna at $45/mo.

When does it make sense to pay more than budget tier for extraction?

If your documents are messy, require complex multi-field reasoning, or the schema is ambiguous enough that budget models produce unreliable JSON, stepping up to mid-tier (e.g. Gemini 3 Flash at $240/mo or DeepSeek V4 Pro at $257/mo) may be worth it. Flagship-tier pricing ($780-$4,500/mo), like Claude Opus or GPT-6 Astra, is generally built for harder reasoning tasks rather than straightforward structured extraction.

USE CASE · VERIFIED 2 OCT 2026

What's the cheapest AI API for data extraction and structured output?

For a workload of 300M input tokens and 30M output tokens a month, GPT-6 Luna is cheapest at $45/mo, followed by Qwen3.8 Flash at $59/mo and GLM 5.3 Flash at $60/mo. GPT-4o mini and DeepSeek V4.1 Flash tie close behind at $63/mo, so for pure extraction work the budget tier is where almost all the relevant competition sits.

Worked example: 300M input, 30M output tokens/month

Pulling fields out of documents into JSON: long inputs, short structured outputs. The six cheapest of the 30 models we track, plus two flagships for scale. Open-weight models are priced at the original provider's own list price.

GPT-6 Luna$45/mo

Qwen3.8 Flash$59/mo

GLM 5.3 Flash$60/mo

GPT-4o mini$63/mo

DeepSeek V4.1 Flash$63/mo

DeepSeek V3$93/mo

Grok 4.7$780/mo

Gemini 3.1 Pro$960/mo

Why extraction workloads favor input-heavy pricing

Pulling structured fields out of documents means you're paying mostly for input tokens — the document text, tables, scanned text, or OCR output — while the output is usually a small JSON object. That makes the input price per 1M tokens the dominant cost driver, not the output price. This is why models with high output prices but low input prices, like Gemini 2.5 Flash ($0.3/$2.5) or Claude Haiku 4.5 ($1/$5), end up costing far more here than their 'budget' label suggests — the output rate matters less for chat but can sting if your JSON responses get verbose or you're extracting many fields per call.

Budget tier is doing all the work here

At this volume, the gap between the cheapest model ($45/mo) and the most expensive flagship ($4,500/mo) is 100x, but almost none of that gap is justified by extraction needs specifically. Budget models like GPT-6 Luna, Qwen3.8 Flash, GLM 5.3 Flash, GPT-4o mini, and DeepSeek V4.1 Flash all sit under $65/mo and all offer large (1M, except GPT-4o mini at 128K) context windows — plenty for most documents. Mid-tier and flagship models (Claude Sonnet, GPT-6.1 Sol, Gemini 3.1 Pro, Claude Opus) cost 10-50x more and are built for reasoning and complex multi-step tasks, not for cheaply reformatting text into JSON. Unless your extraction task is unusually hard — messy handwriting, ambiguous schemas, multi-document cross-referencing — you're very likely overpaying by going above budget tier for this use case.

Context window, batching, and caching change the math

If your documents are long (legal contracts, full reports, multi-page PDFs), check context window before price: GPT-4o mini caps at 128K while most budget competitors offer 1M, so you may need to chunk documents with GPT-4o mini that fit whole in GLM 5.3 Flash or DeepSeek V4.1 Flash. Also look into whether your provider supports prompt caching — if you're repeatedly extracting from the same template or re-sending a long system prompt with schema instructions, caching can cut effective input cost substantially, often around half, which matters even more at high volume. Batch APIs (processing requests asynchronously rather than live) are also worth checking since extraction jobs are rarely latency-sensitive and batching often comes with its own discount.

How we'd actually decide

High-volume, simple field extraction with big documents: GPT-6 Luna or Qwen3.8 Flash — lowest cost per token, large context window, built for exactly this kind of workload.
Need 1M context but want OpenAI ecosystem: GPT-6 Luna — same family as GPT-4o mini but cheaper and far larger context (1M vs 128K).
Documents under 128K tokens and already on OpenAI tooling: GPT-4o mini — fine if context size isn't a constraint, though DeepSeek V4.1 Flash matches its price with 1M context.
Extraction tasks involving ambiguous schemas or messy/unstructured source text: consider stepping up to a mid-tier model like Gemini 3 Flash ($240/mo) or DeepSeek V4 Pro ($257/mo) rather than jumping straight to flagship pricing.

Worked example uses standard (non-batch, non-cached) list pricing verified 2 October 2026. Prices change; use the calculator for today's numbers with your own volume.