Claude 3.5 Haiku vs GPT-4o-mini vs Gemini Flash: Cheap LLM Scrapers

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If you’re routing every scraped page through an LLM to extract structured data, model cost is your biggest variable — and the gap between cheap and expensive models is now wide enough to change your unit economics entirely. Claude 3.5 Haiku, GPT-4o-mini, and Gemini 1.5 Flash are the three models engineers reach for when they want extraction quality close to frontier performance at a fraction of the price. This piece breaks down how they compare across cost, latency, context window, and real scraping workloads in 2026.

What “cheap LLM scraping” actually means

Cheap scraping with LLMs is not about cutting corners on quality. it’s about matching model capability to task complexity. Extracting product names and prices from a structured e-commerce page does not need GPT-4o. it needs a fast, low-cost model with reliable JSON mode and enough context to handle a full HTML page.

The three models in this comparison sit in roughly the same price tier but behave differently under scraping load:

Model	Input (per 1M tokens)	Output (per 1M tokens)	Context window	JSON mode
Claude 3.5 Haiku	$0.80	$4.00	200K	Yes (tool use)
GPT-4o-mini	$0.15	$0.60	128K	Yes (native)
Gemini 1.5 Flash	$0.075	$0.30	1M	Yes (native)

At pure token cost, Gemini Flash wins by a wide margin. GPT-4o-mini is the familiar default for most teams already on OpenAI infrastructure. Claude 3.5 Haiku is the most expensive of the three but brings the largest practical context window outside of Gemini’s 1M-token outlier.

Extraction accuracy on messy HTML

Raw token price means nothing if the model hallucinates field values or breaks JSON output on malformed HTML. In practice, across a mixed dataset of e-commerce product pages, news articles, and SaaS pricing tables:

GPT-4o-mini handles clean, well-structured HTML reliably. it starts making extraction errors on deeply nested tables and dynamically injected content where the DOM is partial.
Claude 3.5 Haiku is the most consistent on instructions. if you write a detailed system prompt with field definitions and output schema, Haiku follows it more precisely than the other two, especially on edge cases.
Gemini 1.5 Flash benefits enormously from its 1M context window. you can dump an entire crawled page, raw JS bundles included, and the model still extracts cleanly. but it has a slightly higher rate of hallucinated field values when the source content is ambiguous.

For scraping pipelines where you control the prompt carefully and pages are reasonably structured, GPT-4o-mini gets you 90% of the way at the lowest cost. for long-document extraction or multi-step agent chains like those built with the Vercel AI SDK and browser automation, Claude 3.5 Haiku’s instruction-following holds up better across pipeline steps.

Latency and throughput under concurrent load

Latency matters when you’re running scrapers at scale. a 3-second LLM call per page is fine for a 100-page crawl. at 10,000 pages per hour, it becomes the bottleneck.

Typical median latency for a 2,000-token input, 200-token output extraction call:

Gemini 1.5 Flash: 800ms to 1.2s
GPT-4o-mini: 1.0s to 1.5s
Claude 3.5 Haiku: 1.2s to 2.0s

Gemini Flash leads on raw speed. Claude Haiku is the slowest of the three, which partially offsets the accuracy advantage on complex prompts.

Rate limits also differ. OpenAI’s tier-based limits are well-documented and scale predictably with spend. Anthropic’s rate limits on Haiku are more conservative at lower tiers. Google AI Studio and Vertex both offer Gemini Flash, and the Vertex path gives significantly higher throughput quotas for production workloads.

import anthropic

client = anthropic.Anthropic()

def extract_product(html: str) -> dict:
    message = client.messages.create(
        model="claude-haiku-3-5-20241022",
        max_tokens=512,
        system="Extract product name, price, and availability as JSON. Return only valid JSON, no explanation.",
        messages=[{"role": "user", "content": f"<html>{html}</html>"}]
    )
    return message.content[0].text

For GPT-4o-mini, swap in response_format={"type": "json_object"} and you get deterministic JSON output without prompt engineering. Claude requires you to enforce JSON via tool use or careful prompting, which adds a small overhead to setup but gives you more control over schema validation.

Cost modeling for real scraping workloads

Here’s how the three models stack up on a realistic scraping job: 50,000 pages per month, each page averaging 3,000 input tokens and 300 output tokens.

Model	Monthly input cost	Monthly output cost	Total
Gemini 1.5 Flash	$11.25	$4.50	$15.75
GPT-4o-mini	$22.50	$9.00	$31.50
Claude 3.5 Haiku	$120.00	$60.00	$180.00

Gemini Flash is not just cheaper — it’s a different cost category. at $15.75/month for 50K pages, it’s competitive even against non-LLM extraction tools. Claude 3.5 Haiku at $180/month only makes sense when extraction accuracy directly impacts downstream data quality, such as feeding a pricing intelligence product or a financial dataset.

For teams exploring alternatives beyond these three, the DeepSeek V3 pricing comparison shows another angle: open-weight models hosted via API can undercut all three on cost while matching GPT-4o-mini on extraction quality for structured pages. similarly, Qwen 2.5 is worth evaluating if you’re running your own inference stack and want to eliminate API spend entirely.

Practical routing strategy

The right approach is not picking one model and running everything through it. use a routing layer:

Gemini 1.5 Flash for high-volume, commodity extraction on clean pages (product listings, news feeds)
GPT-4o-mini for mid-complexity tasks where you need reliable JSON mode with minimal prompt tuning
Claude 3.5 Haiku for low-volume, high-value extractions where instruction-following precision matters (contracts, financial tables, schema-complex sources)

If your scraping infrastructure spans platforms where model API access is restricted, the access guide for ChatGPT, Claude, and Gemini from restricted countries covers proxy and API routing options that keep your pipeline running regardless of region. tools like Replit Agent can also scaffold the routing layer quickly if you want to prototype before committing to infrastructure.

Bottom line

For most scraping workloads in 2026, Gemini 1.5 Flash is the default choice on cost and speed. GPT-4o-mini is the safe fallback for teams already on OpenAI with predictable rate limits. Claude 3.5 Haiku earns its higher price only when prompt precision and instruction-following directly affect data quality. DRT will keep benchmarking these models as pricing and rate limits shift — the gap between them is narrowing faster than most teams expect.