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Cut your LLM bill by matching the model to the task — a real price comparison

With per-token pricing, the model you pick is the single biggest lever on your bill. Running a frontier model on a simple classification job can cost 10–90× more than a smaller model that handles it just as well. Here's a real price comparison and where the cheaper option is usually enough.

All prices are USD per 1M tokens (input / output), list prices — the savings below come from model choice, not a discount.

The price spread

Model Input Output
Claude Opus 4.8 $5.00 $25.00
Claude Sonnet 4.6 $3.00 $15.00
Claude Haiku 4.5 $1.00 $5.00
GPT-4o $2.50 $10.00
GPT-4o mini $0.15 $0.60
Gemini 2.5 Pro $1.25 $10.00
Gemini 2.5 Flash $0.30 $2.50
DeepSeek V4 Flash $0.10 $0.20

Where a cheaper model is usually enough

Simple tasks (classification, extraction, rewriting, batch jobs) → GPT-4o mini instead of GPT-4o
$0.15 vs $2.50 input, $0.60 vs $10 output — ~94% cheaper. For structured, well-defined tasks the mini model rarely loses quality.

Everyday coding & general work → Claude Sonnet 4.6 instead of Opus 4.8
$3 vs $5 input, $15 vs $25 output — 40% cheaper. Save Opus for genuinely hard reasoning.

High-concurrency lightweight calls → Claude Haiku 4.5 instead of Sonnet 4.6
$1 vs $3 input, $5 vs $15 output — ~67% cheaper.

Long-document summarization & retrieval QA → Gemini 2.5 Flash instead of 2.5 Pro
$0.30 vs $1.25 input, $2.50 vs $10 output — ~75% cheaper.

Chinese-language work & high-volume throughput → DeepSeek V4 Flash instead of GPT-4o
$0.10 vs $2.50 input, $0.20 vs $10 output — 96–98% cheaper.

The catch: routing by hand is tedious

Knowing the cheaper model exists is one thing; actually picking it per request — and remembering to switch back for the hard prompts — is friction nobody keeps up with.

That's the problem Modelis is built around: it's an OpenAI-compatible gateway that classifies each request by difficulty and routes it to a fitted model automatically, then bills a flat per-call price so your cost stays predictable regardless of which model answered. Every response carries an X-Modelis-Routed-Model header so you can see exactly which model handled it.

Free tier — get a key.

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