$1 – $50
the per-million-token range across the market, from Haiku 4.5's input rate to Fable 5's output rate
Blog
LLM API pricing: the complete guide
Every provider prices the same way, dollars per million tokens, and almost nobody's bill is legible from the rate card. This guide covers the whole market's prices, what real tasks actually cost, and the levers that cut the bill, with measured receipts throughout.
$0.0016 – $0.1462
our measured range for one real task across the ladder: same prompt, metered bills
50% + 90%
the two standard discounts almost everyone leaves unused: batch on both sides, cache reads on input
LLM APIs bill per million tokens, split into input (your prompt and context) and output (the response). As of July 2026 the market runs from $1/$5 (Claude Haiku 4.5) to $10/$50 (Claude Fable 5) per million input/output tokens, with standard discounts of 50% for batched jobs and ~90% on cached input reads.
How LLM API pricing works
Four things decide your bill, and only the first is on the pricing page. Rates: a price per million input tokens and a higher one per million output tokens. Token counts: how much context you send and how much the model writes back, which varies by model even on identical prompts. Discount usage: whether your workload exploits caching and batching. Failure rates: every failed cheap run that gets retried on an expensive model bills you twice. Rate cards answer the first; this guide covers all four.
Every major model's price, July 2026
| Model | Input $/M | Output $/M | Tier | Full breakdown |
|---|---|---|---|---|
| Claude Fable 5 | $10.00 | $50.00 | premium | Fable 5 pricing |
| GPT-5.6 Sol | $5.00 | $30.00 | frontier | Sol pricing |
| Claude Opus 5 | $5.00 | $25.00 | frontier | Opus 5 pricing |
| Claude Opus 4.8 | $5.00 | $25.00 | prior frontier | Opus 4.8 pricing |
| GPT-5.6 Terra | $2.50 | $15.00 | mid | |
| Claude Sonnet 5 | $2.00 | $10.00 | mid, intro to Aug 31 | Sonnet 5 pricing |
| GPT-5.6 Luna | $1.00 | $6.00 | volume | Luna pricing |
| Claude Haiku 4.5 | $1.00 | $5.00 | volume | |
| Pareto | at cost | at cost | blended model | model card |
Sources: Anthropic pricing, OpenAI pricing reference. Verified 2026-07-28. Note the Sonnet 5 introductory rate has a scheduled 50% increase on September 1, 2026.
What tasks actually cost: measured, not estimated
Rate cards mislead because token counts differ by model. We sent an identical dashboard-generation prompt to every model we hold keys for and metered the bills: Fable 5 $0.1462, Opus 5 $0.1158, Opus 4.8 $0.0724, Sonnet 5 $0.0410, Pareto $0.0196, Haiku 4.5 $0.0162. Two findings worth the whole table: Opus 5 billed 60% more than Opus 4.8 at the same rates because it writes more tokens (verbosity is a price), and the cheap tiers really did complete the task. Full method, prompts, and playable outputs in the Fable 5 breakdown.
For agentic coding the meter runs differently: our real Claude Code session billed $0.5887 for one bug fix, with 78x more tokens read than written. Context, not answers, is what drains credits.
The two levers that cut any bill
Batching: both major providers take 50% off both sides for asynchronous jobs. Batched Fable 5 costs exactly synchronous Opus prices; batched anything costs half. If a workload can wait an hour, this is free money. Caching: cache reads bill at roughly a tenth of the input rate (Anthropic 10%, OpenAI 90% discount with a 1.25x write premium). Our measured agentic session would have cost 2.6x more without it. Stable system prompts and shared context are the implementation; churning your cache is the mistake.
Price your own traffic
The LLM cost calculator runs this whole table against your numbers: pick a task profile or paste an actual prompt, set requests per day, and compare monthly bills across all nine models. Nothing you type leaves the browser.
The question rate cards cannot answer
Cheapest per token is not cheapest per outcome. A volume-tier model that fails 20% of the time bills you for the retry on the frontier model anyway, plus the review time. That is why we publish benchmark-by-benchmark cost per completed task rather than rate-card math alone, and why the honest framing is quality per dollar for your workload: measured, on your traffic, before you commit. The Stack Finder gets you a starting recommendation in a few quick questions.
LLM API pricing questions, answered
Why do output tokens cost more than input tokens?
Generation is the expensive operation: the model computes every output token sequentially, while input is processed in parallel. The market prices that asymmetry at roughly 5x on most models, which is why generation-heavy workloads (code, long-form writing) cost far more than classification or extraction on the same rate card.
What is the cheapest LLM API in 2026?
By list price, Claude Haiku 4.5 at $1/$5 per million tokens, with GPT-5.6 Luna at $1/$6 beside it. Cheapest per successful task depends on your failure rate: our measured runs put working one-shot code generation at $0.0162 on Haiku, but harder tasks that fail cheap bill twice.
How do I estimate tokens from text?
Roughly four characters per token for English prose, closer to 3.5 for code. A thousand-word document runs about 1,300 tokens. Our calculator does this estimate locally when you paste a prompt; exact counts vary by each model's tokenizer.
Are API prices going up or down?
Down at every tier over time, in steps rather than a slope: the Opus-class price fell 67% overnight in November 2025, and Sonnet 5 launched below its own planned rate. The exception is new premium tiers opening above the old ceiling, like Fable 5 at $10/$50. Do not architect around today's premium price being permanent.
Is there a price difference between providers for the same model?
Yes, on aggregators: the same open-weights model can be served by multiple providers at different rates and, measurably, different quality. First-party APIs price uniformly; the variance question belongs to the gateway layer, covered in our gateway guide.
Or stop doing this math per model. $100 to verify.
Buy Pareto API credits