$0.10 – $50
the per-million-token range across the market, from GPT-6 Luna's input rate to Fable 5's output rate
$0.0162 – $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 October 2026 the market runs from $0.10/$0.50 (GPT-6 Luna) 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, October 2026
| Model | Input $/M | Output $/M | Tier | Full breakdown |
|---|---|---|---|---|
| Claude Fable 5 | $10.00 | $50.00 | premium | Fable 5 pricing |
| Claude Opus 5.5 | $4.00 | $20.00 | frontier | |
| 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 |
| Kimi K3 | $3.00 | $15.00 | mid | |
| GPT-6.1 Sol | $2.00 | $10.00 | frontier | Sol pricing |
| Claude Sonnet 5.5 | $2.00 | $10.00 | mid | Sonnet pricing |
| Claude Sonnet 5 | $2.00 | $10.00 | mid | Sonnet 5 pricing |
| Claude Haiku 5.5 | $0.10 | $0.50 | volume | |
| Claude Haiku 4.5 | $1.00 | $5.00 | volume | |
| GPT-6 Luna | $0.10 | $0.50 | volume | Luna pricing |
| Pareto | $0.80 | $3.20 | blended model | model card |
Rates as of 2026-10-19, rendered from our machine-readable price feed (prices.json) with verification dates on each entry. OpenAI rates are Standard short-context; every rate here was re-checked against the provider’s own pricing page on October 19. Provider sources: Anthropic pricing, OpenAI pricing. Note that Sonnet 5's $2/$10 is now its standard rate: the 50% increase previously scheduled for September 1, 2026 was cancelled by Anthropic on August 10.
The rate card vs the rate you effectively pay
Effective rate = list output rate × the model's measured Verbosity Index score: how many tokens it writes for the same task versus the market median. GPT-6.1 Sol, GPT-6 Luna and Claude Sonnet 5.5 are shown at list rate; we have not metered their verbosity yet. Data: verbosity-index.json.
What tasks actually cost: measured, not estimated
Pareto 26.10’s preliminary benchmark scores and costs per task are on the model card. A composite score has not been published for this release.
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 5% to 10% depending on the model). 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.
Stack the two discounts on a real workload
Fixed example workload: RAG chat, 3,000 input / 400 output tokens, 1,000 requests/day, 30 days. Cache reads billed at roughly 10% of the input rate; batching takes 50% off both sides of the batched share. Both discounts are standard on Anthropic and OpenAI.
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 whole market at your volume
List-price math at the stated token profile, 30 days; no caching, batching, or verbosity applied (each moves the number, see the levers below). Pareto’s published rates are $0.80 in / $3.20 out per MTok (verified 2026-10-19); its benchmark costs per task are on the model card. Full control in the calculator.
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.
Nine months that repriced the ladder
Flagship prices moved in steps, not slopes, and the steps were violent. Click through the events; the pattern is the argument against architecting around today's rate card.
Sourced from provider announcements; the sourced flagship price table is in the Fable 5 breakdown. The September 1 Sonnet 5 increase was cancelled by Anthropic on August 10; there is no scheduled change on the ladder right now.
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, GPT-6 Luna at $0.10/$0.50 per million tokens, a tenth of Claude Haiku 4.5's $1/$5. 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.
Compare any two models
VS
List rates and dated competitor measurements: prices and measured bills. Preliminary Pareto 26.10 benchmark costs per task from October 1, 2026 are on the model card. Verbosity: Edition 2.
Or stop doing this math per model. $100 to verify.
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