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Pareto 26.9 API Pricing

Pareto 26.9 costs $2.50 per 1M input tokens and $7.50 per 1M output tokens (cache reads: $0.250). Context window: 262K. Prices verified 2026-10-21 · source

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Alternatives to consider

🏠 Same provider
Pareto 26.10
$0.800 in / $3.20 out

Cheapest unbiased model — 63% less per month

Price history
2026-10-14 → 2026-10-21 · 59 daily snapshots
inputoutput

Rebuilt daily from our snapshot archive — the chart appears once a model has 5+ snapshots of history.

Pricing behavior
Stable — no price moves across our 59 daily snapshots (2026-08-23 → 2026-10-21).

From our own daily snapshot archive — independent of provider announcements.

Input / 1M tokens
$2.50
Output / 1M tokens
$7.50
Context window
262K
Max output
131K

Token economics

Token typePricevs base input
Standard input$2.50—
Cache read$0.250−90%
Output$7.50—

Prompt caching stores repeated context server-side: reads cost far less, but writes carry a one-time premium.

What would Pareto 26.9 cost you per month?

WorkloadRequests/moAvg tokens (in/out)Est. monthly cost
Light chatbot10,000800 / 400$50.00
Coding agent5,0008,000 / 2,000$175.00
RAG pipeline30,0004,000 / 500$412.50
Doc summarizer2,00020,000 / 1,000$115.00

Estimates use list prices without prompt caching. Caching can cut input cost by up to 90% on repeated context.

About Pareto 26.9

Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks.

Compare Pareto 26.9 with

Popular head-to-heads — see all on the comparisons index.

Frequently asked questions

How much does Pareto 26.9 cost?

Pareto 26.9 costs $2.50 per 1M input tokens and $7.50 per 1M output tokens; cached input tokens cost $0.250 per 1M. Verified 2026-10-21 — provider source.

What is the context window?

262K tokens, with up to 131K output tokens per response. That's roughly 328 pages of text in a single request.

Does it support prompt caching?

Yes — cached input is $0.250/1M, about 90% cheaper than standard input. Agents and RAG workloads with repeated context benefit most.

What's a cheaper alternative?

ScaleDown: Summarize saves about 99% on a typical workload with comparable quality.

Official resources