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Inception: Mercury 3 API Pricing

Inception: Mercury 3 costs $0.100 per 1M input tokens and $0.350 per 1M output tokens (cache reads: $0.010). Context window: 1M. Prices verified 2026-10-21 · source

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Alternatives to consider

🏠 Same provider
Inception: Mercury 2.5
$0.040 in / $0.150 out
Quality score: 12.3

Cheapest inception model — 58% less per month

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
$0.100
Output / 1M tokens
$0.350
Context window
1M
Max output
131K

Token economics

Token typePricevs base input
Standard input$0.100—
Cache read$0.010−90%
Output$0.350—

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

Reasoning behavior
Mode: On by default (toggleable)
Effort levels:highmediumlownone

Higher effort = deeper thinking = more reasoning tokens billed as output.

What would Inception: Mercury 3 cost you per month?

WorkloadRequests/moAvg tokens (in/out)Est. monthly cost
Light chatbot10,000800 / 400$2.20
Coding agent5,0008,000 / 2,000$7.50
RAG pipeline30,0004,000 / 500$17.25
Doc summarizer2,00020,000 / 1,000$4.70

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

About Inception: Mercury 3

Mercury 3 is a diffusion-based language model from Inception, and the lab's most capable model to date. It is suited for agentic workflows and real-time applications where latency compounds, such...

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Frequently asked questions

How much does Inception: Mercury 3 cost?

Inception: Mercury 3 costs $0.100 per 1M input tokens and $0.350 per 1M output tokens; cached input tokens cost $0.010 per 1M. Verified 2026-10-21 — provider source.

What is the context window?

1M tokens, with up to 131K output tokens per response. That's roughly 1250 pages of text in a single request.

Does it support prompt caching?

Yes — cached input is $0.010/1M, about 90% cheaper than standard input. Agents and RAG workloads with repeated context benefit most.

What's a cheaper alternative?

ScaleDown: Summarize saves about 83% on a typical workload with comparable quality.

Official resources