Inception: Mercury Coder Small Beta
Mercury Coder Small Beta is a language model from Inception released on Feb 26, 2025. It reads text and writes text, with a 32K token context window and up to 16K tokens per reply.
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It costs $0.25 per million input tokens and $1 per million output tokens.
It supports tool calling.
- Modalities
- In / out price
- $0.25 / $1 per 1M
- Context
- 32K
- Max output
- 16K
- Released
- Feb 26, 2025
- Knowledge cutoff
- n/a
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
Mercury Coder Small Beta pricing
USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.
| Input | $0.25 / 1M tokens | |
|---|---|---|
| Output | $1 / 1M tokens |
What Mercury Coder Small Beta costs per month
Pick a workload close to yours or type your own volumes.
Supported request parameters
What you can set when you call Mercury Coder Small Beta through an OpenAI compatible API.
- Tools
tools - Temperature
temperature
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Mercury Coder Small Beta questions
How much does the Mercury Coder Small Beta API cost?
$0.25 per million input tokens and $1 per million output tokens. The estimator on this page turns that into a monthly bill for your own workload.
What is the context window of Mercury Coder Small Beta?
32,000 tokens of input, and it can write up to 16,384 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Does Mercury Coder Small Beta support tool calling?
Yes. It accepts tool definitions, which is what an agent needs to call your APIs.
Is Mercury Coder Small Beta open source?
No. Inception serves it through an API only, so you cannot download the weights or host it yourself.
When was Mercury Coder Small Beta released?
Feb 26, 2025.
Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.
Building on Mercury Coder Small Beta?
I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.
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