Model Cost Profile

Inception: Mercury Coder

Developer: inception

Pricing updated Mar 11, 2026

Input rank: #156Output rank: #149

Live Pricing

Input: $0.2500

Output: $0.7500

Pricing via OpenRouter API ยท Last synced Mar 11, 2026

Inception: Mercury Coder offers a substantial context window of 128,000 tokens, making it ideal for complex applications such as document summarization, code generation, and large-scale data analysis. With an input price of $0.25 per million tokens and an output price of $1.00 per million tokens, teams can effectively manage costs while leveraging its capabilities for extensive projects. This model is particularly suited for organizations requiring in-depth processing of large datasets or intricate text interactions, allowing for efficient resource allocation.

๐Ÿ”ง Tool Calling๐Ÿ“‹ Structured Output

Context Window

128,000

Tokens

Input Price / 1M

$0.2500

Prompt tokens

Output Price / 1M

$0.7500

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

Inception: Mercury Coder Pricing Trend

Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 โ€” Mar 11
$0.2500$0.5000$0.7500Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$0.2500

Current Output / 1M

$0.7500

Cheaper Alternatives to Compare

Quick links for cost-down decisions before production rollout.

FAQ

Common pricing and benchmark questions for Inception: Mercury Coder.

How much does Inception: Mercury Coder cost per 1M input tokens?

Inception: Mercury Coder input pricing is $0.2500 per 1M tokens based on the latest synced provider data.

How much does Inception: Mercury Coder cost per 1M output tokens?

Inception: Mercury Coder output pricing is $0.7500 per 1M tokens based on the latest synced provider data.

What context window does Inception: Mercury Coder support?

Inception: Mercury Coder supports a context window of 128,000 tokens.

How can I compare Inception: Mercury Coder with cheaper alternatives?

Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.