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Inception · Efficient

Mercury 3

Added Oct 19, 2026

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Use in the app

Mercury 3 is Inception's diffusion language model for fast chat, coding, and multi-step agents. It accepts and generates text, with adjustable reasoning, parallel tool calls, structured JSON output, and a shared 1,000,000-token context window. Responses can use up to 131,072 tokens, including reasoning, within the remaining context.

  • Reasoning
  • Tools
  • Structured output
  • Parallel tools
Input
Text
Output
Text
Reasoning effort
none · low · medium · high
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.35
Cache read
$0.01

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
131.1K
Average output (7 days)
1.9K tokens
Longer than 91% of 465 models

Side by side

Mercury 3 compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Mercury 3InceptionThis modelMercury 2InceptionRemove
ProviderInceptionInception
Discovery tierEfficientBalanced
AddedOct 19, 2026Jan 1, 2024
Limits
Context window1MBest128K
Max output131.07KBest50K
Average output1.9K tokens—
Pricing, USD per 1M tokens
Input$0.10Best$0.25
Output$0.35Best$0.75
Cache read$0.01Best$0.025
Performance
Throughput735.8 tok/s—
Time to first token3.7 s—
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index—10.8
Coding Index—31.1
Capabilities
ReasoningYesYes
ToolsYesYes
Structured outputYesYes
Parallel toolsYesNo
Reasoning effortnone, low, medium, high—

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Mercury 3
  • Mercury 2

Intelligence Index

  • Mercury 3—
  • Mercury 210.8
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Mercury 3: —
    • Mercury 2: 31.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mercury 3: —
    • Mercury 2: 1.7%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mercury 3: —
    • Mercury 2: 0.3%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mercury 3: —
    • Mercury 2: 265 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mercury 3: —
    • Mercury 2: 494 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mercury 3: —
    • Mercury 2: 3.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Mercury 3: —
    • Mercury 2: 43.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mercury 3: —
    • Mercury 2: 17.1%

  • GPQA Diamond

    Graduate-level science questions

    • Mercury 3: —
    • Mercury 2: 77.0%

  • IFBench

    Following precise output instructions

    • Mercury 3: —
    • Mercury 2: 69.8%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mercury 3: —
    • Mercury 2: 0.0%

  • SciCode

    Scientific computing in Python

    • Mercury 3: —
    • Mercury 2: 37.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mercury 3: —
    • Mercury 2: 26.5%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mercury 3: —
    • Mercury 2: 70.8%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mercury 3: —
    • Mercury 2: 43.7%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
735.8 tok/s
Faster than 99% of 251 models
Time to first token
3.7 s
Quicker than 47% of 251 models
Prompt cache hits
77.9%
About $0.0299 per 1M input tokens with caching
Request success (24h)
100.0%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 83.3%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "inception/mercury-3",
    "reasoning_effort": "medium",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="inception/mercury-3",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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