NOUMENOpen app
NOUMEN

Anthropic · Frontier

Claude 4.6 Opus

Added Feb 5, 2026

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Use in the app

Claude Opus 4.6 supports advanced coding, agentic workflows, and long-context analysis with up to a 1M-token context window.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • PDF input
Input
Text, Images
Output
Text
Reasoning effort
low · medium · high · xhigh · max
Discovery tier
Frontier

Pricing

USD per 1M tokens
Input
$5.00
Output
$25.00
Cache read
$0.50

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
128K
Average output (7 days)
1.4K tokens
Longer than 84% of 465 models

Side by side

Claude 4.6 Opus compared

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

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Claude 4.6 OpusAnthropicThis modelNvidia Nemotron 3.5 Super VLnvidiaRemove
ProviderAnthropicnvidia
Discovery tierFrontierOpen weights
AddedFeb 5, 2026Oct 15, 2026
Limits
Context window1MBest262.14K
Max output128KBest16.38K
Average output1.4K tokens1.1K tokens
Pricing, USD per 1M tokens
Input$5.00$0.15Best
Output$25.00$0.50Best
Cache read$0.50$0.02Best
Performance
Throughput45.8 tok/s654.7 tok/sBest
Time to first token2.1 s1.8 sBest
Request success (24h)100.0%Best98.0%
Benchmark indexes
Intelligence Index26.426.5Best
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputYesNo
Video inputNoYes
Reasoning effortlow, medium, high, xhigh, maxnone, medium, high

Public evaluations

Benchmarks

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

  • Claude 4.6 Opus
  • Nvidia Nemotron 3.5 Super VL

Intelligence Index

  • Claude 4.6 Opus26.4
  • Nvidia Nemotron 3.5 Super VL26.5

Claude 4.6 Opus tested at high reasoning effort

Claude 4.6 Opus: Better than 66% of 326 scored models

3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Claude 4.6 Opus: —
    • Nvidia Nemotron 3.5 Super VL: 14.8%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Claude 4.6 Opus: —
    • Nvidia Nemotron 3.5 Super VL: 2.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Claude 4.6 Opus: —
    • Nvidia Nemotron 3.5 Super VL: 999 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Claude 4.6 Opus: —
    • Nvidia Nemotron 3.5 Super VL: 1,162 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Claude 4.6 Opus: —
    • Nvidia Nemotron 3.5 Super VL: 8.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • Claude 4.6 Opus: 67.0%
    • Nvidia Nemotron 3.5 Super VL: 69.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Claude 4.6 Opus: —
    • Nvidia Nemotron 3.5 Super VL: 13.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Claude 4.6 Opus: 19.1%
    • Nvidia Nemotron 3.5 Super VL: 30.7%Top

  • GPQA Diamond

    Graduate-level science questions

    • Claude 4.6 Opus: 84.0%
    • Nvidia Nemotron 3.5 Super VL: —

  • IFBench

    Following precise output instructions

    • Claude 4.6 Opus: 44.6%
    • Nvidia Nemotron 3.5 Super VL: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Claude 4.6 Opus: —
    • Nvidia Nemotron 3.5 Super VL: 0.0%

  • SciCode

    Scientific computing in Python

    • Claude 4.6 Opus: —
    • Nvidia Nemotron 3.5 Super VL: 47.8%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Claude 4.6 Opus: 48.5%
    • Nvidia Nemotron 3.5 Super VL: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Claude 4.6 Opus: 84.8%
    • Nvidia Nemotron 3.5 Super VL: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Claude 4.6 Opus: 67.0%
    • Nvidia Nemotron 3.5 Super VL: 69.0%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Claude 4.6 Opus was tested at high reasoning effort; compare scores measured at the same effort.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
45.8 tok/s
Faster than 33% of 250 models
Time to first token
2.1 s
Quicker than 72% of 250 models
Prompt cache hits
4.4%
About $4.80 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 95.8%
  • 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": "anthropic/claude-opus-4.6",
    "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="anthropic/claude-opus-4.6",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Anthropic

Other Anthropic models, starting with the same family.