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Anthropic · Frontier

Claude 4.7 Opus

Added Apr 16, 2026

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Use in the app

Claude Opus 4.7 is a major upgrade for advanced software engineering, long-running complex tasks, and high-resolution vision understanding.

  • 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)
742 tokens
Longer than 63% of 473 models

Side by side

Claude 4.7 Opus compared

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

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Claude 4.7 OpusAnthropicThis modelClaude Opus 4.8AnthropicRemove
ProviderAnthropicAnthropic
Discovery tierFrontierFrontier
AddedApr 16, 2026May 28, 2026
Limits
Context window1M1M
Max output128K128K
Average output742 tokens360 tokens
Pricing, USD per 1M tokens
Input$5.00$5.00
Output$25.00$25.00
Cache read$0.50$0.50
Performance
Throughput80.1 tok/s81.9 tok/sBest
Time to first token1.2 sBest1.2 s
Request success (24h)99.5%100.0%Best
Benchmark indexes
Intelligence Index30.941.8Best
Coding Index—74.3
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputYesYes
Reasoning effortlow, medium, high, xhigh, maxlow, medium, high, xhigh, max

Public evaluations

Benchmarks

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

  • Claude 4.7 Opus
  • Claude Opus 4.8

Intelligence Index

  • Claude 4.7 Opus30.9
  • Claude Opus 4.841.8

Claude 4.7 Opus tested at high reasoning effort

Claude 4.7 Opus: Better than 74% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Claude 4.7 Opus: —
    • Claude Opus 4.8: 74.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Claude 4.7 Opus: —
    • Claude Opus 4.8: 45.6%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Claude 4.7 Opus: —
    • Claude Opus 4.8: 17.4%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Claude 4.7 Opus: 1,067 Elo
    • Claude Opus 4.8: 1,319 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Claude 4.7 Opus: —
    • Claude Opus 4.8: 1,454 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Claude 4.7 Opus: —
    • Claude Opus 4.8: 22.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Claude 4.7 Opus: 75.7%
    • Claude Opus 4.8: 77.7%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Claude 4.7 Opus: —
    • Claude Opus 4.8: 19.4%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Claude 4.7 Opus: 33.3%
    • Claude Opus 4.8: 48.7%Top

  • GPQA Diamond

    Graduate-level science questions

    • Claude 4.7 Opus: 88.5%
    • Claude Opus 4.8: 92.0%Top

  • IFBench

    Following precise output instructions

    • Claude 4.7 Opus: 43.6%
    • Claude Opus 4.8: 62.2%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Claude 4.7 Opus: —
    • Claude Opus 4.8: 21.7%

  • SciCode

    Scientific computing in Python

    • Claude 4.7 Opus: —
    • Claude Opus 4.8: 54.4%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Claude 4.7 Opus: 54.5%
    • Claude Opus 4.8: 58.3%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Claude 4.7 Opus: 74.0%
    • Claude Opus 4.8: 94.4%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Claude 4.7 Opus: 75.7%
    • Claude Opus 4.8: 77.7%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Claude 4.7 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
80.1 tok/s
Faster than 53% of 256 models
Time to first token
1.2 s
Quicker than 94% of 256 models
Prompt cache hits
25.5%
About $3.85 per 1M input tokens with caching
Request success (24h)
99.5%
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 91.4%
  • 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-4.7",
    "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-4.7",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Anthropic

Other Anthropic models, starting with the same family.