Anthropic · Frontier
Claude Opus 4.8
Anthropic Claude Opus 4.8 with text, image, and file inputs and 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)
- 360 tokens
- Longer than 37% of 473 models
Side by side
Compare models
Put Claude Opus 4.8 next to up to three other models. The comparison stays in the link, so you can share it.
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
Intelligence Index
41.8
Tested at max reasoning effort
Better than 86% of 326 scored models
Indexes
Coding Index
74.3Composite of the coding evaluations
Better than 86% of 168 scored models
Agentic work
AutomationBench-AA
45.6%Workflow automation, penalized for guardrail violations
Better than 60% of 173 scored models
AutomationBench-AA tasks completed
17.4%Workflows finished end to end without a guardrail violation
Better than 63% of 173 scored models
AA-Briefcasev1.1
1,319 EloAgentic knowledge work, rated head to head
Better than 68% of 166 scored models
GDPval-AAv2.1
1,454 EloEconomically valuable occupational tasks, rated head to head
Better than 73% of 187 scored models
Document reasoning
GDP.pdf
22.8%Professional reasoning over PDF documents, all-pass rate
Better than 81% of 171 scored models
AA-LCRv1.1
77.7%Reasoning across long documents
Better than 64% of 309 scored models
MLCR-AA
19.4%Long-context reasoning over medical records
Better than 80% of 121 scored models
Reasoning and knowledge
Humanity’s Last Exam
48.7%Expert-level questions across academic fields
Better than 92% of 323 scored models
GPQA Diamond
92.0%Graduate-level science questions
Better than 86% of 291 scored models
IFBench
62.2%Following precise output instructions
Better than 61% of 215 scored models
Coding
Terminal-Benchv4.0
21.7%Practical software engineering in a terminal
Better than 74% of 171 scored models
SciCode
54.4%Scientific computing in Python
Better than 72% of 174 scored models
Legacy evaluations
Terminal-Bench Hard
58.3%Earlier terminal task suite
Better than 97% of 212 scored models
τ²-Bench Telecom
94.4%Tool use in a customer-support simulation
Better than 85% of 214 scored models
AA-LCR (unversioned)
77.7%Long-context reasoning, earlier grading
Better than 64% of 309 scored models
Scores from Artificial Analysis, updated Oct 26, 2026. Claude Opus 4.8 was tested at max reasoning effort; compare scores measured at the same effort.Artificial Analysis
- #12Roleplay29.6%
Ranks among Noumen catalog models on Use cases leaderboards. Only top-20 placements are listed.
- #16Coding1533.3
- #8Longer Query1502.9
- #14Creative Writing1469.2
- #9Instruction Following1491.0
- #12Hard Prompts1513.1
- #17English1490.0
- #14Expert1526.0
- #14French1506.9
- #14German1492.9
- #9Hard Prompts English1516.9
- #11Industry Business And Management And Financial Operations1488.9
- #16Industry Entertainment And Sports And Media1460.1
- #12Industry Legal And Government1497.6
- #16Industry Life And Physical And Social Science1504.1
- #14Industry Mathematical1506.5
- #18Industry Medicine And Healthcare1500.2
- #14Industry Software And It Services1523.0
- #14Industry Writing And Literature And Language1475.4
- #10Multi Turn1497.0
- #14Polish1485.4
- #15Russian1495.0
- #15Spanish1480.5
- #14Vision Style Control Overall1285.7
- #15Vision Style Control · Chinese1334.0
- #10Vision Style Control · Creative Writing Vision1289.1
- #14Vision Style Control · Diagram1318.9
- #10Vision Style Control · English1281.4
- #8Vision Style Control · Homework1331.8
- #13Vision Style Control · Humor1269.8
- #15Vision Style Control · Ocr1298.9
- #14Search Overall1204.1
- #9Document Overall1474.8
- #7Document Style Control Overall1485.9
Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena
- #13FrontierCode Main46.5%
- #12FrontierCode Extended59.6%
Ranks among Noumen catalog models on FrontierCode leaderboards. Only top-20 placements are listed.FrontierCode
- #16Epoch Capabilities Index159.0
Ranks among Noumen catalog models on Epoch AI leaderboards. Only top-20 placements are listed.Epoch AI
- #19ARC-AGI-272.1%
- #10ARC-AGI-3 (Default harness)1.5%
Ranks among Noumen catalog models on ARC Prize leaderboards. Only top-20 placements are listed.ARC Prize
- #8EQ-Bench 41267
- #15Longform Writing80.8
- #2EQ-Bench 32058
Ranks among Noumen catalog models on EQ-Bench leaderboards. Only top-20 placements are listed.EQ-Bench
- #10ASCII Art1276
- #5Agentic Game Dev1201
- #13Web Apps1233
- #8Full Stack1249
- #6Native Apps1236
- #11Mobile Apps1221
Ranks among Noumen catalog models on Design Arena leaderboards. Only top-20 placements are listed.Design Arena
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
- 70.6 tok/s
- Faster than 50% of 260 models
- Time to first token
- 1.1 s
- Quicker than 93% of 260 models
- Prompt cache hits
- 35.8%
- About $3.39 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.
- 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.
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-4.8",
"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-4.8",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Anthropic
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