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Artificial AnalysisK

Artificial AnalysisModels

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Image, Speech, Video

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Anthropic

•Claude Opus 5.5max

•Proprietary model

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Model summary

IntelligenceUpdated

#1 / 206

Artificial Analysis Intelligence Index

4 out of 4 units for Intelligence.

Speed

N/A

Output tokens per second

Unknown out of 4 units for Speed.

Cost

#87 / 206

In $4.00Out $20.00Cache Discount 95%

$5.98

Cost per Intelligence Index task

4 out of 4 units for Cost.

Verbosity

#89 / 206

260M

Output tokens from Intelligence Index

4 out of 4 units for Verbosity.

Comparison Summary

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is amongst the leading models in intelligence, but somewhat expensive when comparing to other models of similar price. The model supports text and image input, outputs text, and has a 1M tokens context window.

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) scores 58 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 25). When evaluating the Intelligence Index, it generated 260M tokens, which is very verbose in comparison to the median of 92M.

Pricing for Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is $4.00 per 1M input tokens (somewhat expensive, median: $2.00) and $20.00 per 1M output tokens (somewhat expensive, median: $10.00). In total, it cost $8708.20 to evaluate Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) on the Intelligence Index.

ReasoningYesThis page shows the reasoning version of this model.

Context window1M~1500 A4 pages of size 12 Arial font

206 models in this class

Metrics are compared against models of the same class:

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

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Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

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ProprietaryOpen WeightsOpen Weights (Commercial Use Restricted)

Artificial Analysis Intelligence Index

Open Weights

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Measures the performance of models on specific capabilities and industries

Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better

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Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

CodingAgenticTool UsePrivate DatasetUser InteractionFinanceMedicalLegalIntelligence IndexLong ContextMultimodalInstruction FollowingFaithfulnessWritingBusinessSee more

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AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2.1Updated

Agentic real-world work tasks, (Elo-500)/2000

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench 4.0New

Agentic coding & terminal use

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AANew

Medical long context reasoning

Intelligence Evaluation Relevance

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index

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AA-Briefcase Elo

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

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AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

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Most attractive quadrant

Pareto line

AnthropicXiaomiOpenAISpaceXAIGoogleMetaZ AIDeepSeekAlibaba

Cost per Intelligence Index Task

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Artificial Analysis Intelligence Index

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AnswerReasoning

Output Tokens per Intelligence Index Task

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

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AnswerReasoningCache WriteCache HitInput

Cost per Intelligence Index Task

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OutputReasoningCache WriteCache ReadNon-Cache Input

Cost to Run Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

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Cache HitInputOutput

Cache Hit

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

4 more notes

Context Window

Context WindowIntelligence Index vs. Context Window

Context Window

Context window: tokens limit · Higher is better

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Context Window for RAG

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Context Window

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Common questions about Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback)

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) was released on September 22, 2026.

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) was created by Anthropic.

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) scores 58 on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 25).

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) costs $4.00 per 1M input tokens (somewhat higher than average, median: $2.00) and $20.00 per 1M output tokens (somewhat higher than average, median: $10.00), based on Anthropic's API.

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) costs $4.00 per 1M input tokens and $20.00 per 1M output tokens (based on Anthropic's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $2.94 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) generated 260M output tokens, which is at the higher end compared to other reasoning models in a similar price tier (median: 92M).

Yes, Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) supports text and image input.

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) supports text output.

Yes, Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) supports image input and can analyze, describe, and answer questions about images.

Yes, Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is multimodal. It can process text and image input and generate text output.

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.

No, Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is proprietary. The model weights are not publicly available.

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is a proprietary model and Anthropic has not disclosed the model size or parameter count.

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) achieves a score of 58 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Yes, Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is available via API through 1 provider. Compare API providers

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is available through 1 API provider. Compare providers

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Claude Opus 5.5 Intelligence, Performance and Price Analysis (Max)