Claude Opus 5.5 (max with fallback) - Intelligence, Performance & Price Analysis | Artificial AnalysisArtificial AnalysisKArtificial AnalysisModelsCoding AgentsImage, Speech, VideoInferenceLeaderboardsAboutAI TrendsArenasKAnthropic•Claude Opus 5.5max•Proprietary model•Released September 2026Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) Intelligence, Performance & Price AnalysisCompareTry it out API Provider Benchmarks Model summaryIntelligenceUpdated#1 / 20658Artificial Analysis Intelligence Index4 out of 4 units for Intelligence.SpeedN/AOutput tokens per secondUnknown out of 4 units for Speed.Cost#87 / 206In $4.00Out $20.00Cache Discount 95%$5.98Cost per Intelligence Index task4 out of 4 units for Cost.Verbosity#89 / 206260MOutput tokens from Intelligence Index4 out of 4 units for Verbosity.Comparison SummaryClaude 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.Technical specificationsReasoningYesThis page shows the reasoning version of this model.A non-reasoning variant may also exist.Input modalitySupports: text and imageOutput modalitySupports: textContext window1M~1500 A4 pages of size 12 Arial font206 models in this classMetrics are compared against models of the same class:Non-reasoning models → compared only with other non-reasoning modelsReasoning models → compared across both reasoning and non-reasoningOpen weights models → compared only with other open weights models of the same size class:Tiny: ≤4B parametersSmall: 4B–40B parametersMedium: 40B–150B parametersLarge: >150B parametersProprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:<$0.15 per 1M tokens$0.15–$1 per 1M tokens>$1 per 1M tokensModel ComparisonAPI Provider BenchmarksHighlightsUpdatedIntelligenceArtificial Analysis Intelligence Index · Higher is betterSpeedOutput tokens per second · Higher is betterCost per TaskWeighted average cost (USD) per Intelligence Index task · Lower is betterIntelligenceUpdatedCapability IndexesBenchmarksIntelligence Index ComparisonsToken UseCostContext WindowPrompt OptionsIntelligenceUpdatedArtificial Analysis Intelligence IndexArtificial 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 v1.129 of 661 modelsAdd model from specific providerArtificial Analysis Intelligence IndexArtificial 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.Open Weights / ProprietaryReasoning / Non-ReasoningText Only / Multimodal InputsArtificial Analysis Intelligence Index by Open Weights / ProprietaryArtificial 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 v1.129 of 661 modelsAdd model from specific providerProprietaryOpen WeightsOpen Weights (Commercial Use Restricted)Artificial Analysis Intelligence IndexArtificial 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.Open WeightsIndicates 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.Capability IndexesMeasures the performance of models on specific capabilities and industriesFinance & AccountingStrategy & OpsLegalEngineeringEconomicsArtificial Analysis Finance & Accounting IndexIncorporates 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 better29 of 158 modelsAdd model from specific providerBenchmarksIntelligence EvaluationsIntelligence evaluations measured independently by Artificial Analysis · Higher is betterCodingAgenticTool UsePrivate DatasetUser InteractionFinanceMedicalLegalIntelligence IndexLong ContextMultimodalInstruction FollowingFaithfulnessWritingBusinessSee more18 of 26 evaluations29 of 661 modelsAdd model from specific providerAA-Briefcase v1.1UpdatedAgentic knowledge work, (Elo-500)/2000GDPval-AA v2.1UpdatedAgentic real-world work tasks, (Elo-500)/2000AutomationBench-AAUpdatedAgentic SaaS workflowsTerminal-Bench 4.0NewAgentic coding & terminal useSciCodeCodingHumanity's Last ExamReasoning & knowledgeGDP.pdfNewProfessional document reasoning, All-passCritPtPhysics reasoningAA-Omniscience AccuracyKnowledgeAA-Omniscience Non-Hallucination Rate1 - hallucination rateAA-LCR v1.1Long context reasoningHarvey LAB-AALegal agentic work, criterion pass rateEnterpriseOps-Gym-AAAgentic business operationsAA-AnalystAgentQuantitative analysis on spreadsheets & documents𝜏³-BankingAgentic tool useITBench-AAKubernetes incident root-cause analysisMMMU-ProVisual reasoningMLCR-AANewMedical long context reasoningIntelligence Evaluation RelevanceWhile model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.Artificial Analysis Intelligence IndexArtificial 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.AA-Briefcase v1.1UpdatedAA-Briefcase EloAA-Briefcase Rubric Score (%)Analytical Quality & Presentation EloAA-Briefcase EloAA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better29 of 179 modelsAdd model from specific providerAA-Briefcase EloAA-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.AA-OmniscienceAA-Omniscience IndexAA-Omniscience AccuracyAA-Omniscience Hallucination RateAA-Omniscience IndexAA-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.29 of 536 modelsAdd model from specific providerAA-Omniscience IndexAA-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.Intelligence Index ComparisonsIntelligence Index vs. Cost per TaskIntelligence Index vs. Time per TaskIntelligence Index vs. Output SpeedIntelligence Index vs. End-to-End Response TimeIntelligence Index vs. Cost per Intelligence Index TaskArtificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task29 of 661 modelsMost attractive quadrantPareto lineAnthropicXiaomiOpenAISpaceXAIGoogleMetaZ AIDeepSeekAlibabaCost per Intelligence Index TaskWeighted 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 IndexArtificial 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.Token UseOutput Tokens per TaskIntelligence Index vs. Output Tokens per TaskIntelligence Index Token UseIntelligence Index vs. Token UseOutput Tokens per Intelligence Index TaskWeighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index29 of 661 modelsAnswerReasoningOutput Tokens per Intelligence Index TaskThe 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).CostCost per TaskIntelligence Index vs. Cost per TaskEvaluation BreakdownCost per Intelligence Index TaskWeighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better29 of 661 modelsAnswerReasoningCache WriteCache HitInputCost per Intelligence Index TaskWeighted 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.Intelligence Index Total CostIntelligence Index vs. Total CostCost to Run Artificial Analysis Intelligence IndexCost (USD) to run all evaluations in the Artificial Analysis Intelligence Index29 of 661 modelsAdd model from specific providerOutputReasoningCache WriteCache ReadNon-Cache InputCost to Run Artificial Analysis Intelligence IndexThe 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).Cache Hit, Input, and Output PricingBlended PriceBlended Price (Stacked)Cache DiscountIntelligence Index vs. PriceIntelligence Index vs. Price (Log, Inverted)Image Input PricingPricing: Cache Hit, Input, and OutputPrice (USD per M Tokens)29 of 661 modelsAdd model from specific providerCache HitInputOutputCache HitPrice 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 notesContext WindowContext WindowIntelligence Index vs. Context WindowContext WindowContext window: tokens limit · Higher is better29 of 661 modelsAdd model from specific providerContext Window for RAGLarger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.Context WindowMaximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).Frequently Asked QuestionsCommon questions about Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback)When was Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) released?Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) was released on September 22, 2026.Who created Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback)?Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) was created by Anthropic.How intelligent is Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback)?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).How much does Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) cost?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.What is Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) API pricing?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 pricingHow verbose is Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback)?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).Is Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) a reasoning model?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.What input modalities does Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) support?Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) supports text and image input.What output modalities does Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) support?Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) supports text output.Can Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) process images?Yes, Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) supports image input and can analyze, describe, and answer questions about images.Is Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) multimodal?Yes, Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is multimodal. It can process text and image input and generate text output.What is the context window of Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback)?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.Is Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) open source?No, Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is proprietary. The model weights are not publicly available.How many parameters does Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) have?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.How does Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) perform on benchmarks?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.Is Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) available via API?Yes, Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is available via API through 1 provider. Compare API providersWhere can I use Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback)?Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is available through 1 API provider. Compare providersArtificial AnalysisGet notified about new articlesEmail addressSubscribeArtificial AnalysisExploreLLM LeaderboardImage ArenaVideo ArenaAI AgentsEvaluationsProductsOptimaMicroEvalsModel RecommenderData PlaygroundImage LabCompanyAboutMethodologyContactArticlesXLinkedInYouTubeRednoteDiscord© 2026 Artificial AnalysisTerms of UseData Platform TermsPrivacy PolicyEnglish

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) is a proprietary model developed by Anthropic, released in September 2026, which is situated among leading models in intelligence based on the Artificial Analysis framework. This model functions as a reasoning model, employing extended thinking or chain-of-thought reasoning to address complex problems before yielding an answer. In terms of input modalities, Claude Opus 5.5 is multimodal, supporting both text and image inputs, and it outputs text. Its context window is substantial, offering 1.0 million tokens, which is noted to accommodate approximately 1500 pages of text in a standard font format.

The model’s performance is quantified using several capability indexes, prominently featuring the Artificial Analysis Intelligence Index, which scores 58, positioning it above the median of 25 for comparable models. This composite metric evaluates performance across reasoning, knowledge, mathematics, and coding, incorporating a set of evaluations such as AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, and AA-Omniscience. The benchmark results further distinguish capabilities through indices like AA-Briefcase Elo, which combines analytical quality, presentation, and rubric pass rates, and the AA-Omniscience Index, which measures knowledge reliability and hallucination rates.

When analyzing performance against other models, cost is a significant factor; Claude Opus 5.5 is priced at $4.00 per 1 million input tokens and $20.00 per 1 million output tokens, which is considered somewhat higher than the median pricing for similar models. The evaluation of the Intelligence Index for this model resulted in the generation of 260 million output tokens, which is higher than the median volume observed across reasoning models in the same price tier. Furthermore, the model exhibits specific characteristics related to knowledge handling, with the AA-Omniscience Index scoring rewards for correct answers while penalizing hallucinations, ranging from negative one hundred to positive one hundred.

The extensive benchmarking also provides deep comparisons regarding efficiency and resource utilization. Metrics assess speed, output tokens per second, and various cost structures, including the cost per task for the Intelligence Index. The analysis further delineates the relationship between intelligence and various factors, comparing the Intelligence Index against output tokens, time per task, and cost per task, illustrating the trade-offs between performance, throughput, and expenditure. Specific comparisons also address the cost implications of using cached prompts versus raw input and output, reflecting a blended pricing structure that accounts for cache hit, input, and output costs. Overall, the data establishes Claude Opus 5.5 as a highly capable multimodal reasoning engine with a robust, albeit premium, performance profile across a wide array of analytical and agentic work tasks.