{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-artificial-analysis","slug":"artificial-analysis","name":"Artificial Analysis","type":"benchmark","url":"https://artificialanalysis.ai/","page_url":"https://unfragile.ai/artificial-analysis","categories":["testing-quality","deployment-infra"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-artificial-analysis__cap_0","uri":"capability://data.processing.analysis.multi.dimensional.model.ranking.with.proprietary.intelligence.indexing","name":"multi-dimensional model ranking with proprietary intelligence indexing","description":"Evaluates and ranks 496+ AI models across three independent dimensions (intelligence, speed, cost) using a proprietary Intelligence Index v4.0 that synthesizes 10 named benchmarks (GDPval-AA, τ²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt) into a single numerical score. The platform aggregates these metrics into a sortable, filterable leaderboard that updates as new model versions and providers enter the market, enabling side-by-side comparison of model capabilities without requiring users to run their own evaluations.","intents":["I need to choose between Claude, GPT-4, and Llama for my production application based on objective capability metrics","I want to understand how a newly released open-source model compares to commercial alternatives across intelligence and cost","I need to track how model rankings have shifted over the past few months to inform my vendor strategy","I want to filter models by specific capabilities like reasoning (indicated by lightbulb icon) to narrow my options"],"best_for":["ML engineers and AI architects evaluating model selection for production deployments","Product managers comparing LLM API providers for cost-performance trade-offs","Technical decision-makers at enterprises choosing between OpenAI, Anthropic, Google, and open-source alternatives","Researchers tracking the evolution of model capabilities across the industry"],"limitations":["Intelligence Index methodology is proprietary and not fully transparent — users cannot audit how the 10 benchmarks are weighted or combined into the final score","Benchmark freshness SLA is unknown — changelog shows April 2024 updates but no documented re-evaluation frequency or staleness guarantees","Metrics do not include critical context window lengths, which significantly impact real-world applicability for long-document tasks","Rankings are snapshot-based — no historical time-series data or trend visualization to show how models have evolved over quarters","No real-world latency measurements — speed metric is output tokens/second (throughput) not end-to-end response time, which varies by hardware and batch size"],"requires":["Web browser with internet access","No authentication or API keys required for free tier access"],"input_types":["user preference weights (intelligence vs speed vs cost priority)","optional use-case filter (general, coding, agents, customer support)"],"output_types":["ranked model list with numerical scores","comparative metric tables (tokens/sec, $/1M tokens, intelligence index)","model detail pages with benchmark breakdowns"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-artificial-analysis__cap_1","uri":"capability://planning.reasoning.cost.performance.filtering.and.recommendation.engine","name":"cost-performance filtering and recommendation engine","description":"Implements a personalized model recommendation system that accepts user-defined weights for intelligence, speed, and cost, then applies algorithmic filtering to surface optimal models matching those priorities. The engine appears to use rule-based or weighted-scoring logic to rank models by the user's stated trade-off preferences, enabling teams to quickly identify models that fit their specific operational constraints (e.g., 'fastest models under $1/1M tokens' or 'highest intelligence within 50ms latency budget').","intents":["I have a $0.50/1M token budget and need the best intelligence I can get within that constraint","I need the fastest model for real-time chat applications, even if it's less capable","I want to balance intelligence and cost equally — show me the sweet spot models","I need to find models suitable for my specific use case (coding, customer support, general work)"],"best_for":["Cost-conscious startups and small teams optimizing for unit economics","Teams with strict latency SLAs needing to identify speed-optimized models","Product managers building pricing models that depend on LLM inference costs","DevOps engineers selecting models for different workload tiers (premium vs standard vs budget)"],"limitations":["Recommendation mechanism is opaque — unclear whether it uses weighted scoring, Pareto frontier analysis, or rule-based heuristics","Price data is list-price only — does not account for volume discounts, enterprise agreements, or actual negotiated rates that vary by customer","Speed metric (tokens/sec) is hardware-dependent — doesn't normalize for batch size, GPU type, or inference framework, making cross-provider comparisons potentially misleading","No cost simulation for actual usage patterns — cannot calculate total monthly spend for a specific query volume or token distribution","Recommendation is static at recommendation time — no ongoing monitoring to alert users when a better model enters the market or pricing changes"],"requires":["Web browser with JavaScript enabled","No API key or authentication required"],"input_types":["slider or numeric input for intelligence weight (0-100)","slider or numeric input for speed weight (0-100)","slider or numeric input for cost weight (0-100)","optional dropdown for use-case category (general, coding, agents, customer support)"],"output_types":["ranked list of recommended models","model cards showing intelligence score, speed (tokens/sec), and price ($/1M tokens)","visual comparison charts or tables"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-artificial-analysis__cap_2","uri":"capability://data.processing.analysis.real.world.agent.performance.benchmarking.with.hardware.aware.metrics","name":"real-world agent performance benchmarking with hardware-aware metrics","description":"Newly launched AA-AgentPerf capability that benchmarks AI agents on real agent workloads using actual hardware setups, moving beyond model-only evaluation to measure end-to-end agent performance including tool calling, planning, and execution overhead. This capability captures how agents perform on practical tasks (not just raw model capability) and accounts for infrastructure factors like latency, memory, and concurrent request handling that affect production deployments.","intents":["I need to choose between Claude, GPT-4, and open-source agents for my customer support automation system","I want to understand how much overhead agent frameworks add compared to raw model inference","I need to benchmark agents on my specific workload (coding tasks, customer support, general work) before committing to a vendor","I want to see how agents perform under realistic load (concurrent requests, long-running tasks) not just single-request latency"],"best_for":["Teams building agentic AI systems (not just using models directly)","Companies evaluating agent frameworks and orchestration platforms","Technical leads assessing whether agent overhead is acceptable for their latency requirements","Researchers studying the performance characteristics of agentic vs non-agentic LLM deployments"],"limitations":["AA-AgentPerf is newly launched with minimal documentation — specific workloads, hardware configurations, and evaluation methodology are not detailed in available materials","Unclear which agent frameworks are included in benchmarks (e.g., LangChain, LlamaIndex, AutoGPT, custom implementations)","Hardware specifications for benchmarks are not documented — results may not be representative of user's actual deployment hardware","Agent performance is highly task-dependent — benchmarks on one workload may not predict performance on different agent tasks","No breakdown of overhead attribution — unclear how much latency comes from the model vs the agent framework vs tool calling"],"requires":["Web browser with internet access","No special setup or API keys required to view benchmarks"],"input_types":["optional filter for agent framework or platform","optional filter for workload type (coding, customer support, general work)","optional filter for hardware tier (standard, high-performance)"],"output_types":["agent performance rankings with latency, throughput, and success rate metrics","comparative tables showing agent vs raw model performance overhead","workload-specific performance breakdowns"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-artificial-analysis__cap_3","uri":"capability://data.processing.analysis.specialized.capability.indexing.for.coding.and.reasoning.tasks","name":"specialized capability indexing for coding and reasoning tasks","description":"Provides domain-specific benchmark indices (Coding Index, Agentic Index, and reasoning capability indicators) that isolate model performance on specialized tasks beyond general intelligence. The platform marks models with reasoning capabilities (indicated by lightbulb icon) and maintains separate leaderboards for coding-specific evaluation, allowing users to find models optimized for their specific task domain rather than relying on general-purpose rankings.","intents":["I need the best model specifically for code generation and debugging, not general chat","I want to identify which models have strong reasoning capabilities for complex problem-solving","I need to compare models on agentic tasks (planning, tool use, multi-step reasoning) separately from raw intelligence","I want to filter out models that lack reasoning capabilities for my use case"],"best_for":["Software engineers and development teams selecting models for code generation and refactoring","Teams building reasoning-heavy applications (research, analysis, complex decision-making)","AI researchers studying model specialization across different task domains","Product managers building domain-specific AI features (coding copilots, research assistants)"],"limitations":["Coding Index and Agentic Index methodologies are not documented — unclear which benchmarks are included or how they differ from the general Intelligence Index","Reasoning capability indicator (lightbulb icon) is binary — no nuance on degree of reasoning ability or types of reasoning (chain-of-thought, multi-step, etc.)","Specialized indices may not reflect performance on your specific coding language or domain — benchmarks may emphasize Python/JavaScript over niche languages","No task-specific latency or cost data — a model may rank high on Coding Index but be slower or more expensive for code tasks than alternatives","Agentic Index is mentioned but not detailed — unclear if it measures agent framework compatibility or raw agentic capability"],"requires":["Web browser with internet access","No special setup or authentication required"],"input_types":["optional filter for task domain (coding, reasoning, agentic)","optional filter for reasoning capability (yes/no)"],"output_types":["domain-specific model rankings with index scores","comparative tables showing domain-specific performance","model detail pages with domain-specific benchmark breakdowns"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-artificial-analysis__cap_4","uri":"capability://search.retrieval.comparative.agent.platform.analysis.and.recommendation","name":"comparative agent platform analysis and recommendation","description":"Evaluates and compares AI agent platforms and frameworks (not just models) across capabilities, pricing, and supported integrations. The platform provides agent-specific comparison tables that help users choose between different agentic systems (e.g., comparing agents built on Claude vs GPT-4 vs open-source, or comparing agent orchestration platforms), including filtering by use case (general work, coding, customer support) and platform features.","intents":["I need to choose between a Claude-based agent and a GPT-4-based agent for my customer support system","I want to compare agent pricing models (per-task, per-token, subscription) across different platforms","I need to find agents that support my specific integrations (Slack, Salesforce, custom APIs)","I want to understand which agents are best for coding tasks vs general work vs customer support"],"best_for":["Non-technical founders and product managers evaluating agent solutions without building custom agents","Teams deciding between buying pre-built agents vs building custom agents on models","Enterprise procurement teams comparing agent platform vendors","Developers evaluating agent frameworks before committing to one for their stack"],"limitations":["Agent comparison is less mature than model comparison — fewer agents tracked and less frequent updates compared to model leaderboards","Agent capabilities are harder to quantify than model metrics — comparison relies more on feature lists than objective benchmarks","Pricing for agents is often usage-based and opaque — listed prices may not reflect actual costs for your specific workload","Agent performance is highly dependent on underlying model choice — a 'best agent' ranking may be driven by model selection rather than agent framework quality","No integration testing data — platform doesn't test how agents work with specific third-party services (Slack, Salesforce, etc.)"],"requires":["Web browser with internet access","No API keys or authentication required to view comparisons"],"input_types":["optional filter for use case (general work, coding, customer support)","optional filter for pricing model (per-task, per-token, subscription)","optional filter for required integrations"],"output_types":["ranked agent list with capability and pricing comparison","comparative feature matrices","agent detail pages with integration lists and pricing breakdowns"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-artificial-analysis__cap_5","uri":"capability://data.processing.analysis.model.evaluation.changelog.and.update.tracking","name":"model evaluation changelog and update tracking","description":"Maintains a timestamped changelog of model ranking changes, new model additions, and benchmark updates, allowing users to track how the model landscape has evolved over time. The changelog shows dated entries (e.g., April 20-24, 2024) indicating when models were added, re-evaluated, or changed position in rankings, providing transparency into platform updates and enabling users to understand which changes are due to new models vs re-evaluation of existing models.","intents":["I want to see which new models have been added to the platform in the last month","I need to understand why a model I was tracking has changed position in the rankings","I want to know when the platform last re-evaluated models to assess benchmark freshness","I need to track how a specific model's ranking has changed over time to inform my vendor strategy"],"best_for":["Technical decision-makers monitoring the model landscape for strategic planning","Researchers tracking model capability evolution over time","Teams evaluating whether to switch models based on recent ranking changes","Analysts studying the pace of model improvement and competitive dynamics"],"limitations":["Changelog is not queryable or filterable — users must manually scan entries to find specific models or date ranges","No historical snapshots of full rankings — changelog shows updates but not the complete ranking state at each point in time","Update frequency is not documented — unclear if changelog is updated daily, weekly, or monthly","No trend analysis or visualization — changelog is text-based, not graphical, making it hard to spot patterns","No explanation of why rankings changed — changelog shows that a model moved but not whether it's due to re-evaluation, new benchmarks, or new models entering the market"],"requires":["Web browser with internet access","No special setup or authentication required"],"input_types":["optional date range filter (from/to dates)","optional model name search"],"output_types":["timestamped changelog entries","model addition/removal/re-evaluation notifications","ranking change indicators (up/down/new)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-artificial-analysis__cap_6","uri":"capability://text.generation.language.independent.analysis.and.editorial.content.on.model.trends","name":"independent analysis and editorial content on model trends","description":"Publishes original analysis articles and commentary on model releases, capability trends, and competitive dynamics (e.g., 'DeepSeek is back among the leading open weights models'). These editorial pieces provide context and interpretation beyond raw benchmark numbers, helping users understand the significance of ranking changes and emerging trends in the model landscape. Content is authored by the Artificial Analysis team and appears alongside benchmark data to provide narrative context.","intents":["I want to understand the implications of a new model release for my product strategy","I need context on why open-source models are gaining ground against commercial APIs","I want expert analysis on whether a ranking change reflects a real capability improvement or just benchmark noise","I need to stay informed on emerging trends in the model landscape without reading dozens of blog posts"],"best_for":["Product managers and technical leaders making strategic model selection decisions","Researchers and analysts studying model market dynamics","Teams evaluating whether to switch models based on new releases","Non-technical stakeholders who need context on model capability changes"],"limitations":["Editorial content is subjective — analysis reflects the Artificial Analysis team's perspective, not a consensus view","Content frequency is unknown — unclear how often new analysis pieces are published or whether there's a regular cadence","No peer review or external validation — analysis is not subject to academic rigor or external fact-checking","Limited depth on methodology — analysis pieces may not explain the technical details of why a model performs better","No structured tagging or categorization — articles are listed chronologically, making it hard to find analysis on specific topics"],"requires":["Web browser with internet access","No special setup or authentication required"],"input_types":["optional date range filter","optional topic or model name search"],"output_types":["article text with analysis and commentary","embedded benchmark data and charts","links to related models or benchmarks"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-artificial-analysis__cap_7","uri":"capability://search.retrieval.web.based.interactive.model.comparison.interface","name":"web-based interactive model comparison interface","description":"Provides a responsive web dashboard where users can select models, adjust comparison criteria, and view side-by-side metrics in real-time. The interface supports filtering by use case, reasoning capability, and custom metric weighting, with interactive tables and charts that update as users modify their selections. The dashboard is designed for quick exploration and decision-making without requiring API calls or command-line tools.","intents":["I want to quickly compare three models side-by-side to see which is fastest and cheapest","I need to filter models by reasoning capability and then sort by cost","I want to visualize the trade-off between intelligence and speed for different models","I need to drill down into a specific model to see its benchmark breakdown and detailed metrics"],"best_for":["Non-technical stakeholders who need quick model comparisons without command-line tools","Teams making rapid model selection decisions in meetings or planning sessions","Researchers exploring the model landscape interactively","Anyone who prefers visual interfaces over APIs or data exports"],"limitations":["No programmatic access — users cannot integrate Artificial Analysis data into their own tools or dashboards via API","No data export functionality documented — users cannot download comparison data for further analysis or sharing","Limited customization — interface is fixed; users cannot create custom metrics or combine benchmarks in novel ways","No persistence — selections and comparisons are not saved; users must recreate them on each visit","Performance may degrade with 496+ models — filtering and sorting large datasets in the browser may become slow"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","JavaScript enabled","Internet connection with access to artificialanalysis.ai","No authentication or API keys required"],"input_types":["model selection (checkboxes or multi-select dropdown)","metric weighting sliders (intelligence, speed, cost)","filter dropdowns (use case, reasoning capability)","sort controls (by intelligence, speed, cost, or custom metric)"],"output_types":["interactive comparison tables with sortable columns","scatter plots or bubble charts showing metric relationships","model detail cards with benchmark breakdowns","recommendation lists based on selected criteria"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-artificial-analysis__cap_8","uri":"capability://data.processing.analysis.multi.provider.model.aggregation.and.normalization","name":"multi-provider model aggregation and normalization","description":"Aggregates model information and pricing from multiple LLM providers (OpenAI, Anthropic, Google, Meta, Mistral, etc.) into a unified schema, normalizing pricing units ($/1M tokens), speed metrics (tokens/second), and capability scores across providers with different pricing models and measurement approaches. This allows direct comparison of models from different vendors despite their different pricing structures (per-token, per-request, subscription) and measurement methodologies.","intents":["I want to compare OpenAI's GPT-4 with Anthropic's Claude and Google's Gemini using the same metrics","I need to understand how pricing differs across providers when they use different billing models","I want to see all available models in one place rather than visiting each provider's website","I need to identify which provider offers the best value for my specific use case"],"best_for":["Teams evaluating multiple LLM providers for the first time","Cost-conscious organizations comparing pricing across vendors","Technical leads building multi-provider LLM applications","Procurement teams negotiating with multiple vendors"],"limitations":["Pricing normalization is lossy — converting different billing models (per-token, per-request, subscription) to $/1M tokens may not reflect actual costs for your usage pattern","Speed metrics are not normalized for hardware or batch size — tokens/second varies by inference hardware, batch size, and provider infrastructure, making cross-provider comparisons potentially misleading","Provider pricing changes frequently — list prices may be stale, and actual negotiated rates (especially for enterprise) are not captured","No volume discount data — pricing shown is list price, not the discounted rates available for high-volume customers","Model availability varies by region and access tier — a model may be available in one region but not another, or require special access"],"requires":["Web browser with internet access","No API keys or authentication required to view aggregated data"],"input_types":["optional provider filter (OpenAI, Anthropic, Google, Meta, Mistral, etc.)","optional model type filter (base, instruction-tuned, specialized)","optional region or access tier filter"],"output_types":["unified model list with normalized metrics","provider comparison tables","pricing comparison across providers","model detail pages with provider-specific information"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-artificial-analysis__cap_9","uri":"capability://search.retrieval.free.tier.benchmarking.and.comparison.access.without.authentication","name":"free-tier benchmarking and comparison access without authentication","description":"Provides full access to all model rankings, comparisons, metrics, and analysis content without requiring user registration, login, or payment. The platform operates on a freemium model where core benchmarking and comparison features are available to all users, with no documented paywall or premium tier restrictions visible in the provided materials. This low-friction access model enables rapid exploration and decision-making without account creation overhead.","intents":["I want to quickly check model rankings without signing up for an account","I need to share a model comparison with my team without worrying about access restrictions","I want to explore the platform before committing to any paid tier","I need to access benchmark data from a restricted network that blocks authentication"],"best_for":["Individual developers and researchers exploring the model landscape","Teams making quick model selection decisions without procurement overhead","Organizations with restrictive authentication policies","Anyone who values privacy and wants to avoid account creation"],"limitations":["Pricing model is undocumented — it's unclear if there are premium tiers, API access fees, or enterprise features not visible in free tier","No data export or API access documented — free tier may be limited to web interface browsing only","No personalization or saved comparisons — users cannot save their preferences or create custom dashboards","No historical data access — free tier may not include time-series data or historical rankings","No SLA or uptime guarantee documented — free tier may have lower availability or performance guarantees than paid tiers"],"requires":["Web browser with internet access","No registration, API key, or authentication required","No payment method required"],"input_types":["none — access is immediate upon visiting the website"],"output_types":["full access to all benchmark data, comparisons, and analysis content"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Web browser with internet access","No authentication or API keys required for free tier access","Web browser with JavaScript enabled","No API key or authentication required","No special setup or API keys required to view benchmarks","No special setup or authentication required","No API keys or authentication required to view comparisons","Modern web browser (Chrome, Firefox, Safari, Edge)","JavaScript enabled","Internet connection with access to artificialanalysis.ai"],"failure_modes":["Intelligence Index methodology is proprietary and not fully transparent — users cannot audit how the 10 benchmarks are weighted or combined into the final score","Benchmark freshness SLA is unknown — changelog shows April 2024 updates but no documented re-evaluation frequency or staleness guarantees","Metrics do not include critical context window lengths, which significantly impact real-world applicability for long-document tasks","Rankings are snapshot-based — no historical time-series data or trend visualization to show how models have evolved over quarters","No real-world latency measurements — speed metric is output tokens/second (throughput) not end-to-end response time, which varies by hardware and batch size","Recommendation mechanism is opaque — unclear whether it uses weighted scoring, Pareto frontier analysis, or rule-based heuristics","Price data is list-price only — does not account for volume discounts, enterprise agreements, or actual negotiated rates that vary by customer","Speed metric (tokens/sec) is hardware-dependent — doesn't normalize for batch size, GPU type, or inference framework, making cross-provider comparisons potentially misleading","No cost simulation for actual usage patterns — cannot calculate total monthly spend for a specific query volume or token distribution","Recommendation is static at recommendation time — no ongoing monitoring to alert users when a better model enters the market or pricing changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=artificial-analysis","compare_url":"https://unfragile.ai/compare?artifact=artificial-analysis"}},"signature":"5Gbj4HZkQXoG2YBNllK34MTqkmJkgSxERsq79m2SKFjkW3yCKPY6XcPiD/cDPDZsbQXoU5Psxw1OX7N8Uhx6Dw==","signedAt":"2026-06-22T15:18:04.587Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/artificial-analysis","artifact":"https://unfragile.ai/artificial-analysis","verify":"https://unfragile.ai/api/v1/verify?slug=artificial-analysis","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}