AOverview
Artificial Analysis offers independent comparison and analysis of AI models and API hosting providers, featuring benchmarks and leaderboards across intelligence, coding agents, image/speech/video, and inference metrics to help teams choose the best model and provider for a use case. It publishes data-driven reports such as Intelligence Indexes, capability indexes, and trend analyses that translate vendor claims into actionable insights. It does not host or sell models, instead providing independent evaluation as media/data products for AI buyers. Its goal is to help buyers understand the AI landscape and select the most suitable models and API providers.
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About Artificial Analysis
Industries
Company Size
51-200
Company Stage
Seed
Total Funding
$250K
Headquarters
Newark, Delaware
Founded
2024
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What believers are saying
- September 2026 Cyber Index with IBM broadened Artificial Analysis into enterprise security.
- AA-Video-T2V v2.0 and Search Index created fresh paid-use cases for buyers.
- Its April 2026 rebrand signals expansion into agents, hardware, music, and custom evaluations.
What critics are saying
- OpenAI and Google already cite Artificial Analysis, reducing its informational monopoly.
- Vendor labs can copy benchmark formats and crowd out third-party leaderboards by 2027.
- If buyers trust internal evals over public scores, Artificial Analysis becomes expendable.
What makes Artificial Analysis unique
- Artificial Analysis runs independent benchmarks across 400+ models and 50+ providers.
- Its October 2026 Cyber Index and Search Index extend evaluation beyond language models.
- Optima tests models on customer data, cost, and task time, not vendor claims.
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48%Artificial Analysis' Decision Index shows Jev beats Claude Haiku 5.5 at 11x speed. A new leaderboard pits purpose-built decision models against LLMs on classification, tool calling, knowledge, and reasoning, with cost and latency side by side. · 3 hrs ago Read 4 min * Artificial Analysis launched the Decision Index v1.0, benchmarking decision models on classification, tool calling, knowledge, and reasoning. * Jev 1.13 from TypeSafe leads at 58, ahead of GLiDE and Microsoft-Decision-1 (both 54). * Perplexity's Decider v1.1 27B is cheapest at $17 per 1M tasks; Celeris-1 Decision is fastest at 0.06s per task. * Claude Haiku 5.5 scores 61 but costs 4.4x more and runs 11x slower than Jev 1.13. * Decision models match LLMs on classification and tool calling but trail on knowledge and reasoning. * All tested decision models are overconfident, especially on knowledge and reasoning tasks. Decision models near LLM scores with lower latency and cost. Artificial Analysis has launched the Decision Index v1.0, a benchmark for models that choose among fixed options and assign each option a probability. Its leading decision model scores three points behind Claude Haiku 5.5 (Low), yet completes a task about 11 times faster and costs roughly 4.4 times less in the published comparison. Decision models accept a state plus one or more fixed-choice questions, then return a selected option and probabilities in a single API call. Their constrained output suits routing, classification, safety filtering, and tool selection. The probabilities also allow developers to test calibration, which measures whether confidence scores match observed accuracy. Four tests set the score. The Decision Index gives equal weight to four evaluations, with each contributing 25 percent of the composite score. The mix covers knowledge, reasoning, tool use, and classification so that one capability cannot dominate the result. * AA-Omniscience-Decision: Uses 500 private questions with four candidate answers, exactly one correct answer, and an "I don't know" option. Accuracy and non-hallucination rate contribute 12.5 percent each. * HLE-MultiChoice: Uses 513 text-only multiple-choice questions from Humanity's Last Exam. Answers are scored by exact letter match. * When2Call: Presents conversations in which an assistant can use tools. The model must answer directly, call a tool, request more information, or report that it cannot answer. * WildGuardTest: Asks up to three yes-or-no questions about harmful requests, refusals, and harmful replies. It uses average F1, a metric that balances precision and recall. Jev leads the specialists. Jev 1.13 from TypeSafe leads the dedicated decision models with a score of 58. GLiDE from Fastino and Microsoft-Decision-1 follow at 54. Perplexity's Decider v1.1 27B records the lowest reported cost at $17 per million tasks. Microsoft-Decision-1 costs $31 and Jev 1.13 costs $42 per million tasks. Artificial Analysis places all three on the capability-cost Pareto frontier, meaning no measured alternative is both cheaper and higher-scoring. Celeris-1 Decision posts the lowest reported latency at 0.06 seconds per task, or 60 milliseconds. Jev 1.13 takes 0.13 seconds. Claude Haiku 5.5 (Low) reaches 61 points, compared with Jev's 58, but requires 1.4 seconds per task and costs $184 per million tasks. Closest published comparison by Decision Index score | Model | Type | Index score | Time per task | Cost per 1M tasks | | Claude Haiku 5.5 (Low) | LLM | 61 | 1.4 s | $184 | | Jev 1.13 | Decision model | 58 | 0.13 s | $42 | The gap depends on the job. Leading decision models perform comparably to LLMs on classification and tool calling. LLMs retain a substantial advantage on broad knowledge and multi-step reasoning, with the clearest gaps appearing in AA-Omniscience-Decision and HLE-MultiChoice. That task-level split limits where a dedicated endpoint can replace an LLM call. Stable labels and narrow actions align with a decision model's fixed output space. Tasks that require synthesis, explanation, or reasoning across several steps remain better suited to an LLM. Confidence runs hot. Across almost every confidence bucket, the tested decision models achieve lower accuracy than their stated probabilities imply. A well-calibrated model that reports 80 percent confidence should be correct on roughly 80 percent of comparable predictions. These models fall short of that standard, especially on knowledge and reasoning tasks. WildGuardTest and When2Call show better calibration than the knowledge and reasoning evaluations. Production systems that route requests or trigger fallbacks at fixed confidence thresholds should therefore validate those thresholds on representative data and recalibrate the probabilities when necessary. Same task, different API contract. Each benchmark item contains a state, such as a conversation or text sample, followed by one or more questions. A question either names several possible choices or requests a yes-or-no decision. For binary questions, the benchmark selects "yes" when its probability reaches at least 0.5. Decision APIs return the chosen option and a probability for every option. LLMs receive equivalent task content through structured outputs. This setup compares the decisions produced by each system while preserving the interface each model is designed to use. A practical deployment split. * Use a decision model for high-volume routing, intent classification, safety checks, and tool-call gating when the option set is stable. * Keep an LLM for tasks that depend on broad knowledge, multi-step reasoning, synthesis, or generated explanations. * Validate confidence thresholds on production-like traffic before using probabilities to approve actions or invoke fallbacks. * Measure the full workload because label distribution, ambiguity, and domain language can shift accuracy, latency, and cost from benchmark results. Artificial Analysis publishes per-benchmark results with the index and documents task construction, scoring, and model handling in its methodology. Teams evaluating these endpoints can use the benchmark as a shortlist, then test the leading candidates against their own labels, traffic, and error costs.
Artificial Analysis launches AA-3D benchmark to rank AI models on interactive 3D scenes. The independent benchmarking firm is asking human judges to pick winners as AI models turn written briefs into working three.js experiences 6 hours ago Sponsored: Vera - AI-powered prediction market intelligence, built for serious analysts Explore Vera Artificial Analysis has a new way to test frontier AI models: ask them to build a 3D world, then let humans decide whose world is better. The independent AI benchmarking firm announced AA-3D on 12 October 2026. The benchmark measures how well models can generate interactive 3D scenes from written briefs, with results ranked by human judges using an Elo-style rating system. How AA-3D works. A model receives a text brief describing a scene. It then has to produce a functional three.js experience from that description. For the uninitiated, three.js is a JavaScript library used to render 3D graphics in a web browser. The model is not drawing a picture of a scene. It is writing the code that builds one. Those outputs are then run in an offline sandbox environment. A recruited panel casts blind pairwise preference votes. Judges see two outputs side by side, without knowing which model made which, and pick the one they prefer. Those head-to-head results feed an Elo-style ranking. AI, tech, and the markets they move - in one daily briefing. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. A familiar playbook from Artificial Analysis. AA-3D is not the firm's first experiment with human preference as a yardstick. The most recent example is AA-Video-T2V v2.0, which launched around 30 September 2026. That text-to-video benchmark drew more than 68,000 votes across 1,000 prompts. The new benchmark also stakes out territory that existing efforts do not fully cover. Academic and community projects such as T3Bench and Blender AI Arena already assess text-to-3D tasks. Their focus, however, is broader text-to-3D generation and static 3D assets. AA-3D narrows the lens to interactive three.js outputs. Why interactive 3D is a tougher test. Generating a convincing 3D model is already a demanding task. Generating one that actually works as interactive software raises the bar in several ways at once. The model has to understand the brief, translate that brief into code that runs without breaking, and produce a result that looks good and feels coherent once a human starts interacting with it. That combination makes AA-3D something of a hybrid exam. It blends coding ability, spatial reasoning and visual judgment into a single score. Traditional coding benchmarks often check whether code passes tests. This one checks whether humans actually like what the code produces. What this means for AI developers and users. The human-preference format matters here. Automated metrics can sometimes be gamed by tuning a model toward whatever the scoring system rewards. Blind pairwise voting is harder to optimize against, because the target is subjective human judgment rather than a fixed formula. That does not make the approach flawless. Preference votes reflect the tastes of whoever sits on the panel. How the panel is assembled could influence which models rise to the top. As of the announcement, there are no publicly available details on the methodology, prompt set size, or results for AA-3D. Disclosure: This article was edited by Diego Almada Lopez. For more information on how Cryptobriefing create and review content, see its Editorial Policy.
Artificial Analysis' Music Arena v1.1 crowns Suno v6 across 1,000 harder prompts. Artificial Analysis rebuilt its Music Arena with 1,000 new multi-genre prompts and 37,000 blind panel votes, shaking up how text-to-song models are ranked. · 4 hrs ago Read 5 min * AA-Music v1.1 launches with 1,000 new prompts across 17 genres and 37,000 blind panel votes. * Suno v6 ranks #1 on both vocal (Elo 1142) and instrumental (Elo 1140) leaderboards. * Suno v6-mini takes #2 on both boards, giving Suno the top two slots everywhere. * Mureka V9.5 reaches #3 on vocals with a 51-Elo jump over Mureka V9. * MiniMax Music 3.0 is the top open-weights model, anchored at Elo 1000. * Public arena votes no longer count; only recruited blind panel ratings feed Elo. Music Arena v1.1 adds 1,000 prompts and panel-only scoring. Artificial Analysis has released v1.1 of its two Music Arena benchmarks, introducing a more demanding prompt set and restricting ranked votes to recruited evaluators. The changes aim to distinguish leading text-to-music systems such as Suno v6, Lyria 3.5 and Mureka V9.5, whose earlier scores clustered near the top. Detailed briefs test composition, performance and mix. The Music Arena now uses 1,000 newly written prompts, divided evenly between vocal and instrumental tests. The set covers 17 genres, from classical, jazz and blues to EDM, metal and reggae. Prompts specify song structure, mood, instrumentation, production and vocal style. One sample vocal prompt requests tech house with a sassy, half-spoken female vocal, rubbery wobble bass, swung hi-hats and a dry club mix. Fulfilling that brief requires the model to coordinate genre conventions, arrangement, performance and mix character in one generation. Ranked Elo scores now use only blind preferences from a recruited evaluator panel. Public arena votes no longer affect the leaderboard, limiting the influence of brigading and casual voting. The launch snapshot includes more than 37,000 blind votes, with about 16,000 for vocals and 21,000 for instrumentals. Every ranked model has received more than 2,000 head-to-head comparisons. Suno takes both top slots. At launch, Suno v6 ranks first on both boards, followed by Suno v6-mini. Mureka V9.5 places third on the vocal standings, while four models share a statistically tight cluster below Suno on the instrumental benchmark. Top three models on each v1.1 board at launch | Board | Rank | Model | Elo | | Vocal | 1 | Suno v6 | 1142 | | Vocal | 2 | Suno v6-mini | 1116 | | Vocal | 3 | Mureka V9.5 | 1103 | | Instrumental | 1 | Suno v6 | 1140 | | Instrumental | 2 | Suno v6-mini | 1109 | | Instrumental | 3 | Mureka V9 | 1081 | Mureka V9.5 gains 51 vocal Elo points over Mureka V9, the largest generation-to-generation improvement reported for that family. On the instrumental board, Mureka V9 at 1081, Mureka V9.5 at 1080, Lyria 3 Pro at 1078 and Lyria 3.5 at 1076 are statistically tied from third through sixth, spanning only five Elo points. Signals below the leaders. * End-to-end vocals: The benchmark supplies no lyrics, so each model must write and perform them. * Open weights: MiniMax Music 3.0 is the highest-ranked open-weights entry, placing No. 11 for vocals and No. 13 for instrumentals. It serves as the 1000-point Elo anchor on both boards. * Close instrumental challenger: Stable Audio 3 Medium scores 998, within three points of MiniMax Music 3.0. * Earlier models: Google's Lyria 2 scores 932, below Lyria 3 Pro and Lyria 3.5. Meta's MusicGen records 881, the lowest reported score. One vote captures several qualities. Each comparison presents two tracks generated from the same prompt, and a panelist chooses a favorite without seeing the model names. Elo converts those wins and losses into relative ratings, with frequent winners receiving higher scores. An Elo value is neither a quality percentage nor an absolute measurement, and comparisons across benchmark versions or between the two boards have limited meaning. Because a panelist makes one preference decision, the score combines prompt adherence, composition, performance, sound quality and personal taste. The benchmark does not assign separate grades to those dimensions, so a track can win through strong production even when another follows the brief more precisely. Separate vocal and instrumental boards expose different capabilities. Instrumental generation emphasizes composition, arrangement and production, while the vocal task also requires lyric writing, pronunciation, singing style and placement of the voice in the mix. Those added demands explain why some models change position between the boards. Build a product test from the shortlist. Product teams building game soundtracks, advertising music, podcast beds or user-generated audio can use the boards to narrow their candidate set. A shipping decision still requires workload-specific testing because Elo omits latency, price, throughput, maximum duration, editing controls, stem export, consistency, safety policies and commercial terms. * Match the workload. Build an internal prompt set that reflects the genres, durations, vocal styles and production constraints your application will use. * Filter by deployment needs. Suno v6 leads this evaluation as a proprietary model. Teams evaluating open weights can begin with MiniMax Music 3.0 and include Stable Audio 3 Medium for instrumental work, subject to license and infrastructure requirements. * Run blind comparisons. Generate several tracks per prompt and randomize model labels. Repeated samples help expose consistency problems that a single output can hide. * Inspect narrow slices. Overall Elo averages performance across 17 genres. Listen to relevant samples because a model's strengths in EDM, classical music or vocal production may differ sharply. * Test detailed briefs. Include mood, structure, instrumentation, vocal delivery and mix characteristics so the evaluation reflects the controls a production system needs. * Review operational constraints. Confirm API access, rate limits, data handling, content policies, licensing and output rights before integration. The v1.1 launch data places Suno v6 first for both vocal and instrumental generation, identifies MiniMax Music 3.0 as the leading open-weights entry, and shows how little separates several other systems. For developers, the benchmark provides a sharper shortlist and a useful evaluation pattern: detailed prompts, blind pairwise review and separate tests for vocal and instrumental workloads.
Artificial Analysis rebuilds AA-Video to rank models by real-world jobs. Artificial Analysis rebuilt its text-to-video benchmark around 1080p playback, 1,000 tagged prompts, and 68,000 human votes across 10 use cases and 10 capabilities. · 17 hrs ago Read 5 min * Artificial Analysis launched AA-Video-T2V v2.0 and a silent variant, judging every model at 1080p. * 68,000+ blind human votes on 1,000 prompts tagged by use case, capability, and visual style. * Wan 3.0 ranked #1 at launch, leading 10 of 20 category boards at $12/min. * MiniMax H3 puts an open-weights model on the Pareto frontier at $4.80/min for 768p. * Gemini Omni Flash 1.1 dominates audio synchronization and UI/UX motion design categories. * Full methodology covers -18 LUFS audio leveling, 10 Mbps H.264, and per-session quality checks. AA-Video v2 ranks video models by the jobs they handle. Artificial Analysis has released AA-Video-T2V v2.0, a pair of human-preference benchmarks for generated video. The benchmarks add separate rankings for production use cases, technical capabilities, and visual styles, with standardized playback capped at 1080p. Developers can now compare models for tasks such as animation, camera control, text rendering, and lip-synced dialogue instead of relying on one aggregate score. One benchmark becomes two. The release separates evaluation into AA-Video-T2V v2.0, which scores text-to-video generation with audio, and AA-Video-T2V-Silent v2.0, which scores silent output. The audio benchmark contains 1,000 prompts, while the silent benchmark contains 500. Both prompt pools are refreshed regularly. Artificial Analysis retires prompts when they stop distinguishing among models or no longer reflect current prompting patterns. Each prompt carries tags across three dimensions: a production use case, a technical capability, and a visual style. Sampling is balanced across use-case and capability combinations. A single comparison can therefore contribute to several category rankings as well as the overall Elo score. What each score contains. | Dimension | Categories | | Use case | Marketing & Advertising; Retail & E-commerce; Live-Action Film; Animation & Gaming; Architecture & Real Estate; Productivity & Knowledge Work; UI/UX & Motion Design; Consumer; Social Media & Creator Content; and Frontier tasks that stretch current model capabilities. | | Capability | Physics; Complex Composition; Human Anatomy; Lighting & Materials; Multi-Scene & Narrative; Camera Control; Text Rendering; Spatio-Temporal Consistency; Audio Synchronization; and Dialogue & Lip Sync. | | Style | Visual treatments such as Cartoon & Anime. | The overall Elo summarizes performance across the full prompt mix, rewarding models that handle varied tasks consistently. Category boards reveal specialists whose performance may be obscured by an aggregate ranking. Elo is a relative rating derived from head-to-head results. A model gains points when evaluators prefer its output over another model's response to the same prompt. Ratings can move as votes accumulate, and models with similar scores may be statistically tied. Playback rules remove easy advantages. Evaluators watch two clips generated from the same prompt without seeing the model names. Both clips play at full size, and voting remains disabled until each has played for at least eight seconds. * Resolution: Outputs retain their generated resolution up to 1080p. Higher-resolution clips are downscaled to 1080p, while lower-resolution clips are never upscaled. * Encoding: Videos use H.264 with bitrate peaks capped at 10 Mbps. * Audio: Clips are adjusted toward -18 LUFS using one fixed gain, limiting any advantage from higher playback volume. * Quality control: Roughly one in five matchups has a strong consensus answer. Artificial Analysis discards an entire evaluation session when it fails those checks. Different boards produce different leaders. The launch analysis ranked Wan 3.0 first overall and credited it with leading 10 of 20 category boards. The results also showed substantial specialization among the highest-ranked models. | Model | Launch result | Reported price | | Wan 3.0 | First overall; led categories including Cartoon & Anime and Animation & Gaming | $12 per generated minute | | Dreamina Seedance 2.5 | Second overall; led Human Anatomy and Dialogue & Lip Sync | $34.12 per generated minute | | MiniMax H3, 768p | Statistically tied for third | $4.80 per generated minute | | Gemini Omni Flash 1.1 | Fifth overall; led UI/UX & Motion Design and Audio Synchronization | Not stated | A later live-board snapshot placed Gemini Omni Flash first in text-to-video with audio at 1233 Elo, followed by Wan 3.0 at 1229 and a Fal-tuned MiniMax H3 Max at 1227. MiniMax H3 was the highest-rated open-weights model at 1220. These values are snapshots rather than fixed results because new votes continually change the rankings. Text Rendering produced the widest score spread in the reported results. MiniMax H3 led the category, with a gap of more than 100 Elo points to Kling 3.0. Even highly rated outputs showed text degrading when the printed surface flipped or rotated. Camera Control produced a tighter cluster because many models struggled with pans, dollies, orbits, and zooms. Wan 3.0 led that category by 45 Elo points, making it the clearest outlier among otherwise similar scores. Native audio also changed model preferences. In comparisons that scored the same videos with and without sound, Gemini Omni Flash 1.1 gained 55 Elo points when audio was included, Seedance 2.5 gained 44, and FLUX 3 gained 38. Those gains are relevant to workflows involving ambience, sound effects, or synchronized speech. Cost changes the model choice. The launch data reported a statistical tie among four frontier live-action models whose prices ranged from $4.80 to $34.12 per generated minute, a roughly 7.1-fold difference. Category scores and pricing can therefore produce a different shortlist from the overall ranking. * Animation and gaming: Wan 3.0 led the corresponding use-case board. * Human performance and dialogue: Seedance 2.5 led Human Anatomy and Dialogue & Lip Sync. * UI motion and synchronized audio: Gemini Omni Flash 1.1 performed well above its aggregate score in those categories. * Open-weights deployment: MiniMax H3 was the highest-rated open model in the later snapshot, though the cited variant generated at 768p. * Camera-heavy sequences: Wan 3.0 held the largest reported lead in Camera Control. Benchmark scores cover human preference under controlled playback. Production evaluations still need representative prompts and measurements for latency, failure rate, effective cost, resolution, API limits, reproducibility, licensing, moderation, and available generation controls. Current scores and category boards are available on the live leaderboard. Artificial Analysis documents prompt handling, playback, voting, and quality controls in its full methodology.
Artificial Analysis launches Cyber Index to benchmark AI models on enterprise vulnerability defense. The new index evaluates how well AI systems can discover, validate, and patch security vulnerabilities using three specialized benchmarks developed with industry partners. 4 hours ago Sponsored: CryptoSlots - Cryptoslots Play now! Artificial Analysis, the independent AI benchmarking platform, is expanding into cybersecurity. The organization launched its Cyber Index (v1) on September 25, evaluating how effectively AI models perform the work of finding and fixing vulnerabilities in enterprise systems. Three benchmarks, one composite score. The Cyber Index is built on a composite of three distinct benchmarks, each contributed by industry partners: CWE-Bench-AA, DeepsecBench-AA, and CyberGym-E2E-AA. Together, they cover the full lifecycle of enterprise vulnerability management, from discovery through validation to patching. CWE-Bench-AA appears oriented around Common Weakness Enumeration categories, the standardized classification system that catalogs software and hardware vulnerability types. DeepsecBench-AA focuses on deeper security analysis tasks. CyberGym-E2E-AA rounds out the trio with end-to-end evaluation scenarios, testing whether AI agents can navigate realistic attack surfaces from start to finish. Defense, not offense. One deliberate design choice stands out: the Cyber Index focuses squarely on defensive cybersecurity. The index evaluates models on their ability to help businesses preemptively manage vulnerabilities, not on their capacity to break into systems. AI, tech, and the markets they move - in one daily briefing. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. Building on a benchmarking empire. The Cyber Index is not Artificial Analysis's first foray into specialized domain evaluations. The platform, co-founded by Micah Hill-Smith, has previously launched Capability Indices covering finance, legal, healthcare, and other sectors. Its general-purpose Intelligence Index has reached version 4.3.2, aggregating 10 separate evaluations across hundreds of AI models used by major labs. Disclosure: This article was edited by Diego Almada Lopez. For more information on how Crypto Briefing create and review content, see its Editorial Policy.
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About Artificial Analysis
Industries
Company Size
51-200
Company Stage
Seed
Total Funding
$250K
Headquarters
Newark, Delaware
Founded
2024
Artificial Analysis is Hiring for 18 Jobs on Simplify!
Find jobs on Simplify and start your career today