{"data":{"schema_version":"model_decision_evidence.v1","snapshot_date":"2026-09-25","model":{"model":"claude-opus-5-5","direct_answer":"Claude Opus 5.5 is Anthropic's closed-weight model for long-running agentic coding and knowledge work, released on 2026-09-22 with a 1M-token context window, 128K output tokens and text and image input. It leads AI Coding Daily's coding leaderboard (57.83) and scored 55/60 on kzhu's version-2.2 test, and several one-prompt comparisons preferred its results, but it cost more than GPT-6 Sol in every comparison and had a rough showing in one lava-lamp build; one user's cache-heavy Claude Code history re-prices 44% below Opus 5 rates.","verified_at":"2026-09-24","model_card":{"context_window":"1M tokens","max_output":"128K tokens (up to 300K on the Message Batches API with a beta header)","modalities":"Text and image input; text output","parameter_count":"Not disclosed"},"official_sources":[{"title":"Introducing Claude Opus 5.5","publisher":"Anthropic","url":"https://www.anthropic.com/claude-opus-5-5","retrieved_at":"2026-09-24"},{"title":"Claude Opus 5.5 model documentation","publisher":"Anthropic","url":"https://platform.claude.com/docs/en/models/opus-5-5/overview","retrieved_at":"2026-09-24"},{"title":"System Card: Claude Opus 5.5","publisher":"Anthropic","url":"https://www.anthropic.com/claude-opus-5-5-system-card","retrieved_at":"2026-09-24"}],"open_weights":{"status":"not_available","summary":"Anthropic publishes no Claude Opus 5.5 weights. The model is available on the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS.","repository":"","licence":"Proprietary hosted model","hardware":"Not applicable","recommended_runtime":"ChinaAPI or the Claude API","source_urls":["https://platform.claude.com/docs/en/models/opus-5-5/overview","https://platform.claude.com/docs/en/models/opus-5-5/whats-new-opus-5-5"],"verified_at":"2026-09-24","agent_recipe":""},"curated_comparisons":["gpt-6-sol","gpt-6-astra"],"open_weight_alternatives":["deepseek-flash","glm-5.3-flash"],"reviews":[{"id":"x-kappaemme-planets-site-2026-09","platform":"X","evidence_type":"task_test","title":"Claude Opus 5.5 and GPT-6 Sol on one interactive website prompt","url":"https://x.com/Kappaemme1926/status/2102729710174196022","published_at":"2026-09-23","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Kappaemme","handle":"@Kappaemme1926","profile_url":"https://x.com/Kappaemme1926","description":"X user who posted this side-by-side test"},"task":"Build an interactive website about imaginary planets from a single prompt","method":"Same prompt with both models on High; the author reported wall-clock time, how much each result covered and the share of each subscription's usage.","finding":"Opus 5.5 took 26 minutes and built a fuller, explorable solar system with more detail, using 16% of the $20 plan's usage; GPT-6 Sol took 10 minutes for three planets and 1% of the $200 plan's usage.","limitation":"One prompt judged by the author; the usage shares are meters of two different subscription plans, not API costs.","metrics":[{"label":"Opus 5.5 time","value":"26 min"},{"label":"GPT-6 Sol time","value":"10 min"}]},{"id":"x-bhavy-eiffel-threejs-2026-09","platform":"X","evidence_type":"task_test","title":"Four models build the Eiffel Tower in Three.js","url":"https://x.com/Bhavani_00007/status/2102795814355763277","published_at":"2026-09-23","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Bhavy","handle":"@Bhavani_00007","profile_url":"https://x.com/Bhavani_00007","description":"X user who ran the same Three.js task on four models and reported cost and time"},"task":"Build the Eiffel Tower in Three.js","method":"Same task on Claude Opus 5.5, GPT-6 Astra, GPT-6 Sol and Kimi K3; the author reported cost and time for each and compared the results by eye.","finding":"Opus 5.5 cost $8.95 and took 10 minutes; the author preferred its frontend and 3D detailing to GPT-6 Astra's ($7.45, 8 minutes), found GPT-6 Sol ($3.90, 5 minutes) disappointing and Kimi K3 ($4.04, 6 minutes) strong for the price.","limitation":"One task judged by eye by the author; the post does not say how the models were run or how the costs were counted.","metrics":[{"label":"Opus 5.5","value":"$8.95 · 10 min"},{"label":"GPT-6 Astra","value":"$7.45 · 8 min"}]},{"id":"x-shownotover-20-plans-2026-09","platform":"X","evidence_type":"community_experience","title":"Claude Pro, Codex Plus and SuperGrok on the same work at maximum settings","url":"https://x.com/shownotover/status/2102950408465338855","published_at":"2026-09-24","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"shownotover","handle":"@shownotover","profile_url":"https://x.com/shownotover","description":"X user comparing $20 AI subscriptions in their first video"},"task":"One piece of work run on each $20 subscription; the task itself is shown only in the author's video","method":"Each plan's top model at maximum reasoning (Grok 4.7 at xhigh); the author recorded the share of the weekly limit, tokens, working time and a 1–10 quality score.","finding":"Opus 5.5 used 10% of the weekly limit (115 million tokens) over 2 hours and scored 5/10, behind GPT-6 Sol at 7/10 (29 million tokens, 69 minutes) and Grok 4.7 at 6/10; the author still recommends Claude at $20 for its usage allowance.","limitation":"The post does not describe the task, the quality score is the author's own, and the numbers are subscription meters rather than API usage.","metrics":[{"label":"Quality (author)","value":"5/10"},{"label":"Working time","value":"2 h"},{"label":"Tokens","value":"115M"}]},{"id":"reddit-claudecode-cache-repricing-2026-09","platform":"Reddit","evidence_type":"community_experience","title":"Eight weeks of Claude Code usage re-priced at Opus 5 and Opus 5.5 rates","url":"https://www.reddit.com/r/ClaudeCode/comments/1wom776/opus_55_reads_the_price_page_as_20_cheaper_on_8/","published_at":"2026-09-23","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Drasezv","handle":"u/Drasezv","profile_url":"https://www.reddit.com/user/Drasezv/","description":"r/ClaudeCode member who built the transcript-reading tool the analysis comes from"},"task":"Price every request from eight weeks of the author's own Claude Code transcripts at Opus 5 and Opus 5.5 list prices","method":"Requests deduplicated by requestId and priced per row, with cache reads, 1h and 5m cache writes, output and plain input counted separately.","finding":"97% of the tokens were cache reads, so the same usage came to $2,849 at Opus 5.5 rates against $5,071 at Opus 5 rates — 44% less, rather than the 20% the headline input and output prices suggest.","limitation":"It re-prices the same token counts and does not measure whether Opus 5.5 uses more or fewer tokens on the same work; list prices, not subscription billing; the author built the tool that read the transcripts.","metrics":[{"label":"Cache reads","value":"97% of tokens"},{"label":"At Opus 5 rates","value":"$5,071"},{"label":"At Opus 5.5 rates","value":"$2,849"}]},{"id":"x-bridgebench-lava-lamp-2026-09","platform":"X","evidence_type":"task_test","title":"Four models build a lava lamp from one prompt","url":"https://x.com/bridgebench/status/2102770788319310302","published_at":"2026-09-23","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Bridgebench","handle":"@bridgebench","profile_url":"https://x.com/bridgebench","description":"BridgeMind's benchmark account, which calls itself the official AI benchmark of the vibe coding movement"},"task":"Build a lava lamp from a single prompt","method":"Same prompt on Claude Opus 5.5, GPT-6 Astra, GPT-6 Sol and GPT-6 Luna; the account reported cost and time, asked readers which did best and commented on their answers in replies.","finding":"Claude Opus 5.5 was the slowest and most expensive at $1.27 and 9 min 35 s, and in replies the account agreed it had a rough showing, answering 'Not on this test' to a claim that Opus beat Sol.","limitation":"One prompt with the quality verdict left mostly to readers; the account is BridgeMind's own benchmark and does not say how the models were accessed.","metrics":[{"label":"Claude Opus 5.5","value":"$1.27 · 9m 35s"},{"label":"GPT-6 Sol","value":"$0.08 · 50 s"}]},{"id":"x-bridgebench-black-hole-2026-09","platform":"X","evidence_type":"task_test","title":"Claude Opus 5.5 and GPT-6 Sol on a black hole merger test","url":"https://x.com/bridgebench/status/2103091707021848921","published_at":"2026-09-24","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Bridgebench","handle":"@bridgebench","profile_url":"https://x.com/bridgebench","description":"BridgeMind's benchmark account, which calls itself the official AI benchmark of the vibe coding movement"},"task":"The account's black hole merger visualisation test","method":"The same test on both models; the account reported cost and time and judged the design.","finding":"Claude Opus 5.5 cost $0.69 and took 6 min 8 s; the account called it 'a significant leap for design', while GPT-6 Sol did the test for $0.08 in 1 min 33 s.","limitation":"One test judged by the account; the task is shown only in the attached video.","metrics":[{"label":"Claude Opus 5.5","value":"$0.69 · 6m 8s"},{"label":"GPT-6 Sol","value":"$0.08 · 1m 33s"}]},{"id":"x-hunterbohm-motion-graphic-2026-09","platform":"X","evidence_type":"task_test","title":"Claude Opus 5.5 and GPT-6 Sol on one motion-graphic prompt","url":"https://x.com/TheHunterBohm/status/2102724864205566388","published_at":"2026-09-23","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Hunter Bohm","handle":"@TheHunterBohm","profile_url":"https://x.com/TheHunterBohm","description":"Builds agent systems for business and posted a longer comparison video"},"task":"Make a 20-second motion graphic explaining how a language model turns a prompt into an answer and why long answers cost more","method":"Same prompt on both models; the author reported time and cost and posted the prompt and a full comparison video in replies.","finding":"Claude Opus 5.5 took 12.7 minutes and $2.86 against GPT-6 Sol's 11.4 minutes and $1.09; the author called the gap 'not even close' and, in replies, agreed that Sol's result was far worse.","limitation":"One subjective prompt; the verdict comes from the author's post and replies rather than a score.","metrics":[{"label":"Claude Opus 5.5","value":"12.7 min · $2.86"},{"label":"GPT-6 Sol","value":"11.4 min · $1.09"}]},{"id":"aicodingdaily-llm-coding-leaderboard-2026-09","platform":"Independent benchmark","evidence_type":"independent_suite","title":"AI Coding Daily LLM Coding Leaderboard","url":"https://aicodingdaily.com/leaderboard","published_at":"2026-09-24","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"AI Coding Daily","handle":"@AICodingDaily","profile_url":"https://www.youtube.com/@AICodingDaily","description":"Coding-model testing channel and website that runs its own leaderboard and sells premium tutorials and channel advertising"},"task":"Implement and harden production-shaped projects in several stacks, including a Laravel app, a Go service and a React/TypeScript app, from the same prompts","method":"Each model and reasoning setting is a separate row run in a coding harness, with the average cost and time per run. Code quality is graded by a judge model against a published 100-point rubric and scaled to 20 points per project; behavioural reliability runs each of four projects five times and scores the checks every attempt fails.","finding":"Claude Opus 5.5 led the board at 57.83 on high reasoning ($0.79 and 3 min 10 s per run in Claude Code), with its medium setting second at 57.37.","limitation":"One author's benchmark whose code-quality half is graded by a judge model; rows run in different harnesses, the page is updated as models are added (read on 2026-09-24, 47 rows), and the author sells premium tutorials and advertising.","metrics":[{"label":"Points (high)","value":"57.83"},{"label":"Rank","value":"1 of 47"},{"label":"Cost per run","value":"$0.79"}]},{"id":"techgogogo-kzhu-llm-benchmark-2026-09","platform":"Independent benchmark","evidence_type":"independent_suite","title":"kzhu's six-question LLM test leaderboard","url":"https://www.techgogogo.com/llm-benchmark/","published_at":"2026-09-24","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"AI产品狙击手 kzhu","handle":"@kevinzhu9305","profile_url":"https://www.youtube.com/@kevinzhu9305","description":"Chinese YouTube reviewer who scores models on a six-question test and publishes every score with per-question notes"},"task":"Six questions scored out of 10: constrained sentence writing, two reasoning puzzles (replaced by pelican and compound-bow SVG animations in version 2.2), a browser OS with a space shooter, a 3D racing game and a Trello-style board","method":"Each row names the harness the model ran in (Pi, Claude Code, WorkBuddy and others) and the date; the author scores every question with a note, and warns that totals are only comparable within one test version.","finding":"Claude Opus 5.5 scored 55/60 on version 2.2 in Pi (54 in the author's note), 9 or 10 on every question; the author called its pelican SVG the best implementation seen and found nothing to fault in the browser OS, racing game or Trello board.","limitation":"One author's scores on six questions; rows use different harnesses and test versions (2.1 or 2.2), and a few rows' table total differs by one point from the author's note.","metrics":[{"label":"Score","value":"55 / 60 (v2.2, Pi)"}]}]},"ranking":{"name":"claude-opus-5-5","display_name":"Claude Opus 5.5","vendor":"Anthropic","origin":"intl","category":"text","release_date":"2026-09-22","on_chinaapi":true,"sources":[{"leaderboard":"lmarena_text","metric":"Arena score (Elo)","value":"1504","entry":"claude-opus-5.5-high","url":"https://arena.ai/leaderboard/text","retrieved_at":"2026-10-05","interval":"±9","samples":4552},{"leaderboard":"lmarena_text_chinese","metric":"Arena score (Elo)","value":"1571","entry":"claude-opus-5.5-high","url":"https://arena.ai/leaderboard/text/chinese","retrieved_at":"2026-10-05","interval":"±29","samples":402},{"leaderboard":"lmarena_text_coding","metric":"Arena score (Elo)","value":"1538","entry":"claude-opus-5.5-high","url":"https://arena.ai/leaderboard/text/coding","retrieved_at":"2026-10-05","interval":"±18","samples":1060},{"leaderboard":"aa_index","metric":"Intelligence Index","value":"58","entry":"Claude Opus 5.5 (max with fallback)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"},{"leaderboard":"epoch_eci","metric":"General ECI","value":"167","entry":"Claude Opus 5.5","url":"https://epoch.ai/eci","retrieved_at":"2026-10-04","interval":"(164 - 172)"},{"leaderboard":"aa_gdpval","metric":"Agentic Real-World Work Tasks, (Elo-500)/2000 (%)","value":"67","entry":"Claude Opus 5.5 (max with fallback)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"},{"leaderboard":"aa_tbench_v40","metric":"Agentic Coding \u0026 Terminal Use (%)","value":"60","entry":"Claude Opus 5.5 (max with fallback)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"},{"leaderboard":"vals_index","metric":"Accuracy (GDP-weighted finance, coding, legal and tax)","value":"70.18","entry":"Claude Opus 5.5","url":"https://www.vals.ai/benchmarks/vals_index","retrieved_at":"2026-10-23","interval":"±1.00"}],"notes":"Released 2026-09-22 per Anthropic's announcement (Introducing Claude Opus 5.5), the first model in the Claude 5.5 family. Artificial Analysis printed no output speed for its Claude Opus 5.5 (max with fallback) row on 2026-09-22 (the column reads --), so no speed figure is quoted; the speeds of its lower-effort rows are not substituted. LiveBench 1.0 scores were withdrawn on 2026-10-23 after the site moved them into its historical-release view and launched the incompatible LiveBench 2.0 categories."},"sources":{"aa_gdpval":{"id":"aa_gdpval","name":"Artificial Analysis · GDPval-AA v2.1","operator":"Artificial Analysis","metric":"Agentic Real-World Work Tasks, (Elo-500)/2000 (%)","url":"https://artificialanalysis.ai/leaderboards/models","edition":"GDPval-AA v2.1, live board, read 2026-09-22. 220 agentic task-completion tasks with file outputs, ranked pairwise by a panel of three frontier LLM judges; since v2.1 the Elo scale is anchored to DeepSeek V4.1 Flash (max) at 1600 and fitted with a Crowd-BT model, so its figures are not comparable with the v2 figures this page quoted from its 2026-09-12 read. The column prints clamp((Elo-500)/2000). It carries 10% of the Agents category inside Intelligence Index v4.3.2, so this column is a component of the AA Intelligence Index shown as an individual source, not an independent operator. Rows Artificial Analysis marks deprecated are read with the leaderboard's Status filter set to All (CA-948). The column is hidden until the leaderboard's Intelligence group is expanded","retrieved_at":"2026-09-22","scene":"agent_work"},"aa_image_edit":{"id":"aa_image_edit","name":"Artificial Analysis Editing","operator":"Artificial Analysis","metric":"Elo","url":"https://artificialanalysis.ai/image/leaderboard/editing","edition":"live board; exact row settings retained","retrieved_at":"2026-09-13","task":"image_editing"},"aa_image_t2i":{"id":"aa_image_t2i","name":"Artificial Analysis Text To Image","operator":"Artificial Analysis","metric":"Elo","url":"https://artificialanalysis.ai/image/leaderboard/text-to-image","edition":"live board; exact row settings retained","retrieved_at":"2026-09-13","task":"text_to_image"},"aa_index":{"id":"aa_index","name":"Artificial Analysis Intelligence Index","operator":"Artificial Analysis","metric":"Intelligence Index","url":"https://artificialanalysis.ai/leaderboards/models","edition":"Intelligence Index v4.3.2, read 2026-09-22; its figures are not comparable with the v4.3 figures this page quoted from its 2026-09-12 read. v4.3.1 refreshed the pairwise judge panels of AA-Briefcase and GDPval-AA, and v4.3.2 moved GDPval-AA to v2.1 (Elo scale anchored to DeepSeek V4.1 Flash (max) at 1600, fitted with a Crowd-BT model) and refitted AA-Briefcase v1.1 with Crowd-BT, so rows moved without any model changing. v4.3 had already swapped τ³-Banking out of the index for AutomationBench-AA and Terminal-Bench v2.1 for v4.0. The leaderboard's default Status: Current filter hides rows Artificial Analysis marks deprecated; they still render with Status set to All and are re-read from that view like any other row (CA-948). A trailing * is reproduced as the board prints it: the board's data marks that score as estimated (intelligenceIndexIsEstimated), i.e. not every evaluation in the index had been run for that model","retrieved_at":"2026-09-22"},"aa_mlcr":{"id":"aa_mlcr","name":"Artificial Analysis · MLCR-AA","operator":"Artificial Analysis","metric":"Medical Long Context Reasoning (%)","url":"https://artificialanalysis.ai/evaluations/mlcr-aa","edition":"MLCR-AA, live board, read 2026-09-22. Artificial Analysis marks it a standalone evaluation that is NOT part of Intelligence Index v4.3 — unlike the GDPval-AA v2 and Terminal-Bench v4.0 columns, this one is not a component of the AA Intelligence Index shown as an individual source, only the same operator. The underlying benchmark is Wisedocs' open MLCR (Wisedocs-AI/medical-long-context-reasoning): synthetic medical records of roughly 25,000-64,000 tokens, graded across six tiers from locating a single fact to expert-level clinical synthesis. It measures multi-document reasoning over long records, not medical capability in general. The main leaderboard carries no column for it; the figures were first read off the evaluation page's chart (2026-09-07) and since 2026-09-22 from the per-model pages' payload, which carries every model the chart can show (CA-956)","retrieved_at":"2026-09-22","scene":"medical"},"aa_speed":{"id":"aa_speed","name":"Artificial Analysis · Output Speed","operator":"Artificial Analysis","metric":"Median output tokens/s","url":"https://artificialanalysis.ai/leaderboards/models","edition":"live board; measured by Artificial Analysis against the vendor's own API, not through ChinaAPI. Rows Artificial Analysis marks deprecated are read with the leaderboard's Status filter set to All (CA-948)","retrieved_at":"2026-09-22","scene":"speed"},"aa_tbench_v40":{"id":"aa_tbench_v40","name":"Artificial Analysis · Terminal-Bench v4.0","operator":"Artificial Analysis","metric":"Agentic Coding \u0026 Terminal Use (%)","url":"https://artificialanalysis.ai/leaderboards/models","edition":"Terminal-Bench 4.0 (66 tasks), run by Artificial Analysis with the mini-swe-agent harness, pass@1 averaged over 3 repeats per task; read 2026-09-22. When this column replaced the benchmark's own board on 2026-09-07 the methodology page named the harness as mini-SWE-agent v2.4.6; on 2026-09-21 it and the evaluation page name mini-swe-agent without a version, and every figure quoted before then read back unchanged. One harness for every model, so the column compares models rather than model-plus-best-scaffold — which is why it replaced the benchmark's own board, whose rows are agent × model and covered one Chinese model out of 34 (CA-788, overturning CA-734). It carries 10% of the Coding category inside Intelligence Index v4.3.2, so this column is a component of the AA Intelligence Index shown as an individual source, not an independent operator. Rows Artificial Analysis marks deprecated are read with the leaderboard's Status filter set to All (CA-948). The column is hidden until the leaderboard's Intelligence group is expanded","retrieved_at":"2026-09-22","scene":"agentic"},"aa_video_i2v_audio":{"id":"aa_video_i2v_audio","name":"Artificial Analysis Image To Video · With Audio","operator":"Artificial Analysis","metric":"Elo","url":"https://artificialanalysis.ai/video/leaderboard/image-to-video","edition":"live board; with audio pool; exact row settings retained","retrieved_at":"2026-09-13","task":"image_to_video_audio"},"aa_video_t2v_audio":{"id":"aa_video_t2v_audio","name":"Artificial Analysis Text To Video · With Audio","operator":"Artificial Analysis","metric":"Elo","url":"https://artificialanalysis.ai/video/leaderboard/text-to-video","edition":"live board; with audio pool; exact row settings retained","retrieved_at":"2026-09-13","task":"text_to_video_audio"},"epoch_eci":{"id":"epoch_eci","name":"Epoch Capabilities Index (ECI)","operator":"Epoch AI","metric":"General ECI","url":"https://epoch.ai/eci","edition":"live board","licence":"CC BY 4.0, as labelled on the page (https://creativecommons.org/licenses/by/4.0/)","retrieved_at":"2026-10-04"},"lmarena_i2v":{"id":"lmarena_i2v","name":"LMArena Image-to-Video Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/image-to-video","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (image_to_video, category overall), published 2026-09-22, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","task":"image_to_video","updated_at":"2026-09-22"},"lmarena_image_edit":{"id":"lmarena_image_edit","name":"LMArena Image Edit Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/image-edit","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (image_edit, category overall), published 2026-09-30, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","task":"image_editing","updated_at":"2026-09-30"},"lmarena_t2i":{"id":"lmarena_t2i","name":"LMArena Text-to-Image Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text-to-image","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (text_to_image, category overall), published 2026-09-24, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","task":"text_to_image","updated_at":"2026-09-24"},"lmarena_t2v":{"id":"lmarena_t2v","name":"LMArena Text-to-Video Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text-to-video","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (text_to_video, category overall), published 2026-09-22, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","task":"text_to_video","updated_at":"2026-09-22"},"lmarena_text":{"id":"lmarena_text","name":"LMArena Text Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (text_style_control, category overall), published 2026-10-02, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","updated_at":"2026-10-02"},"lmarena_text_chinese":{"id":"lmarena_text_chinese","name":"LMArena Text Arena · Chinese","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text/chinese","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (text_style_control, category chinese, prompts in Chinese), published 2026-10-02, revision 46919c467f7f. 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Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","scene":"coding","updated_at":"2026-10-02"},"superclue":{"id":"superclue","name":"SuperCLUE 智能指数","operator":"SuperCLUE","metric":"总分 (overall)","url":"https://www.superclueai.com/homepage","edition":"2026-07 evaluation, published 2026-08-06. The homepage reads this edition from its data file (/data/generalboard/2026年7月.xlsx, sheet 总排行榜, last modified 2026-09-11), which also carries rows evaluated after publication, each with its own evaluation publication date (the latest 2026-09-11); the page's Latest Update line refers to a different sub-board","retrieved_at":"2026-10-04"},"vals_index":{"id":"vals_index","name":"Vals Index","operator":"Vals AI","metric":"Accuracy (GDP-weighted finance, coding, legal and tax)","url":"https://www.vals.ai/benchmarks/vals_index","edition":"V2.2, page updated 2026-10-22; complete current cohort read 2026-10-23. Finance Agent v2.1 replaces v2 in this index; figures are not comparable with the V2.1 snapshot. Missing exact builds are not carried forward.","retrieved_at":"2026-10-23"},"vendor_card":{"id":"vendor_card","name":"Vendor's own evaluation card","operator":"the model's own vendor","metric":"varies; the benchmark is named in each row","url":"","edition":"self-reported","self_reported":true,"retrieved_at":"2026-09-22"}}},"success":true}