{"data":{"schema_version":"model_decision_evidence.v1","snapshot_date":"2026-09-25","model":{"model":"gpt-6-sol","direct_answer":"GPT-6 Sol is OpenAI's closed-weight model for complex coding and agentic work, released on 2026-09-22 as a lower-cost alternative to GPT-6 Astra, with a 1,050,000-token context window (922,000 input), 128,000 output tokens and text and image input. Reviewed tests consistently find it much faster and cheaper than Claude Opus 5.5 and GPT-6 Astra; on quality, both coding leaderboards place it below Opus 5.5 and several one-prompt comparisons preferred Opus's results, while a lava-lamp build and a $20-plan comparison favoured Sol.","verified_at":"2026-09-24","model_card":{"context_window":"1,050,000 tokens (up to 922,000 input)","max_output":"128,000 tokens","modalities":"Text and image input; text output","parameter_count":"Not disclosed"},"official_sources":[{"title":"GPT-6 Sol model documentation","publisher":"OpenAI","url":"https://developers.openai.com/api/docs/models/gpt-6-sol","retrieved_at":"2026-09-24"},{"title":"OpenAI API changelog","publisher":"OpenAI","url":"https://developers.openai.com/api/docs/changelog","retrieved_at":"2026-09-24"},{"title":"GPT-6 Astra System Card (appendix on GPT-6 Sol and GPT-6 Luna)","publisher":"OpenAI","url":"https://deploymentsafety.openai.com/gpt-6-astra","retrieved_at":"2026-09-24"}],"open_weights":{"status":"not_available","summary":"OpenAI publishes no GPT-6 Sol weights. The model is served through the OpenAI API (Responses, Chat Completions and Batch) and on Amazon Bedrock in us-east-1.","repository":"","licence":"Proprietary hosted model","hardware":"Not applicable","recommended_runtime":"ChinaAPI or the OpenAI API","source_urls":["https://developers.openai.com/api/docs/models/gpt-6-sol","https://developers.openai.com/api/docs/guides/amazon-bedrock"],"verified_at":"2026-09-24","agent_recipe":""},"curated_comparisons":["claude-opus-5-5","gpt-6-astra","gpt-6-luna"],"open_weight_alternatives":["mimo-v2.6-pro","deepseek-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":"GPT-6 Sol took 10 minutes and built three planets, using 1% of the $200 plan's usage; Claude Opus 5.5 took 26 minutes for a fuller, explorable solar system with more detail, using 16% of the $20 plan's usage. The author found the planets surprisingly similar: Sol faster, Opus bigger and more detailed.","limitation":"One prompt judged by the author; the usage shares are meters of two different subscription plans, not API costs.","metrics":[{"label":"GPT-6 Sol time","value":"10 min"},{"label":"Claude Opus 5.5 time","value":"26 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":"GPT-6 Sol was the cheapest and fastest of the four at $3.90 and 5 minutes, but the author found its result a bit disappointing; they preferred Claude Opus 5.5 ($8.95, 10 minutes) and rated 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":"GPT-6 Sol","value":"$3.90 · 5 min"},{"label":"Claude Opus 5.5","value":"$8.95 · 10 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":"GPT-6 Sol on Codex Plus used 9% of the weekly limit (29 million tokens) over 69 minutes and scored 7/10, the highest of the three plans, against 5/10 for Opus 5.5 and 6/10 for Grok 4.7; the author still recommends Claude at $20 because it yields far more tokens, and puts Codex Plus at about $330 of API usage a month.","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":"7/10"},{"label":"Working time","value":"69 min"},{"label":"Tokens","value":"29M"}]},{"id":"x-xueyu-svg-quota-2026-09","platform":"X","evidence_type":"task_test","title":"GPT-6 Astra, Sol and Luna on one SVG animation prompt","url":"https://x.com/xueyu1125/status/2102692499710193960","published_at":"2026-09-23","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"雪瑜","handle":"@xueyu1125","profile_url":"https://x.com/xueyu1125","description":"Programmer and AI-tools builder on X who posted the timings and quota readings"},"task":"Generate an SVG animation of a pelican riding a bicycle, shown in H5","method":"Same prompt on GPT-6 Astra, GPT-6 Sol, GPT-6 Luna and GPT-5.6 Sol; the author recorded time taken and the share of a five-hour subscription quota each used.","finding":"GPT-6 Sol took 4 min 42 s and used 2% of the five-hour quota, against 6 min 3 s and 16% for GPT-6 Astra; the author puts Sol's quota use at an eighth of Astra's and recommends Sol when quota matters.","limitation":"One prompt; the quota shares are a subscription meter, not API cost, and the post gives no quality score for Sol.","metrics":[{"label":"Time","value":"4 min 42 s"},{"label":"Five-hour quota used","value":"2%"}]},{"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":"GPT-6 Sol cost $0.08 and took 50 seconds, against $1.27 and 9 min 35 s for Claude Opus 5.5 and $0.63 and 5 min 4 s for GPT-6 Astra; in replies the account agreed Opus had a rough showing on this test.","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":"GPT-6 Sol","value":"$0.08 · 50 s"},{"label":"Claude Opus 5.5","value":"$1.27 · 9m 35s"}]},{"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":"GPT-6 Sol cost $0.08 and took 1 min 33 s, an eighth of Claude Opus 5.5's cost and four times faster, but the account judged Opus 5.5 'a significant leap for design. It's not close.'","limitation":"One test judged by the account; the task is shown only in the attached video.","metrics":[{"label":"GPT-6 Sol","value":"$0.08 · 1m 33s"},{"label":"Claude Opus 5.5","value":"$0.69 · 6m 8s"}]},{"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":"GPT-6 Sol took 11.4 minutes and $1.09 against Claude Opus 5.5's 12.7 minutes and $2.86; the author called the gap 'not even close' and agreed with a reply 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":"GPT-6 Sol","value":"11.4 min · $1.09"},{"label":"Claude Opus 5.5","value":"12.7 min · $2.86"}]},{"id":"x-dimentary-robot-drawing-2026-09","platform":"X","evidence_type":"task_test","title":"GPT-6 Sol and GPT-6 Astra on a robot drawing benchmark","url":"https://x.com/dimentary/status/2102539444834402314","published_at":"2026-09-22","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Dmytro Hrybov","handle":"@dimentary","profile_url":"https://x.com/dimentary","description":"ML research engineer working on physical learning and evaluations"},"task":"The author's robot-drawing-on-the-board benchmark","method":"The same benchmark on both models; the author compared output tokens, drawing speed and running cost.","finding":"GPT-6 Sol used about 25k output tokens against Astra's 22k and its controller drew twice as fast, at almost a fifth of the running cost; the author had expected stronger spatial reasoning from it.","limitation":"One run of a benchmark the post describes only by name, with no score.","metrics":[{"label":"Output tokens","value":"~25k vs ~22k (Astra)"},{"label":"Running cost","value":"Almost 5× cheaper"}]},{"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":"GPT-6 Sol scored 52.42 at high reasoning ($0.31 and 5 min 18 s per run in Codex CLI) and 50.07 at medium, 8th and 16th of 47 rows; Claude Opus 5.5 led with 57.83 and GPT-6 Astra at medium scored 56.03.","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":"52.42"},{"label":"Rank","value":"8 of 47"},{"label":"Cost per run","value":"$0.31"}]},{"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":"GPT-6 Sol scored 44/60 on version 2.2 in Pi: full marks on the constrained sentences, but SVG animations with reversed wheels and an arrow that broke physics (6 each), a space shooter that only moved sideways, and an English interface for a Chinese Trello brief; Claude Opus 5.5 scored 55 on the same version.","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":"44 / 60 (v2.2, Pi)"}]}]},"ranking":{"name":"gpt-6-sol","display_name":"GPT-6 Sol","vendor":"OpenAI","origin":"intl","category":"text","release_date":"2026-09-22","on_chinaapi":true,"sources":[{"leaderboard":"lmarena_text","metric":"Arena score (Elo)","value":"1457","entry":"gpt-6-sol-max","url":"https://arena.ai/leaderboard/text","retrieved_at":"2026-10-05","interval":"±7","samples":8787},{"leaderboard":"lmarena_text_chinese","metric":"Arena score (Elo)","value":"1477","entry":"gpt-6-sol-max","url":"https://arena.ai/leaderboard/text/chinese","retrieved_at":"2026-10-05","interval":"±23","samples":696},{"leaderboard":"lmarena_text_coding","metric":"Arena score (Elo)","value":"1520","entry":"gpt-6-sol-max","url":"https://arena.ai/leaderboard/text/coding","retrieved_at":"2026-10-05","interval":"±13","samples":2071},{"leaderboard":"aa_index","metric":"Intelligence Index","value":"48","entry":"GPT-6 Sol (max)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"},{"leaderboard":"aa_speed","metric":"Median output tokens/s","value":"104","entry":"GPT-6 Sol (max)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"},{"leaderboard":"aa_gdpval","metric":"Agentic Real-World Work Tasks, (Elo-500)/2000 (%)","value":"49","entry":"GPT-6 Sol (max)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"},{"leaderboard":"aa_tbench_v40","metric":"Agentic Coding \u0026 Terminal Use (%)","value":"44","entry":"GPT-6 Sol (max)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"}],"notes":"Released 2026-09-22 per OpenAI's announcement (Introducing GPT-6 Sol and Luna) and the API changelog entry of the same day. 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. The exact build is absent from the Vals Index v2.2 cohort read on 2026-10-23; its former v2.1 figure is no longer quoted."},"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}