{"data":{"schema_version":"model_decision_evidence.v1","snapshot_date":"2026-09-25","model":{"model":"mimo-v2.6-flash","direct_answer":"MiMo-V2.6-Flash is Xiaomi's MIT-licensed open-weight mixture-of-experts model (309B total and 15B active per the model card) with a 1M-token context window and text, image, video and audio input. Reviewed results are split: one-prompt builds rate its output highly at very low cost, but reviewers report long thinking times and stalls on longer agentic work, including an engineer's git task that went badly off course.","verified_at":"2026-09-24","model_card":{"context_window":"1M tokens (1,048,576)","max_output":"128K tokens (131,072, including reasoning tokens)","modalities":"Text, image, video and audio input; text output","parameter_count":"309B total, 15B active per the model card; the technical report states 310B total"},"official_sources":[{"title":"MiMo-V2.6: Scaling Up Reinforcement Learning for Self-Improvement","publisher":"Xiaomi MiMo","url":"https://mimo.mi.com/docs/en-US/news/latest/v2-6","retrieved_at":"2026-09-24"},{"title":"MiMo-V2.6-Flash model page","publisher":"Xiaomi MiMo","url":"https://mimo.mi.com/models/en-US/mimo-v2.6-flash","retrieved_at":"2026-09-24"},{"title":"MiMo-V2.6-Flash-RL model card","publisher":"Xiaomi MiMo","url":"https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL","retrieved_at":"2026-09-24"}],"open_weights":{"status":"available","summary":"Xiaomi published the MiMo-V2.6-Flash-RL weights, which need custom code, under the MIT License declared in the model card, with SGLang and vLLM launch instructions. Xiaomi's pages do not say in so many words that this checkpoint is the one behind the mimo-v2.6-flash API.","repository":"https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL","licence":"MIT (declared in the model card metadata)","hardware":"Not published by Xiaomi beyond the tensor-parallel launch settings in the model card; the Agent recipe performs a hardware preflight.","recommended_runtime":"SGLang or vLLM","source_urls":["https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL","https://mimo.mi.com/docs/en-US/news/latest/v2-6"],"verified_at":"2026-09-24","agent_recipe":"You are my deployment agent. Deploy the official XiaomiMiMo/MiMo-V2.6-Flash-RL weights as a private, OpenAI-compatible service on this host. Use the publisher's model card as the source of truth: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL.\n\nPreflight\n- Inspect the OS, GPU model and count, free VRAM, driver and CUDA versions, free disk space, and available ports. The model card's launch commands use tensor parallelism but state no minimum GPU; estimate whether the official weights and the requested context fit with safe headroom.\n- The repository ships custom model code that needs trust-remote-code. Pin the exact revision you download, list the Python files it would execute, and ask me to approve them before enabling trust-remote-code.\n- If the host cannot safely run the official weights, stop before installing or launching anything. Explain the limiting resource and propose a separately reviewed alternative; do not substitute a community quantization automatically.\n\nDeployment\n- If preflight passes and I have approved the remote code, create an isolated environment and follow the model card's SGLang or vLLM launch instructions, including its reasoning-parser and tool-call-parser settings. The model card warns that a stable vLLM release may lag behind; use the image or version it names rather than an untested one. Generate an API key and store it in a protected environment variable. Bind to 127.0.0.1 first; do not expose the endpoint publicly.\n- Keep the publisher's sampling defaults (temperature 1.0, top_p 0.95). Start with a context length the preflight supports; the model supports up to 1,048,576 tokens.\n\nVerification and handoff\n- Send a real request to /v1/chat/completions and confirm that the model responds. Report the exact launch command, installed versions, model revision, local endpoint, test result, measured GPU memory and log location. Identify any reverse-proxy or firewall changes that still need my approval. Never print the API key or other secrets."},"curated_comparisons":["deepseek-flash"],"open_weight_alternatives":[],"reviews":[{"id":"x-naymur-one-prompt-game-2026-09","platform":"X","evidence_type":"task_test","title":"MiMo-V2.6-Flash, DeepSeek V4.1 Flash and Grok 4.7 on one game prompt","url":"https://x.com/naymur_dev/status/2102456037286801665","published_at":"2026-09-22","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Naymur Rahman","handle":"@naymur_dev","profile_url":"https://x.com/naymur_dev","description":"Frontend developer whose X profile lists design-and-code work for Command Code"},"task":"Build a playable browser game from a single prompt with Command Code's /design command","method":"Same prompt for all three models; the author rated gameplay UX/UI out of 10 and reported the cost of each run.","finding":"MiMo-V2.6-Flash scored 9/10 for $0.005, the cheapest run, with a playable one-shot game. DeepSeek V4.1 Flash also scored 9/10 at $0.0089; Grok 4.7 scored 8/10 at $0.20 and needed iterations.","limitation":"One prompt rated by the author, whose profile lists work for Command Code, the tool that ran the test; there is no rubric and no repeated run.","metrics":[{"label":"Author rating","value":"9/10"},{"label":"Run cost","value":"$0.005"}]},{"id":"reddit-localllama-mimo-v26-2026-09","platform":"Reddit","evidence_type":"community_experience","title":"A senior engineer's security and git tasks with MiMo-V2.6 Pro and Flash","url":"https://www.reddit.com/r/LocalLLaMA/comments/1woa5d3/mimov26_both_pro_and_flash_is_a_benchmaxxed_scam/","published_at":"2026-09-23","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"crusaderky","handle":"u/crusaderky","profile_url":"https://www.reddit.com/user/crusaderky/","description":"r/LocalLLaMA member who presents the post as a senior software engineer's impressions"},"task":"Create a branch and cherry-pick one commit in a sandbox whose git worktree turned out to be corrupted","method":"Self-reported impression that the author says is not a benchmark; the same post gives MiMo-V2.6-Pro a sandbox security task and has GLM-5.3 review the result.","finding":"MiMo-V2.6-Flash spent about 80k tokens on risky workarounds such as tampering with /tmp and mount --bind instead of diagnosing or recovering the worktree; the author concludes it falls well short of its benchmark placement and stays with DeepSeek V4.1 Flash and Qwen3.8-Flash.","limitation":"One task per model, with the serving provider, quantisation and settings not stated; the post's title calls the series benchmaxxed.","metrics":[{"label":"Tokens spent on the detour","value":"~80k"}]},{"id":"x-wesche-koi-pond-2026-09","platform":"X","evidence_type":"task_test","title":"Three Flash models build the same koi pond locally","url":"https://x.com/WescheNex1q/status/2102527793057661386","published_at":"2026-09-22","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Wësche","handle":"@WescheNex1q","profile_url":"https://x.com/WescheNex1q","description":"Artist and AI enthusiast on X who builds and benchmarks open models on DGX Spark clusters"},"task":"A koi pond build, run as a local head-to-head","method":"The same task on GLM-5.3-Flash, MiMo-V2.6-Flash and DeepSeek V4.1 Flash, each on two DGX Sparks; the author reported time, tokens and fixes needed and left the visual verdict partly to readers.","finding":"All three finished cleanly with zero fixes; MiMo-V2.6-Flash took 30 minutes and 75.5K tokens, between DeepSeek V4.1 Flash (21 minutes, 53.3K) and GLM-5.3-Flash (59 minutes, 90.9K).","limitation":"One local run per model; the post states neither the prompt nor the quantisation or runtime used.","metrics":[{"label":"Time","value":"30 min"},{"label":"Tokens","value":"75.5K"}]},{"id":"youtube-lunchuizhe-mimo-flash-2026-09","platform":"YouTube","evidence_type":"task_test","title":"MiMo-V2.6-Flash on page and SVG tasks and a real app bug fix","url":"https://www.youtube.com/watch?v=LTHLzLotZsU","published_at":"2026-09-22","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"抡锤者","handle":"@抡锤者","profile_url":"https://www.youtube.com/@%E6%8A%A1%E9%94%A4%E8%80%85","description":"Chinese YouTube channel that tests models on real tasks and posts full transcripts on its own forum"},"task":"Build pages explaining Chinese bagua culture and how a thorium molten-salt reactor works, draw Schrödinger's cat as an SVG, and fix a real bug in the author's app","method":"Run through OpenRouter, both inside the DeepSeek harness and in direct chat; the author scored the outputs out of 100 and compared speed with earlier DeepSeek V4.1 Flash runs of the same tasks. The full transcript is at https://lcz.me/topic/1885.","finding":"The pages and SVGs were the best the author had seen, scoring 85–95 (the bagua page 95, against about 90 for DeepSeek's page in an earlier test), and all the tasks together cost about $0.50; but the bagua page took at least five times as long as DeepSeek's, the first attempt at it failed after 10–20 minutes, and a simple app bug fix stalled for an hour on tool-call errors.","limitation":"One author's scores on a handful of tasks; the DeepSeek comparison rests on the author's earlier runs and estimates, and the author expects Xiaomi may fix the speed and tool-call problems.","metrics":[{"label":"Bagua page (author)","value":"95/100"},{"label":"App bug fix","value":"Stalled after 1 hour"},{"label":"Total cost","value":"About $0.50"}]},{"id":"x-simonas-mimo-flash-game-2026-09","platform":"X","evidence_type":"task_test","title":"A MiMo-V2.6-Flash game build that needed steering","url":"https://x.com/SimonasLTU1/status/2102304299803889801","published_at":"2026-09-22","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Simonas","handle":"@SimonasLTU1","profile_url":"https://x.com/SimonasLTU1","description":"X user who builds in public and posts hands-on model tests"},"task":"A browser game build the author meant to run zero-shot","method":"The author ran the task in their own coding setup (OpenCode or ZCode, per a reply), steered the model when it stalled, and reported thinking time, cost and a personal score.","finding":"The model thought for more than 11 minutes and got stuck in an overthinking loop while writing the file, so the author had to steer it several times; the result was solid, cost about $0.05, and scored 9.5/10 from the author, who said it did not feel like a Flash model for speed.","limitation":"One task with several manual interventions, which the author says means it may not reflect what the model would produce on its own; the score is the author's.","metrics":[{"label":"Thinking time","value":"Over 11 min"},{"label":"Cost","value":"About $0.05"},{"label":"Score (author)","value":"9.5/10"}]},{"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":"MiMo-V2.6-Flash scored 40/60 on version 2.2 in Pi, well above the Pro model's 28 in the same batch: only six of ten constrained sentences were right, but its compound bow was the most realistic the author had seen and its 3D racing game was rated top-notch (8).","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":"40 / 60 (v2.2, Pi)"}]}]},"ranking":{"name":"mimo-v2.6-flash","display_name":"MiMo-V2.6-Flash","vendor":"Xiaomi","origin":"cn","category":"text","release_date":"2026-09-22","on_chinaapi":true,"sources":[{"leaderboard":"lmarena_text","metric":"Arena score (Elo)","value":"1452","entry":"mimo-v2.6-flash","url":"https://arena.ai/leaderboard/text","retrieved_at":"2026-10-05","interval":"±8","samples":6053},{"leaderboard":"lmarena_text_chinese","metric":"Arena score (Elo)","value":"1502","entry":"mimo-v2.6-flash","url":"https://arena.ai/leaderboard/text/chinese","retrieved_at":"2026-10-05","interval":"±27","samples":466},{"leaderboard":"lmarena_text_coding","metric":"Arena score (Elo)","value":"1521","entry":"mimo-v2.6-flash","url":"https://arena.ai/leaderboard/text/coding","retrieved_at":"2026-10-05","interval":"±15","samples":1508},{"leaderboard":"vals_index","metric":"Accuracy (GDP-weighted finance, coding, legal and tax)","value":"54.83","entry":"MiMo V2.6 Flash","url":"https://www.vals.ai/benchmarks/vals_index","retrieved_at":"2026-10-23","interval":"±1.11"}],"notes":"Released and open-sourced 2026-09-22 per Xiaomi's release record (MiMo-V2.6 Series Release). The 2026-10-01 Vals Index v2.1 read independently covers this model, replacing the previously quoted vendor self-reported DeepSWE figure."},"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. 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":"chinese","updated_at":"2026-10-02"},"lmarena_text_coding":{"id":"lmarena_text_coding","name":"LMArena Text Arena · Coding","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text/coding","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (text_style_control, category coding), 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","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}