{"data":{"schema_version":"model_decision_evidence.v1","snapshot_date":"2026-09-25","model":{"model":"qwen3.8-max","direct_answer":"Qwen3.8-Max is Alibaba's flagship Qwen3.8 API model, released on 2026-08-02 with 2.4T total and 95B active parameters, a 1,000,000-token context window, 131,072 output tokens and text, image and video input; thinking is on by default. Qwen calls it the official version based on the open Qwen3.8-2.4T-A95B checkpoint, which is text-only, and publishes no weights for the API model itself; AI Coding Daily's leaderboard scored it 47.08, below its 0902 snapshot.","verified_at":"2026-09-24","model_card":{"context_window":"1,000,000 tokens (991,808 input; 983,616 in thinking mode)","max_output":"131,072 tokens in both modes; the chain of thought is capped separately at 262,144","modalities":"Text, image and video input; text output","parameter_count":"2.4T total, 95B active, per Qwen's announcement"},"official_sources":[{"title":"qwen3.8-max Model Info","publisher":"Alibaba Cloud Model Studio","url":"https://www.alibabacloud.com/help/en/model-studio/qwen3-8-max","retrieved_at":"2026-09-24"},{"title":"Model lifecycle and updates","publisher":"Alibaba Cloud Model Studio","url":"https://www.alibabacloud.com/help/en/model-studio/newly-released-models","retrieved_at":"2026-09-24"},{"title":"Qwen3.8-Max: A New Bar for Coding and Cowork","publisher":"Qwen Team","url":"https://qwen.ai/blog?id=qwen3.8","retrieved_at":"2026-09-24"},{"title":"Qwen3.8-2.4T-A95B model card","publisher":"Qwen Team","url":"https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B","retrieved_at":"2026-09-24"}],"open_weights":{"status":"not_available","summary":"No weights are published for qwen3.8-max itself. Qwen's model card calls Qwen3.8-Max the official version based on Qwen3.8-2.4T-A95B, whose weights are open under the Qwen3.8-Max License; that checkpoint is text-only and always thinks, and the licence requires a separate licence before commercial use by model-as-a-service or AI work-assistant businesses with revenue above US$50 million over 12 months (relaying requests to models hosted by others is excluded).","repository":"","licence":"Proprietary hosted model","hardware":"Not applicable","recommended_runtime":"ChinaAPI or Alibaba Cloud Model Studio","source_urls":["https://www.alibabacloud.com/help/en/model-studio/qwen3-8-max","https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B","https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B/blob/main/LICENSE"],"verified_at":"2026-09-24","agent_recipe":""},"curated_comparisons":["qwen3.8-max-0902"],"open_weight_alternatives":["glm-5.3","hy4-preview"],"reviews":[{"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":"Qwen 3.8 Max at high reasoning scored 47.08 ($0.45 and 10 min 21 s per run in OpenCode), 29th of 47 rows, below its 0902 snapshot (48.70).","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":"47.08"},{"label":"Rank","value":"29 of 47"}]}]},"ranking":{"name":"qwen3.8-max","display_name":"Qwen3.8-Max","vendor":"Alibaba","origin":"cn","category":"text","release_date":"2026-08-02","on_chinaapi":false,"sources":[{"leaderboard":"lmarena_text","metric":"Arena score (Elo)","value":"1481","entry":"qwen3.8-max","url":"https://arena.ai/leaderboard/text","retrieved_at":"2026-09-22","interval":"±6"},{"leaderboard":"livebench","metric":"Overall","value":"78.5","entry":"Qwen 3.8 Max","url":"https://livebench.ai/","retrieved_at":"2026-09-12"},{"leaderboard":"aa_index","metric":"Intelligence Index","value":"40","entry":"Qwen3.8 Max","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-21"},{"leaderboard":"superclue","metric":"总分 (overall)","value":"71.48","entry":"Qwen3.8-Max(max)","url":"https://www.superclueai.com/homepage","retrieved_at":"2026-09-22"},{"leaderboard":"vals_index","metric":"Accuracy (GDP-weighted finance, coding and legal)","value":"51.84","entry":"Qwen 3.8 Max","url":"https://www.vals.ai/benchmarks/vals_index","retrieved_at":"2026-09-21","interval":"±1.29"},{"leaderboard":"epoch_eci","metric":"General ECI","value":"157","entry":"Qwen 3.8 Max","url":"https://epoch.ai/eci","retrieved_at":"2026-09-22","interval":"(155 - 159)"},{"leaderboard":"lmarena_text_chinese","metric":"Arena score (Elo)","value":"1538","entry":"qwen3.8-max","url":"https://arena.ai/leaderboard/text/chinese","retrieved_at":"2026-09-22","interval":"±18"},{"leaderboard":"lmarena_text_coding","metric":"Arena score (Elo)","value":"1522","entry":"qwen3.8-max","url":"https://arena.ai/leaderboard/text/coding","retrieved_at":"2026-09-22","interval":"±9"},{"leaderboard":"livebench_coding","metric":"Coding","value":"72.9","entry":"Qwen 3.8 Max","url":"https://livebench.ai/","retrieved_at":"2026-09-12"},{"leaderboard":"livebench_agentic_coding","metric":"Agentic Coding","value":"64.6","entry":"Qwen 3.8 Max","url":"https://livebench.ai/","retrieved_at":"2026-09-12"},{"leaderboard":"aa_speed","metric":"Median output tokens/s","value":"38","entry":"Qwen3.8 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":"55","entry":"Qwen3.8 Max","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-21"},{"leaderboard":"aa_tbench_v40","metric":"Agentic Coding \u0026 Terminal Use (%)","value":"19","entry":"Qwen3.8 Max","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"},{"leaderboard":"aa_mlcr","metric":"Medical Long Context Reasoning (%)","value":"19.4","entry":"Qwen3.8 Max","url":"https://artificialanalysis.ai/evaluations/mlcr-aa","retrieved_at":"2026-09-07"}],"notes":"The 2026-08-02 build of Qwen3.8-Max (Artificial Analysis dates it 2026-08-03), which replaced Qwen3.8-Max-Preview (retired 2026-08-05). Its release_date follows Epoch, which re-dated this build from 2026-07-19, the Preview's announcement at WAIC, to 2026-08-02 between its 2026-08-25 and 2026-09-03 releases; Model Studio listed qwen3.8-max on 2026-08-02 as well. Alibaba Cloud's update notices of 2026-09-02 (www.aliyun.com/notice/118616 and the international Model Studio notice Update Notice for Qwen3.8-Max Models) state that from 10:00 Beijing time on 2026-09-05, subject to the actual change time, the qwen3.8-max model name automatically moved to the qwen3.8-max-0902 snapshot with billing unchanged. The name on sale here therefore now serves the Qwen3.8-Max-0902 row, and Model Studio lists no snapshot ID for this build, so it can no longer be called there. Every figure on this row belongs to this build: Artificial Analysis's Qwen3.8 Max (slug qwen3-8-max-0803), Epoch's Qwen 3.8 Max (dated 2026-08-02), SuperCLUE's Qwen3.8-Max(max) (evaluation published 2026-08-06), LMArena's qwen3.8-max (added to its Text Arena on 2026-08-02 per its changelog), LiveBench's Qwen 3.8 Max (quoted at the same figures since 2026-08-10, before the snapshot existed) and Vals's Qwen 3.8 Max (released 2026-08-03 per its model page). Artificial Analysis marked its Qwen3.8 Max row deprecated between the 2026-09-12 and 2026-09-21 reads; the leaderboard hides such rows under its default Status: Current filter, so this row's leaderboard figures are read with the Status filter set to All."},"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 board that orders the table, 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 board that orders the table, 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 board that orders the table, 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":"2026-09-22"},"livebench":{"id":"livebench","name":"LiveBench","operator":"LiveBench (sponsored by Abacus.AI)","metric":"Overall","url":"https://livebench.ai/","edition":"LiveBench-2026-06-25","retrieved_at":"2026-09-22"},"livebench_agentic_coding":{"id":"livebench_agentic_coding","name":"LiveBench · Agentic Coding","operator":"LiveBench (sponsored by Abacus.AI)","metric":"Agentic Coding","url":"https://livebench.ai/","edition":"LiveBench-2026-06-25","retrieved_at":"2026-09-22","scene":"agentic"},"livebench_coding":{"id":"livebench_coding","name":"LiveBench · Coding","operator":"LiveBench (sponsored by Abacus.AI)","metric":"Coding","url":"https://livebench.ai/","edition":"LiveBench-2026-06-25","retrieved_at":"2026-09-22","scene":"coding"},"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":"live board, last updated 2026-09-02","retrieved_at":"2026-09-12","task":"image_to_video","updated_at":"2026-09-02"},"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":"live board, last updated 2026-09-07","retrieved_at":"2026-09-12","task":"image_editing","updated_at":"2026-09-07"},"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":"live board, last updated 2026-09-07","retrieved_at":"2026-09-12","task":"text_to_image","updated_at":"2026-09-07"},"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":"live board, last updated 2026-09-04","retrieved_at":"2026-09-12","task":"text_to_video","updated_at":"2026-09-04"},"lmarena_text":{"id":"lmarena_text","name":"LMArena Text Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text","edition":"live board","retrieved_at":"2026-09-22"},"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":"live board, Chinese-language category (prompts in Chinese)","retrieved_at":"2026-09-22","scene":"chinese"},"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":"live board, Coding category","retrieved_at":"2026-09-22","scene":"coding"},"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":"2026-09-22"},"vals_index":{"id":"vals_index","name":"Vals Index","operator":"Vals AI","metric":"Accuracy (GDP-weighted finance, coding and legal)","url":"https://www.vals.ai/benchmarks/vals_index","edition":"V2 (released 2026-08-13), page states updated 9/21/2026","retrieved_at":"2026-09-22"},"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}