{"data":{"schema_version":"model_decision_evidence.v1","snapshot_date":"2026-09-25","model":{"model":"qwen3.8-flash","direct_answer":"Qwen3.8-Flash is Alibaba's multimodal Qwen3.8 API model, released on 2026-08-26 with a 1,000,000-token context window, 131,072 output tokens and text, image and video input; thinking mode is on by default. Qwen calls it the official version of the open Qwen3.8-Flash-Next with production features such as 1M context by default and built-in tools, and publishes no weights for it. In RemakeBench's Flash comparison the lab preferred it in several game and 3D-scene rounds, but it missed the single-turn Rubik's Cube and timed out on the CAD fixture; coding leaderboards place it mid-table (47.22 on AI Coding Daily's, 51/60 on kzhu's).","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":"Not disclosed for the API model; the open Qwen3.8-Flash-Next it is based on has 125B parameters with 6B activated, plus a 51B n-gram embedding and a 4B MTP module"},"official_sources":[{"title":"qwen3.8-flash Model Info","publisher":"Alibaba Cloud Model Studio","url":"https://www.alibabacloud.com/help/en/model-studio/qwen3-8-flash","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":"Deep thinking","publisher":"Alibaba Cloud Model Studio","url":"https://www.alibabacloud.com/help/en/model-studio/deep-thinking","retrieved_at":"2026-09-24"},{"title":"Qwen3.8-Flash-Next model card","publisher":"Qwen Team","url":"https://huggingface.co/Qwen/Qwen3.8-Flash-Next","retrieved_at":"2026-09-24"}],"open_weights":{"status":"not_available","summary":"No weights are published for qwen3.8-flash itself. Qwen's model card calls it the official version based on Qwen3.8-Flash-Next, whose weights are open under the Qwen Community License 1.0: that licence requires a separate licence from Qwen before commercial use by anyone running a model-as-a-service or AI work-assistant business, and Flash-Next has 262,144 tokens of native context, extensible to 1,000,000. Qwen3.8-27B in this snapshot is Apache-2.0.","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-flash","https://huggingface.co/Qwen/Qwen3.8-Flash-Next","https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE"],"verified_at":"2026-09-24","agent_recipe":""},"curated_comparisons":["deepseek-flash","glm-5.3-flash","gemini-3.8-flash"],"open_weight_alternatives":["qwen3.8-27b","glm-5.3-flash"],"reviews":[{"id":"remakebench-launch-005-2026-09","platform":"Independent benchmark","evidence_type":"independent_suite","title":"RemakeBench Launch 005: four Flash models on 14 fixtures","url":"https://www.remakebench.com/qwen-3-8-flash-vs-deepseek-v4-1-flash-vs-glm-5-3-flash-vs-gemini-3-8-flash","published_at":"2026-09-18","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"RemakeBench","handle":"@remakebench","profile_url":"https://www.youtube.com/@remakebench","description":"Benchmark channel that publishes per-fixture clips, costs, wall-clock times and validation notes"},"task":"Fourteen fixtures spanning game building, 3D scenes, CAD, drawing and robot control, run on Qwen 3.8 Flash, DeepSeek V4.1 Flash, GLM 5.3 Flash and Gemini 3.8 Flash","method":"Each model's attempt per fixture is shown as a video clip with its displayed cost, wall-clock time and reasoning setting; pass/fail labels are the lab's video judgments.","finding":"No model won overall. The lab's editorial preference put Qwen 3.8 Flash ahead in several game and 3D-scene rounds, such as the interactive voxel diorama, but it did not pass the single-turn Rubik's Cube ($0.2616, 48 min 35 s) and timed out on Turbofan CAD without any geometry.","limitation":"Pass/fail labels are video judgments without a machine-readable validator, and single attempts do not establish reliability; the lab publishes no aggregate score or overall winner. Two Qwen results are still pending a judge, and its reasoning setting is unreported outside the campaign runs.","metrics":[{"label":"Interactive voxel diorama","value":"Pass · $0.3338"},{"label":"Single-turn cube","value":"Did not pass · $0.2616"}]},{"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 Flash at max reasoning scored 47.22 ($0.04 and 8 min 38 s per run in OpenCode), 28th of 47 rows.","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 (max)","value":"47.22"},{"label":"Rank","value":"28 of 47"}]},{"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":"Qwen 3.8 Flash scored 51/60 on version 2.1 in Claude Code: both maths puzzles right, but many small coding flaws, such as an overflowing browser-OS pop-up, a racing game that ends on the first crash and off-centre Trello buttons.","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":"51 / 60 (v2.1)"}]}]},"ranking":{"name":"qwen3.8-flash","display_name":"Qwen3.8-Flash","vendor":"Alibaba","origin":"cn","category":"text","release_date":"2026-08-26","on_chinaapi":true,"sources":[{"leaderboard":"aa_index","metric":"Intelligence Index","value":"40","entry":"Qwen3.8-Flash-Next","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-21"},{"leaderboard":"aa_speed","metric":"Median output tokens/s","value":"54","entry":"Qwen3.8-Flash-Next","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"},{"leaderboard":"aa_gdpval","metric":"Agentic Real-World Work Tasks, (Elo-500)/2000 (%)","value":"56","entry":"Qwen3.8-Flash-Next","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-21"},{"leaderboard":"aa_tbench_v40","metric":"Agentic Coding \u0026 Terminal Use (%)","value":"25","entry":"Qwen3.8-Flash-Next","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"}],"notes":"The boards list this model under its open-weight release name, Qwen3.8-Flash-Next (released 2026-08-26 per the Qwen team). The vendor's announcement states that the production version, with a 1M default context and built-in tools, is served under the name Qwen3.8-Flash, which is the qwen3.8-flash API on sale here since 2026-09-04; the two rows are joined on that statement, not on the row name. Artificial Analysis now prints 40 without an estimated marker (read 2026-09-21). SuperCLUE lists it under the API name itself, Qwen3.8-Flash(max), evaluated through the API with an evaluation published 2026-09-10, so that figure needs no join. Neither Epoch, LMArena nor Vals carried a row as of 2026-09-05. 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":"Epoch AI eci_scores.csv. CA-1270: complete board re-read; existing exact build/configuration citations retained.","licence":"CC BY 4.0, as labelled on the page (https://creativecommons.org/licenses/by/4.0/)","retrieved_at":"2026-11-11"},"lmarena_i2v":{"id":"lmarena_i2v","name":"LMArena Image-to-Video Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset","edition":"Hugging Face lmarena-ai/leaderboard-dataset · image_to_video · category overall · split latest · published 2026-11-09 · revision 4217986c2fa1. CA-1270: complete board re-read; existing exact build/configuration citations retained.","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":"2026-11-11","task":"image_to_video","updated_at":"2026-11-09"},"lmarena_image_edit":{"id":"lmarena_image_edit","name":"LMArena Image Edit Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset","edition":"Hugging Face lmarena-ai/leaderboard-dataset · image_edit · category overall · split latest · published 2026-11-09 · revision 4217986c2fa1. CA-1270: complete board re-read; existing exact build/configuration citations retained.","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":"2026-11-11","task":"image_editing","updated_at":"2026-11-09"},"lmarena_t2i":{"id":"lmarena_t2i","name":"LMArena Text-to-Image Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset","edition":"Hugging Face lmarena-ai/leaderboard-dataset · text_to_image · category overall · split latest · published 2026-11-09 · revision 4217986c2fa1. CA-1270: complete board re-read; existing exact build/configuration citations retained.","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":"2026-11-11","task":"text_to_image","updated_at":"2026-11-09"},"lmarena_t2v":{"id":"lmarena_t2v","name":"LMArena Text-to-Video Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset","edition":"Hugging Face lmarena-ai/leaderboard-dataset · text_to_video · category overall · split latest · published 2026-11-09 · revision 4217986c2fa1. CA-1270: complete board re-read; existing exact build/configuration citations retained.","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":"2026-11-11","task":"text_to_video","updated_at":"2026-11-09"},"lmarena_text":{"id":"lmarena_text","name":"LMArena Text Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset","edition":"Hugging Face lmarena-ai/leaderboard-dataset · text_style_control · category overall · split latest · published 2026-11-09 · revision 4217986c2fa1. CA-1270: complete board re-read; existing exact build/configuration citations retained.","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":"2026-11-11","updated_at":"2026-11-09"},"lmarena_text_chinese":{"id":"lmarena_text_chinese","name":"LMArena Text Arena · Chinese","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset","edition":"Hugging Face lmarena-ai/leaderboard-dataset · text_style_control · category chinese · split latest · published 2026-11-09 · revision 4217986c2fa1. CA-1270: complete board re-read; existing exact build/configuration citations retained.","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":"2026-11-11","scene":"chinese","updated_at":"2026-11-09"},"lmarena_text_coding":{"id":"lmarena_text_coding","name":"LMArena Text Arena · Coding","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset","edition":"Hugging Face lmarena-ai/leaderboard-dataset · text_style_control · category coding · split latest · published 2026-11-09 · revision 4217986c2fa1. CA-1270: complete board re-read; existing exact build/configuration citations retained.","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":"2026-11-11","scene":"coding","updated_at":"2026-11-09"},"superclue":{"id":"superclue","name":"SuperCLUE 智能指数","operator":"SuperCLUE","metric":"总分 (overall)","url":"https://www.superclueai.com/homepage","edition":"2026年11月.xlsx · sheet 总排行榜 · last modified Thu, 05 Nov 2026 02:01:53 GMT. CA-1270: complete board re-read; existing exact build/configuration citations retained.","retrieved_at":"2026-11-11","updated_at":"2026-11-05"},"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":"Vals Index v2.2 · page updated 2026-11-09. CA-1270: complete board re-read; existing exact build/configuration citations retained.","retrieved_at":"2026-11-11","updated_at":"2026-11-09"},"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}