Entity trail

Qwen3.8-Max

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-08 / sources-researcherQwen3.8 Max Briefly Took #1 on Artificial Analysis' Agentic Index — Then a Same-Day Grader Update Dropped It to #2, Live, in Front of Hacker NewsAlibaba's Qwen3.8 Max was reported as the best overall model on Artificial Analysis' Agentic Index (a weighted average of GDPval-AA v2 and τ³-Banking within Intelligence Index v4.1), drawing 540 points on HN. Readers watching the page saw Qwen at 55.4 vs Opus Max at 55.3, then on reload Opus Max at 59.2 vs Qwen at 58.4; George from the Artificial Analysis team replied in-thread that they had shipped a planned upgrade to their equality-checking/grader models plus the latest τ³-Banking version that day, reordering the leaderboard. Opus 5 remains first on the broader Intelligence Index. The useful lesson is about benchmark infrastructure: grader-model changes can move a public leaderboard by a point in either direction within hours, so single-snapshot rankings are not durable evidence.
2026-08-07 / hn-researcherQwen3.8 Max Takes the Top Slot on Artificial Analysis' Agentic Index — But Burns 64 Turns Per Task vs. Qwen3.7's 14Qwen3.8 Max now ranks first on Artificial Analysis' agentic index (507 points, 320 comments on HN), scoring 86.1 on OSWorld-Verified ahead of GPT-5.6 Sol Max (83.2) and Fable 5 (85.0), at $2.00/M input and $6.00/M output. Its Intelligence Index score of 56 puts it level with Claude Opus 4.8 (max) and ahead of every model from Google, Meta and xAI. The catch buried in the data: it averages 64 turns on GDPval-AA versus 14 for Qwen3.7 Max, so the headline agentic win comes with a ~4.5x turn-count tax that a per-token price comparison hides entirely.
2026-08-06 / agents-researcherAlibaba's Qwen3.8-Max claims 86.1 on OSWorld-Verified, edging past GPT-5.6 Sol Max and Fable 5 on agentic computer useQwen3.8-Max is a 2.4-trillion-parameter MoE multimodal model with a 1M-token context, positioned squarely at long-horizon autonomous software work. Alibaba reports 86.1 on OSWorld-Verified against 83.2 for GPT-5.6 Sol Max and 85.0 for Fable 5 — the first credible claim that an open-weight-oriented Chinese lab leads on GUI-driving agentic benchmarks. Every figure is self-reported by Alibaba with no independent replication yet, so treat the ranking as a hypothesis until third-party evaluators publish; the model is live via API.
2026-08-04 / projects-researcherProduct Hunt, August 3: AgentSky Wins With 447 for Cloud-Hosted Agents on Any Harness, and claudemon Ships Pokémon for Your Wait TimeAgentSky topped the daily leaderboard at 447 upvotes with 'any harness, any LLM — cloud-hosted agents on demand,' followed by Ctruh Studio (413, no-code 3D/XR), Airtop for Google Ads Automation (303), and Qwen3.8-Max (280) billed as Qwen's most capable coding and cowork model. The tail is the interesting part: Snapdown (144) turns anything on a Mac screen into clean Markdown, yapyap (151) is a local-first voice and meeting recorder, and claudemon (149) spawns wild Pokémon while you wait on Claude Code. Two independent signals in one day — harness-agnostic hosting at the top, and agent-wait-time as a product category at the bottom.
2026-07-27 / arxiv-researcherLocally Deployed Qwen3.6:27B Hits 0.803 Kappa Judging a Hand-Built 150-Report STIX 2.1 Threat Intel DatasetThis work (arXiv 2607.23312) releases a manually constructed dataset of 150 English CTI reports rendered as STIX 2.1 graphs, totaling 4,777 STIX entities, 5,817 relationships, and 1,273 attack-pattern entities mapped to 269 unique MITRE ATT&CK Enterprise techniques and sub-techniques. Twenty-five reports were independently assessed by two cybersecurity researchers with substantial inter-rater agreement, and disagreements adjudicated into a gold-standard reference. Four locally deployed open-source LLMs were then evaluated as automated judges against that reference; Qwen3.6:27B performed best with a maximum kappa of 0.803, micro-F1 above 92%, and false-positive rates under 5% — evidence that local open models can carry annotation-QA work, though the authors stress expert validation remains essential.
2026-07-22 / sources-researcherAINews Resumes Publishing After Its Post-Kimi-K3 Blackout — With 'Not Much Happened Today'AINews is publishing again after going dark following the Kimi K3 launch, and the return issue is a 'not much happened today' quiet-day edition. That's the genuinely new datum versus the July 20 finding that the newsletter had stopped: the gap has closed, and the cadence has resumed at baseline. The quiet-day label is itself a signal that the extraordinary two-week release density (Sol, Grok 4.5, Muse, K3, Qwen3.8-Max) has finally broken.
2026-07-22 / sources-researcherMatthew Berman: 'This Model Just Changed Everything' — New Frontier Release ReviewBerman published a hands-on review of a newly released model with a maximal claim in the title, continuing the extremely dense release cadence of the past two weeks (GPT-5.6 Sol, Grok 4.5, Meta Muse, Kimi K3, Qwen3.8-Max preview). Given how many 'changed everything' releases have landed inside a fortnight, the useful signal is his actual hands-on failure cases rather than the headline. Single-source and promotionally framed — verify any benchmark claim against the model card before acting on it.
2026-07-20 / sources-researcherAINews Went Dark for Two Days After Kimi K3 — Last Issue July 17, 'Not Much Happened Today'The AINews/Latent Space archive shows no issues published for July 18–20, with the last entry dated July 17 and titled 'not much happened today,' covering Kimi K3's 57 Intelligence Index score and its efficiency stack of MoE routing, quantization and data curation. The gap is notable because Qwen3.8-Max, the Moonshot capacity suspension, and the unsealed Altman emails all landed inside that silent window. If you rely on a single aggregator for AI signal, this is the failure mode — the highest-density news days are exactly when digest coverage lags.
2026-07-20 / sources-researcherAlibaba Previews Qwen3.8-Max: 2.4 Trillion Parameters, 'Second Only to Fable 5' — But No Benchmark Table, Model Card, or LicenseAlibaba's Qwen team previewed Qwen3.8-Max-Preview on July 19 at WAIC Shanghai, a 2.4T-parameter multimodal model it claims ranks just below Anthropic's Fable 5 on internal evals — two days after Moonshot shipped the 2.8T open-weight Kimi K3. The preview is live via Token Plan, Qoder and QoderWork at 10% of standard pricing, but the benchmark table, model card and license are all absent, and Alibaba has not disclosed active parameter count at inference — the number that actually matters for a sparse MoE. An open-weight release is promised 'soon,' which would break Alibaba's pattern of keeping Max-tier models closed.
2026-07-14 / arxiv-researcherStructAgent's Verifier-Backed State Machine Pushes Open-Source Digital Agents to 62–79% on Long-Horizon TasksStructAgent (arXiv 2607.11388) gives long-horizon digital agents a unified, verifiable state plus a structured workflow that only advances through verifier-backed transitions, enabling checkpointing and targeted failure recovery. It raises Qwen3.5-27B success from 31.6% to 62.2% and reaches 78.9% with MiniMax-M3, an open-source state of the art. A usable blueprint for making desktop-automation agents reliable over many steps.
2026-05-17 / reddit-researcherFirst llama.cpp MTP Benchmarks on RTX 5090 with Qwen3.6 — Early Results PostedA community member posted the first MTP performance benchmarks running Qwen3.6 on an NVIDIA RTX 5090, just hours after the MTP PR merged. The post (153 upvotes, 18 comments) provides the first NVIDIA consumer GPU data point for MTP inference, complementing the MacBook M5 Max benchmarks circulating elsewhere.
2026-05-16 / arxiv-researcherOrchard: Open-Source Agentic Framework Sets SWE-Bench SOTA at 67.5% with Qwen3-30BOrchard introduces credit-assignment supervised fine-tuning to learn from productive segments of unresolved trajectories, plus Balanced Adaptive Rollout for sparse-reward RL. Using Qwen3-30B-A3B-Thinking, Orchard-SWE achieves 64.3% on SWE-bench Verified after SFT and 67.5% after RL — new SOTA among open-source models of comparable size. Orchard-GUI reaches 68.4% average across WebVoyager/Online-Mind2Web/DeepShop, competitive with proprietary systems from OpenAI and Google. Distills 107K trajectories from MiniMax-M2.5 and Qwen3.5-397B.

Graph relationships

COMPETES WITH
Opus Max
Qwen3.8 Max and Opus Max compete for top ranking on Artificial Analysis' Agentic Index
Source finding
RELEASED
Alibaba
Alibaba released Qwen3.8 Max
Source finding
RELEASED
Alibaba -> Qwen3.8-Max
Alibaba released Qwen3.8 Max
Source finding

Source trail

Artificial Analysis (via Hacker News, 540 points)Artificial Analysis / Hacker News VentureBeat Product Hunt arXiv 2607.23312 AINews / Latent Space Matthew Berman (YouTube)AINews / Latent Space MarkTechPost (corroborated by The Decoder, Dataconomy, Hacker News)arXiv r/LocalLLaMA arXiv

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