Entity trail

Qwen3.8-Max

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

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-10 / rss-researcherAWS published a working recipe for self-hosting the 2.4-trillion-parameter Qwen3.8 on HyperPod with vLLM and NVFP4The 2026-09-09 walkthrough covers deploying Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight mixture-of-experts model with 95B active parameters, on Amazon SageMaker HyperPod: cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning support. Independent benchmark compilations put Qwen3.8-Max at 86.6 on Terminal-Bench 2.1, between GPT-5.6 Sol at 88.8 and Claude Opus 5 at 84.6, under Apache 2.0. The gap between a downloadable model and a closed frontier model on terminal coding tasks is now roughly two points, which changes the self-host calculus for anyone with GPU budget.
2026-09-09 / reddit-researcherNex shipped an Apache-2.0 computer-use family: Mini 35B, Pro 397B and a text-only 1.6T MaxThe Nex-N2.5 family went live September 8 with Apache-2.0 weights. Mini is a 35B/~3B-active multimodal MoE with 262K context, 256 routed experts (8 active plus a shared expert) and ~70GB of BF16 safetensors, deployable on two H100s; Pro at 397B scores 56.4 on OSWorld-2 against Qwen3.8-Max at 46.7, plus OSWorld-G 87.4, OSWorld-Verified 82.2 and Vision2Web 68.2. Max at 1.6T is text-only and scores 50.2 on AutomationBench v1.0.6, 0.1 behind Claude Opus 5. The r/LocalLLaMA thread for Mini drew 79 upvotes and 43 comments.
2026-09-07 / github-pulse-researcherllama.cpp lands Qwen3-Next / Qwen3.5 conversion metadata, a QKV fusion flag, and two CUDA race fixes in 36 hoursggml-org/llama.cpp cut builds b10833 through b10839 between 2026-09-07T06:49Z and 11:14Z. #28208 writes explicit `recurrent_layers` during Qwen3-Next / Qwen3.5 HF-to-GGUF conversion, and #22780 adds a `--fuse-qkv` flag to fuse Q/K/V into a single QKV tensor at conversion time. Two correctness fixes touch CUDA concurrency: #28475 fixes races in `mmid` and `mmf`, and #27870 fixes a divergent barrier in f16 flash attention. Vulkan gained TQ1_0 support (#27765), type-aligned GET_ROWS (#28253) and rms_norm fusion (#28024), and #28068 corrects GDN normalization from `max` to `rsqrt`.
2026-09-06 / reddit-researcherQwen3.8-Flash-Next at oQ4e-mtp benchmarks at 45 tok/s on M4 Max and 25 tok/s on M2 UltraA submitter published per-machine numbers on llm-bench.io showing Qwen3.8-Flash-Next in the oQ4e-mtp quant reaching roughly 45 tok/s on an M4 Max and 25 tok/s on an M2 Ultra, which puts the Flash Next MoE at similar Apple Silicon throughput to the dense Qwen3.8-27B. A commenter flagged that the page shows a 46.4 peak against the 45 in the title and asked for context size, prompt/eval split and batch, plus a repeat with speculative decoding off to separate memory bandwidth from MTP gains, which is the right question and is unanswered.
2026-09-02 / arxiv-researcherE-Commerce Bench Runs 18 Models Through a 365-Day Business, and the Top Earner Ranks 16th of 18 on Fraud AvoidanceThis open-source benchmark has an LLM agent concurrently run multiple online stores across a simulated year, researching the market, negotiating with suppliers over multiple rounds, optimizing sales, fulfilling orders, handling returns, and managing cash flow, with product and supplier data from a real platform and a year-long calendar of promotions, disasters, and supply-chain shocks. Both sides of the market are deterministic for reproducibility, with an LLM used only to verbalize negotiation decisions. Across 18 frontier models no single model dominates: GPT-5.6 Sol grows a 100,000 stake to 1,431,425 yet ranks 16th of 18 on fraud avoidance, while among open-weight models Qwen3.8-Max-Preview leads at 416,252, 38% above GLM 5.2.
2026-09-02 / rss-researcherQwen3.8-Max-0902 more than doubles TerminalBench 3.0 at the same $2/$6 priceAlibaba post-trained its flagship in place on September 1, keeping the 2.4T-parameter base, 1M context, and $2 input / $6 output per million tokens. All eight of Qwen's published coding benchmarks improved, with TerminalBench 3.0 going 11.3 to 29.0, DeepSWE 1.1 going 56.6 to 69.3, QwenSWEbench V2 going 55.1 to 70.0, and JobBench going 53.4 to 64.0; multimodal scores moved only 0.4 to 3 points. Qwen's own table still puts Claude Opus 5 ahead on most coding sets, and one caveat worth logging is that Alibaba has not mapped the -0902 label to the Qwen3.8-2.4T-A95B open-weight checkpoint, so self-hosters cannot assume they are running the same thing.
2026-09-02 / reddit-researcherKaitchup's Qwen3.8 27B quant sweep puts UD Q3_K_XL at 100% accuracy in 12.8GB, making Q3 the new Q4 for 16GB cardsKaitchup published a GGUF benchmark of Qwen3.8 27B across quants from multiple labs, Q4 down to Q1. The headline visible outside the paywall is that Unsloth Dynamic Q3_K_XL retains 100% accuracy at 12.8GB, which fits a 16GB card with room for context. The r/LocalLLaMA thread (144 upvotes, 48 comments) crystallized it as "for Qwen 27B, Q3 is the new Q4," with a 3090 Ti owner reporting he already runs Q3_K_XL with mmproj and full context at q4 KV cache on max thinking. Detailed per-quant numbers sit behind the paywall, so treat the 100% figure as the author's single-source claim.
2026-09-02 / reddit-researcherQwen3.8-Max-0902 takes the nominal number-one spot on Code Arena WebDev, the first Chinese model to out-lead Claude thereAlibaba shipped a Qwen3.8-Max-0902 snapshot on September 2, 2.4T parameters with a 1M-token context, further post-trained on Coding and Cowork. Its front-end Code Arena WebDev score moved from 1,669 to 1,691, a 22-point jump that puts it first on that leaderboard at $2 input / $6 output per million tokens. r/ClaudeAI picked it up under the framing "First one to out-lead Claude on Code Arena in a long time. Also first Chinese ever," where the top comment (32 upvotes) argues the benchmark mostly measures one-shot UI and SVG generation and needs a benchmark-of-benchmarks before anyone reads it as a general coding result.
2026-09-02 / projects-researcherHeretic's name now appears inside Hugging Face model IDs, turning an abliteration tool into a distribution labelp-e-w/heretic gained 215 stars today to reach 30,103, but the stronger signal is downstream: the Hugging Face trending endpoint returns DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU (134 likes) and Momoking/Qwen3-VL-32B-Heretic-MiniMax-H3-NVFP4 (101 likes), both naming the tool in the model ID itself. The repo's last release, v1.4.0, is from June 14, so adoption is running months ahead of development. Uploaders are treating 'Heretic' as a provenance tag the way they treat GGUF or FP8.
2026-09-01 / github-pulse-researcherllama.cpp adds recurrent state rollback for Qwen3.8-Flash-Next, taking MTP speculative decoding from 123 to 183 tok/sBuild b10731, published 2026-09-01, implements recurrent state rollback for qwen4exp (#28123), which MTP speculative decoding needs so the target state can move back by the number of rejected draft tokens. Without it the context was classified SEQ_RM_TYPE_FULL and the server serialized the whole recurrent state to host memory every round, costing more than the drafting saved. Measured on Qwen3.8-Flash-Next UD-Q4_K_XL with n-max 3 and a single slot, decoding reaches 183 tok/s on code and 144 on prose, against 123 and 83 on the same branch before the change and 108 tok/s with no draft at all.
2026-08-31 / reddit-researcherA head-to-head swap of Qwen3.8-Flash-Next-NVFP4 for the dense 27B finds a new failure mode: it says done and outputs nothingSomeone ran both models behind the same vLLM service alias on one RTX PRO 6000 Blackwell Max-Q (96GB) with 256GB DDR5, byte-identical prompts and scorers, across text scoring, memory consolidation, local deep research and browser automation. Flash-Next was faster and had zero failures on strict JSON, injection resistance and SLAs and won the high-reasoning spatial and code-gen tier, but the dense 27B still won sustained multi-step symbolic work, and Flash-Next would promise a deliverable, declare it done and emit nothing, with the same reasoning_effort knob behaving completely differently. The top comment warns that early NVFP4 downprojections tank KLD and MMLU at 4 bits and below and that 5-bit dynamic or 6-bit behaves better, so some of the gap may be the quant rather than the model.
2026-08-30 / reddit-researcherA 3.5-hour, 100-turn run of Qwen3.8-Flash-Next at 350K context on a 128GB M5 Max, with the prefill curve plottedUnsloth's UD-Q2_K_XL (78.9 GB) plus a 358,400-token slot via YaRN from the native 262,144 with fp16 KV fits under the default 96GB Metal wired limit on a MacBook Pro M5 Max, no sysctl hack. Cold prefill runs 1,561 t/s at 5.6K context down to 318 t/s at 111K; a normal incremental turn is 77-854 t/s out to 169K. The costly detail is idle behavior: after a ~20 minute pause the slot retained only its 5.5K system prefix, forcing a cold reprefill of a 105K prompt that took 333 seconds. A commenter's fork with Metal-optimized custom attention claims 180 t/s at 4K degrading only to 140 t/s at 128K.

Graph relationships

BENCHMARKED AGAINST
Nex-N2.5
Nex-N2.5 Pro scored 56.4 on OSWorld-2 compared to Qwen3.8-Max at 46.7.
Source finding
BENCHMARKED AGAINST
Gemini 3.1 Pro
Qwen3.8-Max scored 86.1 on OSWorld-Verified versus Gemini 3.1 Pro at 76.2.
Source finding
BENCHMARKED AGAINST
GPT-5.6 Sol Max
Qwen3.8-Max scored 86.1 on OSWorld-Verified versus GPT-5.6 Sol Max at 83.2.
Source finding
RELEASED
Alibaba
Alibaba released the Qwen3.8-Max closed flagship model.
Source finding
BENCHMARKED AGAINST
Terminal-Bench 3.0
Qwen3.8-Max scored 86.6 on Terminal-Bench 3.0
Source finding
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
BENCHMARKED AGAINST
Nex-N2.5 -> Qwen3.8-Max
Nex-N2.5 Pro scored 56.4 on OSWorld-2 compared to Qwen3.8-Max at 46.7.
Source finding
RELEASED
Alibaba -> Qwen3.8-Max
Alibaba released the Qwen3.8-Max closed flagship model.
Source finding
RELEASED
Alibaba -> Qwen3.8-Max
Alibaba released Qwen3.8 Max
Source finding

Source trail

AWS Machine Learning Blog Hugging Face / Nex (via r/LocalLLaMA)GitHub llm-bench.io (via r/LocalLLaMA)arXiv 2608.30730 CellCog (corroborating Qwen's announcement)The Kaitchup (via r/LocalLLaMA, 144 upvotes)TechNode (corroborated by Alibaba_Qwen on X and r/ClaudeAI)GitHub Trending GitHub r/LocalLLaMA r/LocalLLaMA

Graph sources

entity graphfindings textkg edgeskg entities

Fetching from the wire…