Entity trail

Qwen 3.7-Plus

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

Briefing refs

Findings

Edges

Sources

Corpus findings

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 / reddit-researcherA €4,000 dual-R9700 build runs Qwen 3.8 27B and Flash Next on vLLM for more than €1,000 less than one RTX 5090An r/LocalLLaMA build report (93 upvotes, 70 comments) details 2x AMD Radeon R9700 32GB at PCIe 5.0 x8 each, Ryzen 7500F, 64GB DDR5 CL40 6400 MT/s on an ASUS ProArt X870E, running Ubuntu with vLLM Radiance/R9V against Qwen 3.8 27B in FP8 and MXFP4 plus Flash Next. Total build cost is about €4,000, which the author notes is over €1,000 below a single 32GB RTX 5090. Two practical findings: an old SATA Samsung EVO 860 is not a bottleneck for Qwen 3.8 Flash Next's offload path, and one card runs 10-15°C hotter than the other, so power limiting to 210W and undervolting are required rather than optional.
2026-09-07 / vibe-coding-researcherQwen Code adds a session resource catalog and dynamic workflow run management to its web shellv0.23.1-preview.2 (7 September) adds `feat(serve): add session resource catalog` (#10837) and visualization plus management of dynamic workflow runs in the web shell (#10594), alongside BTW side questions inside Channel conversations (#10713). The same day's cua-driver-rs v0.20.4 adds two off-by-default env flags worth knowing: `CUA_DRIVER_RS_COORDINATE_SPACE=1` for relative coordinates and `MCP_MODEL_PAYLOAD_FILTER=1` for textual MCP payload filtering on affected model API routes.
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.
2026-09-05 / reddit-researcherA Qwen3.8-27B user reports 8+ hours of unsupervised agentic work with a 2048-token reasoning budgetThe top-voted claim in r/LocalLLaMA today (359 upvotes, 175 comments) is that Qwen3.8-27B is the first local model the poster trusts to run unattended, with the config spelled out in an edit: huihui abliterated Q3_K_XL, reasoning budget capped at 2048 and still coherent at 1024, KV cache at Q8 with 128k context, and a custom chat template plus reasoning-format deepseek to fix tool-call and think-tag generation. Two other threads corroborate the agentic angle, including a run where the model reached a target Wikipedia article in 6 Playwright clicks with no backtracking. This is a single practitioner's report, not a benchmark, but the failure modes named (tool tag generation, compaction) are the ones that actually stop local agent loops.
2026-09-04 / reddit-researcherQwen3.8-Flash-Next multi-token prediction merged into ik_llama.cpp, roughly doubling decode on a 5090The merge supports both an integrated MTP head and a separate -md draft file, with the poster reporting 45 to 90 tok/s on a 5090 with 128GB system RAM and working configurations down to a 12GB 4070. A separate Part 2 post on the 2x3090 plus DDR4 build reports 37-41 t/s decode using UD-Q4_K_XL plus expert cache plus MTP, up from the 25-29 t/s that same builder reported two days ago. Both are single-builder measurements on hand-built branches, not vendor numbers.
2026-09-02 / reddit-researcherSpark-X2.5 arrives as a genuinely new 4B/1.7B architecture on Apache 2.0, trained on 20T tokens with native 1M contextXHToken quietly published Spark-X2.5-4B and Spark-X2.5-1.7B on Hugging Face. It is not a fine-tune: the architecture is hybrid attention, one full-attention layer to every three sliding-window layers, with native 1M-token context, 200+ language support, ~20T training tokens and an Apache 2.0 license. Claimed scores for the 4B include 65.1 on BFCL-V4, 75.1 on tau-squared-bench, 44.4 on SWE-Bench Pro and 90.7 on AIME 2026, benchmarked against Qwen3.5 and Gemma4 at 2B through 12B. It does not run on upstream llama.cpp yet — PR ggml-org/llama.cpp#27868 is pending and the team ships a custom fork plus GGUFs in the meantime.
2026-09-01 / arxiv-researcherQwen3.8-Flash-Next Matches a 397B Predecessor on 8 of 14 Benchmarks Using a Ninth of the Training FLOPsThe architecture report describes a 125B sparse mixture-of-experts model activating 6B parameters per token, plus 51B of n-gram embedding tables held off the accelerator in host memory with prefetching. Against the prior 397B-A17B model it leads on 8 of 14 pre-training benchmarks and trails by at most 2.6 points elsewhere, using a third of the activated parameters, a third of the training tokens and roughly a ninth of the training FLOPs. The design mixes Gated DeltaNet with one full-attention layer per four, adds Qwen Sparse Attention for continued pretraining and a four-branch Gated Residual stream, and reports that pairing it with Muon shifts optimal learning rate and batch size upward and removes the need for batch-size warmup.
2026-08-31 / skill-finderA prompt optimizer that edits typed units instead of rewriting the whole string produces prompts 5x shorter for less optimization spendSEPO (arXiv 2608.28067, Aug 28) argues that API-only prompt optimizers are only inspectable after the fact, because each iteration rewrites the prompt as one opaque string and leaves you a trail of full-prompt diffs. Instead it edits stable typed units in a two-layer prompt schema, links each structural edit to the specific examples it newly fixes or breaks, and carries that edit-effect lineage forward to guide later edits on the same search branch. Across a 14-task held-out suite it beats GEPA by 3.1 points on Llama-3.1-8B-Instruct and 2.2 on Qwen3-8B, reaching 61.9% and 73.3% macro accuracy, while spending 2.9M optimization tokens against GEPA's 4.1M and emitting prompts over 5x shorter. Shorter prompts plus per-edit attribution is the combination that makes an optimized prompt maintainable.
2026-08-31 / github-pulse-researcherllama.cpp adds a Vulkan top-k radix sort shader for k >= 1024, targeted at Qwen 3.8 Flash NextBuild b10712, released 2026-08-31, adds a Vulkan radix-sort top-k path for k >= 1024 plus top-k QSA fusion, with tests written specifically against Qwen 3.8 Flash Next (#28032). Large-k sampling was the bottleneck for that model's sparse-attention design on Vulkan backends, so this is the runtime catching up to a model architecture rather than a generic optimization. Build b10714 the same morning tunes RDNA3 mat-vec to a static 4 rows above four columns on Strix Halo, which benched faster than the defaults across types and batch sizes (#27909).
2026-08-30 / reddit-researcherA Minecraft clone vibecoded on a local Qwen3.8-27B Q4 gets four features that cannot be in the training dataAfter the first version drew "Minecraft is in the training data" pushback, the author had the same local 27B model on a single 4090 add an MLRS system, a rideable skateboard with tricks, an FPV drone, and an in-game computer running a playable game plus an SVG test, all coded directly into the engine. The post hit 393 upvotes on r/LocalLLaMA. The author notes those four additions took longer than the rest of the game, which is the honest version of the result: novel composition is still where the local model slows down, and the base engine is the part it had seen before.
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
Qwen3.8-27B
Qwen3.8-27B with low reasoning preset outscores Qwen 3.7 Plus on reasoning benchmarks.
Source finding
BENCHMARKED AGAINST
Qwen3.8-27B -> Qwen 3.7-Plus
Qwen3.8-27B with low reasoning preset outscores Qwen 3.7 Plus on reasoning benchmarks.
Source finding

Source trail

Hugging Face / Nex (via r/LocalLLaMA)r/LocalLLaMA (93 upvotes, 70 comments)GitHub llm-bench.io (via r/LocalLLaMA)r/LocalLLaMA (359 upvotes)r/LocalLLaMA (162 upvotes)Hugging Face (via r/LocalLLaMA, 201 upvotes)arXiv 2608.30320 arXiv 2608.28067 - SEPO: Evidence-Grounded Prompt Optimization via Structural Editing GitHub r/LocalLLaMA r/LocalLLaMA

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