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Alibaba's Qwen3.8-27B runs on a single consumer GPU, posts frontier-adjacent agentic benchmark scores, and ships under Apache 2.0. It also loses benchmarks printed on its own model card — and its default reasoning setting spent 21 minutes drawing one cartoon bird. Every number in this video is checked against a primary source, and where a comparison mixes benchmark harnesses I say so on screen. Sources are linked below. WHAT YOU GET • The exact release: 27,781,427,952 parameters, dense, 64 layers, 262,144 native context • Why only ONE of the two Qwen3.8 models released that week is actually open • The wins AND the losses — including the two benchmarks it loses on Alibaba's own card • Why "three different scoreboards" means most viral comparisons are invalid • The xhigh default problem, with the real token and wall-clock cost • Honest VRAM math: why a "17 GB" model strands a 16 GB card • Real local speed, and the llama.cpp flag that makes it ~72% faster CHAPTERS 0:00 The 21-minute bird 0:43 What Alibaba actually shipped 1:00 27,781,427,952 parameters, dense 1:51 Only one of the two is open 2:37 The numbers — losses first 3:04 It loses to its own family 3:28 Now the wins 4:21 Three scoreboards, not one ranking 5:28 The catch: xhigh by default 5:47 21 minutes, 22,276 reasoning tokens 6:12 The twist: slow was better 6:52 Qwen's counterpoint 7:10 Same weights, two different models 7:34 Can you run it? The VRAM ladder 8:26 The 17 GB trap 8:52 Real local speed 9:18 Who it's for 9:56 Withhold the crown THE NUMBERS, WITH THEIR SOURCE All Qwen3.8-27B scores below are SELF-REPORTED on the official model card (the model does not appear on the independent Terminal-Bench or DeepSWE leaderboards): • Terminal-Bench 2.1: 73.0 (Qwen3.6-27B 63.4) — Opus4.6 Max scores 78.2 on the same card • SWE-bench Pro: 61.7 (was 53.5) — best in its row • LiveCodeBench v6: 90.3 (was 83.9; Opus4.6 Max 88.8) • DeepSWE 1.1: 42.2 (was 13.3) — a 3.2x generational jump • GPQA Diamond: 89.2 — LOSES to Opus4.6 Max 91.3 and to Qwen's own 3.7-Plus 90.3 • OSWorld-Verified: 84.3 (was 63.9) · WebArena-Verified: 64.8 (was 48.8) • AndroidWorld: 81.9 — beats the card's Opus4.6 Max figure of 62.0 Cross-harness context (different scaffolds — NOT one ranking): the official tbench.ai Terminal-Bench 2.1 board lists Claude Code + Opus 4.8 at 78.9%; the official DeepSWE board lists GPT-5.6 Sol (max) at 73 and Claude Opus 5 at 74.0. Independent check: InsiderLLM measured HumanEval 80.49 vs 82.32 for Qwen3.6-27B on an RTX 3090 — a statistical tie (p=0.70). HARDWARE • BF16 weights 55.6 GB · official FP8 30.9 GB (the FP8 repo has MORE downloads than BF16) • No official GGUF. Community Q4_K_M builds run 16.8–19 GB depending on packager • Unsloth: 4-bit needs 17–19 GB — fits an RTX 4090 / 5080-class card or a 24 GB Mac • It is DENSE, not MoE: CPU offload is painfully slow, unlike the A3B siblings • ~15–30 tok/s from LM Studio; llama.cpp with MTP speculative decoding ≈ 72% faster SOURCES Official model card: https://huggingface.co/Qwen/Qwen3.8-27B Official FP8 build: https://huggingface.co/Qwen/Qwen3.8-27B-FP8 The 2.4T "Max" sibling (text-only, custom license): https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B Qwen GitHub (release dates): https://github.com/QwenLM/Qwen3.8 Simon Willison on the xhigh default: https://simonwillison.net/2026/Aug/16/qwen-38-27b/ Terminal-Bench 2.1 leaderboard: https://www.tbench.ai/leaderboard/terminal-bench/2.1 DeepSWE v1.1 leaderboard: https://deepswe.datacurve.ai llama.cpp GGUF builds: https://huggingface.co/ggml-org/Qwen3.8-27B-GGUF Unsloth run-locally docs: https://unsloth.ai/docs/models/qwen3.8 Unsloth NVFP4 build: https://huggingface.co/unsloth/Qwen3.8-27B-NVFP4 Independent RTX 3090 test: https://insiderllm.com/guides/qwen-3-8-27b-vs-3-6-27b-rtx-3090/ Fixed chat templates: https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates Hacker News discussion: https://news.ycombinator.com/item?id=49299605 r/LocalLLaMA release megathread: https://www.reddit.com/r/LocalLLaMA/comments/1voojjz/megathread_qwen_38_27b_release_day/ CORRECTIONS Found an error? Comment with the primary source and I'll pin the correction. #Qwen #LocalLLM #OpenSourceAI #AIBenchmarks #LLM

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