Based on this YouTube video's total view count of 5.11K views and industry-standard rates, the estimated total earning is $4 - $10 through ad revenue. Historical data is not yet available to calculate daily, weekly, or monthly averages.

Explore Qwen-3.8-27B Released: Everything you need to Know -Beats Opus 4.6 on Computer Use -Runs on a 4090 with 5,110 views, 77 likes, and 13 comments. Experience the impact of this video content that has captured audience attention.

wen shipped Qwen3.8-27B open-weights under Apache-2.0 on 14 August 2026 — a 27B native vision-language model with 262,144 tokens of native context, thinking on by default, and a `reasoning_effort` dial. Its own benchmark card marks it best in table on 8 of 12 text rows, including SWE-bench Pro at 61.7 against 53.4 for Opus4.6 Max, DeepSWE 1.1 at 42.2 where the previous generation managed 13.3, and LiveCodeBench v6 at 90.3. In the vision table it posts 84.3 on OSWorld-Verified computer use against 72.7 for Opus4.6 Max, 64.8 on WebArena-Verified and 81.9 on AndroidWorld. This video reads the whole table, including the four text rows it loses — Terminal Bench 2.1 (73.0 vs 78.2), NL2Repo-Bench (42.3 vs 47.6), GPQA Diamond (89.2, third of five) and Humanity's Last Exam (30.8 vs 40.0, the widest gap in the table) — and the two footnotes that change what those numbers mean: the SWE-bench Pro baselines were not all measured on the same revision of the benchmark, and the 'with CI' vision scores are measured with a code interpreter in hand. It also covers the thing most people actually want: hardware. Full BF16 weights are ~55 GB — H100 territory. Unsloth's day-zero dynamic GGUFs run from UD-IQ2_XXS at 9.01 GB up to BF16 at 54.66 GB, with UD-Q4_K_XL at 17.92 GB fitting a 24 GB card, plus a separate 0.93 GB vision projector that none of those numbers include. Every score here is vendor-reported — there is no independent leaderboard result yet, and the video says so on screen. ## Sources - Qwen3.8-27B model card — https://huggingface.co/Qwen/Qwen3.8-27B - Qwen3.8-27B FP8 weights — https://huggingface.co/Qwen/Qwen3.8-27B-FP8 - Qwen launch post — https://x.com/Alibaba_Qwen/status/2088280182356611304 - Qwen benchmark table post — https://x.com/Alibaba_Qwen/status/2088280188362867185 - Unsloth GGUF quants — https://huggingface.co/unsloth/Qwen3.8-27B-GGUF - Unsloth run-locally guide — https://unsloth.ai/docs/models/qwen3.8 - Unsloth launch post — https://x.com/UnslothAI/status/2088281537427235320 - Qwen3.8-Max comparison chart (3 Aug) — https://x.com/UnslothAI/status/2084110664789024769 - Qwen blog — https://qwen.ai/blog?id=qwen3.8

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