Qwen3.8-27B
An open-source large language model in Alibaba's Qwen series, supporting local deployment with capabilities in code comprehension, reverse engineering, and security analysis.
Core Facts
Qwen3.6-27B supports multimodal capabilities, including text reasoning, image understanding, and video processing.
The Qwen2.5:7B model is approximately 4 GB in size after Q4 quantization.
Qwen3.8-27B delivers impressive performance in specific scenarios such as front-end display, and may be one of the best-performing locally deployed large models currently available, showing a significant capability improvement over the Meta 30B model and Qwen3.6-27B.
Timeline (last 90 days)
On the SWE-Bench Pro benchmark, Nanbeige4.3-33B scores 60.3, slightly below Qwen3.8-27B's 61.7.
On the Terminal-Bench 2.1 benchmark, Nanbeige4.3-33B scores 80.9 and Qwen3.8-27B scores 73.0, both obtained under the same test framework and inference settings.
According to the Reddit community citing the Hugging Face model card, Nanbeige4.3-33B outperforms Qwen3.8-27B on a series of benchmarks.
The test is based on the Qwen3.8-27B model, using EXL3 4.0bpw quantization, a DFlash2 block drafter (EXL3 4bpw, 7-token draft), greedy decoding, running on 2 T4 GPUs.
GVS5H claims that five Qwen3.8-27B instances working in coordination match Fable 5 on LiveCodeBench Hard.
The cost of a single 27B model is far lower than that of top-tier closed-source models.
Running the same problem with five 27B instances costs roughly five times the inference cost of a single instance.
The original post provided no technical details, including the multi-model fusion method and the GVS5H Harness configuration.
Until the configuration of the GVS5H Harness and the details of the comparison baseline are made public, the claim that five Qwen3.8-27B match Fable 5 should be treated as a hypothesis awaiting verification rather than an established fact.
A post appeared on Hacker News whose title claims that five Qwen3.8-27B models match Fable 5 with the help of the GVS5H Harness, and the post received 60 upvotes and 15 comments.
40 more timeline events
All Facts (20)
Qwen3.6-27B supports multimodal capabilities, including text reasoning, image understanding, and video processing.
80%VerifiedThe Qwen2.5:7B model is approximately 4 GB in size after Q4 quantization.
80%VerifiedQwen3.8-27B delivers impressive performance in specific scenarios such as front-end display, and may be one of the best-performing locally deployed large models currently available, showing a significant capability improvement over the Meta 30B model and Qwen3.6-27B.
75%VerifiedReasoning budget refers to the upper limit on the number of intermediate reasoning tokens a chain-of-thought model is allowed to consume before generating a final answer; the higher the budget, the deeper the self-reflection and multi-step verification the model can perform.
75%VerifiedQwen3.8-27B is licensed under Apache 2.0, fully free for commercial use, with no additional commercial use restrictions.
65%VerifiedQwen3.8-27B adopts open weights and the Apache 2.0 license.
65%VerifiedQwen3.8-27B is an open-source model with 27 billion parameters.
65%VerifiedQwen3.8-27B enables Thinking mode by default, with reasoning depth available in three levels: Low, Medium, and X-High.
65%VerifiedA 27B-parameter model can run in quantized form on a consumer-grade GPU with 24GB of VRAM (such as the NVIDIA RTX 4090), without requiring data-center-level hardware.
65%VerifiedThe higher the reasoning effort level, the stronger the reasoning capability but the faster token consumption, representing a trade-off between cost and intelligence.
65%VerifiedThe Unsloth team released a new Qwen3.8-27B GGUF model based on the Dynamic v3 quantization scheme, achieving an additional 10% improvement in top-1% accuracy while maintaining the same model size.
85%UnverifiedLlama 3.1 405B's score on MMLU is already close to the level of GPT-4o, and Qwen 2.5 72B has surpassed GPT-4 Turbo on certain metrics in math and code generation benchmarks.
60%UnverifiedThe Qwen 3.8 Max 27B version is a traditional dense model, suitable for high-performance desktops or servers.
60%UnverifiedQwen3.8-27B trails Claude Opus 4.6 on Terminal Bench, GPQA Diamond, and HLE benchmarks (73 vs 78.2, 89.2 vs 91.3, and 30.8 vs 40, respectively).
60%UnverifiedLocal inference is constrained by the user's hardware: a 7B-parameter quantized model (such as the Q4 version of Llama 3 8B) has a file size of approximately 4–5 GB, can reach 20–40 tokens per second on a Mac with an Apple M-series chip, and may drop to 3–5 tokens per second on a laptop with only an integrated GPU and 8 GB of RAM.
60%UnverifiedThe original Qwen3 model supports a 256K context, but local deployment is limited to 128K due to VRAM constraints
60%UnverifiedOn the Terminal-Bench 2.1 benchmark, Nanbeige4.3-33B scores 80.9 and Qwen3.8-27B scores 73.0, both obtained under the same test framework and inference settings.
50%UnverifiedOn the SWE-Bench Pro benchmark, Nanbeige4.3-33B scores 60.3, slightly below Qwen3.8-27B's 61.7.
50%UnverifiedAccording to the Reddit community citing the Hugging Face model card, Nanbeige4.3-33B outperforms Qwen3.8-27B on a series of benchmarks.
50%UnverifiedThe test is based on the Qwen3.8-27B model, using EXL3 4.0bpw quantization, a DFlash2 block drafter (EXL3 4bpw, 7-token draft), greedy decoding, running on 2 T4 GPUs.
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