GLM-5.3-Flash vs Qwopus3.8-27B-Flash-V2

GLM-5.3-Flash

An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.

Qwopus3.8-27B-Flash-V2

A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.

GLM-5.3-Flash

1. Start it with vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47 (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)
2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs
3. Pass chat_template_kwargs: {"reasoning_effort": "low"} for short tasks; anything other than low or high falls back to max
4. Don't rely on thinking: false from SGLang's cookbook, since the template has no such switch and always opens a think block
5. Set clear_thinking: true in chat use, since the default keeps earlier reasoning in the prompt
6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai

Qwopus3.8-27B-Flash-V2

1. Quickest start is llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja, about 17 GB, and add mmproj-F32.gguf for images
2. The card's tests used Q5_K_M with the bundled MTP head and --spec-type draft-mtp in llama.cpp
3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder
4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0
5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error
6. Earlier reasoning stays in the context unless preserve_thinking is false
7. Cap max_tokens, since a report says it loops more than V1