DeepSeek V4.1 Flash vs Qwen3.5-0.8B

DeepSeek V4.1 Flash

A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.

Qwen3.5-0.8B

A tiny vision-language cousin for prototypes, fine-tuning and light local steps such as routing, tagging or reading an image, running in under 2 GB in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no hosted option, safety results or answers from Alibaba since March.

DeepSeek V4.1 Flash

1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path "$SAVE_PATH" --config config.json --interactive
2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command
3. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding
4. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4
5. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75
6. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends
7. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update

Qwen3.5-0.8B

1. Smallest start is ollama run qwen3.5:0.8b, or qwen3.5:0.8b-q8_0 at 1.0 GB
2. Full precision vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder
3. Thinking is off by default; if you turn it on with "chat_template_kwargs": {"enable_thinking": true}, stream and cut off runaway output, since the card warns of thinking loops, and add --reasoning-parser qwen3 in vLLM or SGLang to split the reasoning out
4. There's no generation_config.json, so pass the card's sampling yourself, for example temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0 for text without thinking
5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests
6. Validate any JSON it returns, since structured output isn't documented