DeepSeek V4.1 Flash vs Qwen3.8-27B

DeepSeek V4.1 Flash

DeepSeek V4.1 Flash gives a 1M-token context, trained tool calls and an effort budget from 1 to 100 under the MIT licence, with every release kept in its own repository. It needs 510 GB of files and runs in vLLM or mlx-lm, prompts come from DeepSeek's code rather than a template, and no safety results or training sources are published.

Qwen3.8-27B

Qwen3.8-27B runs nearly anywhere, in vLLM, SGLang, llama.cpp, Ollama and MLX, with an 18 GB Ollama build and Apache 2.0 terms. The card leaves out tool calling, hardware needs, training data, limitations and safety results, the files haven't changed since 14 August, and Alibaba answered none of the 40 most recent discussions.

DeepSeek V4.1 Flash

1. Build prompts with encoding.py or deepseek-recipe; the September repository's Jinja template is behind the 16 October encoding changes per an open pull request
2. Parse tool calls from the DSML calls block, which V4.1 writes with a space before calls, invoke and parameter, unlike V4
3. Pass reasoning effort as a number from 1 to 100, or low, high or max (50, 75, 100), in thinking mode
4. Allow 256K or more output tokens at high effort, as the card recommends
5. Use DeepSeek's hosted API (the deepseek-api listing) if you can't hold 510 GB of weights

Qwen3.8-27B

1. Serve it with --tool-call-parser qwen3_coder and --reasoning-parser qwen3; the Hermes parser leaves its tool calls unparsed, per SGLang's cookbook
2. Set enable_thinking: false in chat_template_kwargs for short tasks, since thinking is on by default
3. Use the sampling the card gives for each mode, temperature=1.0, top_p=0.95 thinking and temperature=0.7, top_p=0.8, presence_penalty=1.5 without
4. Turn on YaRN only for contexts beyond 262,144 tokens; the card says static YaRN can cost quality on short texts
5. Check JSON output yourself, since structured output isn't documented for the model