DeepSeek V4.1 Flash vs gpt-oss-120b

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.

gpt-oss-120b

A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.

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

gpt-oss-120b

1. Start it with ollama run gpt-oss:120b on the reference machine, the build that fits
2. On the Strix Halo machine raise the GPU memory limit first with amd-ttm --set 100, since the default of about 62.8 GiB is below the 78 GB it needs
3. With vLLM use vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice per vLLM's recipe, or parse raw output with openai-harmony
4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise
5. Set Reasoning: low in the system message for short tasks, medium is the default
6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users
7. Expect one tool call per turn and loop, don't wait for parallel calls