DeepSeek V4.1 Flash vs Kolibri-1

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.

Kolibri-1

Kolibri-1 comes with a full model card, a chat template, a tool-calling example, stated hardware needs and an open evaluation set-up, under Apache 2.0 with no acceptable use policy. It runs through Aleph Alpha's vLLM plugin or Transformers 5.20.0, about 78 GB at FP8, and no safety results are published.

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

Kolibri-1

1. Serve it with --tool-call-parser kolibri1 --reasoning-parser kolibri1 --enable-auto-tool-choice, or tool calls come back as text
2. Install aleph-alpha-inference for vLLM 0.29, or use a Transformers release from 5.20.0
3. Set chat_template_kwargs to {"enable_thinking": false} or reasoning_effort: "none" for short tasks, since thinking is on by default
4. Keep contexts to 262,144 tokens unless you need more, as Aleph Alpha recommends, and pass the config override to go further
5. Validate tool results in your own code, since the card says the trained safeguards don't replace application-level checks