DeepSeek V4.1 Flash vs Ripple-65B Preview

DeepSeek V4.1 Flash

• +2.5 on 4 November 2026: product
• -3.8 on 21 October 2026 (B to C): checklist

Ripple-65B Preview

No score change since it was listed.

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 and runs in vLLM, SGLang and llama.cpp. The main risks are prompts built from DeepSeek's code instead of a template, no published safety results and no DeepSeek replies.

Ripple-65B Preview

A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.

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. In llama.cpp, convert with convert_hf_to_gguf.py from b11870 or later, which added the deepseek41 architecture on 25 October 2026; no GGUF is published by DeepSeek
4. 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
5. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4
6. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75
7. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends
8. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update

Ripple-65B Preview

1. Run Ricursive's launcher from inference/server with uv run --script serve.py on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default
2. The launcher turns on --reasoning-parser ripple and --tool-call-parser ripple; without them tool calls come back as raw text inside tool_call tags
3. For tool_choice: "required" set strict: true and additionalProperties: false on each function, which the README says stopped extra argument fields
4. Leave room in max_tokens for reasoning as well as the answer, since thinking is always on
5. Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support