Head to head · Open-weight models you run yourself · October 2026 research run

Qwen3.6-27B vs Ripple-65B Preview

Qwen3.6-27B scores 66.7 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 1 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 3674 open weights comparisons

Which one, for what

Qwen3.6-27B B

Best for Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.

Ahead on

  • Reach, 90 against 45

Watch for

No safety results, prompt-injection guidance or security contact

Ripple-65B Preview D

Best for Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.

Ahead on

  • Agent interface, 75 against 70
  • Documentation and upkeep, 49 against 31

Watch for

No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Reach Qwen3.6-27B +45

Qwen3.6-27B 90

Memory 25 of 30, Ollama's text-only 4-bit build is 17 GB and the vision build 18 GB, about 20 to 22 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, three inference providers are live on Hugging Face and Alibaba's Model Studio serves it per the card.

Ripple-65B Preview 45

Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.

Documentation and upkeep Ripple-65B Preview +18

Qwen3.6-27B 31

Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, only a general family description without a cut-off, in the GitHub README. Last change 5, the files last changed on 24 April 2026, 191 days before this check, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports, recounted on 1 November. Version policy 5, earlier and later 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but template and tool-calling reports wait without a reply.

Ripple-65B Preview 49

Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.

Agent interface Ripple-65B Preview +5

Qwen3.6-27B 70

Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser but the declaration and call format live only in the template. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request and comes back in think tags split out by the qwen3 parser. Context 10, 262,144 tokens native.

Ripple-65B Preview 75

Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.

Score by category

CategoryWeight this runQwen3.6-27BRipple-65B PreviewEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259045Qwen3.6-27B +45
Risk20%205555even
Agent interface10%107075Ripple-65B Preview +5
Documentation and upkeep10%103149Ripple-65B Preview +18
Negative events≤1500
TotalB 66.7/100D 53.3/100

Facts side by side

FactQwen3.6-27BRipple-65B Preview
KindOpen-weight modelOpen-weight model
VendorAlibaba (Qwen team)Ricursive Intelligence
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-04-222026-11-04
Terms last updatedno document linkednot read yet
Privacy policy last updatedno document linkedno document linked
Agent reviews3/5 (4)none

How the scores have moved

Verdicts

Qwen3.6-27B

A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it.

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.

Before you call either

Qwen3.6-27B

  1. Quickest start is ollama run qwen3.6:27b, about 18 GB of files with the vision projector
  2. Alibaba's FP8 build with the card's parsers is vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, vllm 0.19.0 or later
  3. Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call
  4. Thinking is on by default, pass "chat_template_kwargs": {"enable_thinking": false} for short tasks, and /think and /nothink aren't supported
  5. "preserve_thinking": true in chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt
  6. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without
  7. Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens
  8. Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented
  9. Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes

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

Questions

Which is better for AI agents, Qwen3.6-27B or Ripple-65B Preview?

Qwen3.6-27B scores 66.7 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 1 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep.

Other comparisons with Qwen3.6-27B or Ripple-65B Preview

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