Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3.6-35B-A3B vs Ripple-65B Preview
Qwen3.6-35B-A3B scores 62.7 (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
Best for Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.
Ahead on
- Reach, 85 against 45
Watch for
No safety evaluation, no agent-use guidance and no way to report a security flaw
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 Qwen3.6-35B-A3B +40
Qwen3.6-35B-A3B 85
Memory earns 20 because the smallest 4-bit build from a runtime's own library is Ollama's Q4_K_M at 23 GB, which comes to 27.6 GB with the fifth added, so it needs a 32 GB machine rather than a 24 GB one. Runtimes earn the full 25, since released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the architecture. Builds earn 10 for Alibaba's one quantised build, FP8, with no 4-bit or GGUF from the publisher. Terms earn 15 for plain Apache 2.0, download earns 10 for ungated files, and hosted earns 5 because DeepInfra, Featherless AI and Scaleway serve it through Hugging Face.
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
Qwen3.6-35B-A3B 31
The model card earns 11, covering intended use, architecture, parameters and context but not limitations, languages or a cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 5, a README edit on 24 April 2026, 192 days before this check, past 180 days and within a year, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness earns 0, with no Qwen reply in the 40 most recent of 62 discussions that aren't pull requests, recounted on 2 November. Version policy earns 5, the earlier version kept in its own repository without a note. File health earns 5, the files agree but reports about tool calling and the template wait unanswered.
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
Qwen3.6-35B-A3B 70
The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 10, because the card names the qwen3_coder parser but leaves the return format to the template. Trained tool calling earns 20, with several calls to a turn in the template. Structured output earns 0, as JSON output isn't documented for the model. Reasoning control earns 10, thinking off per request with enable_thinking and the reasoning returned in think tags for the qwen3 parser. Context earns 10 for 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
| Category | Weight this run | Qwen3.6-35B-A3B | Ripple-65B Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 45 | Qwen3.6-35B-A3B +40 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 75 | Ripple-65B Preview +5 |
| Documentation and upkeep | 10%10 | 31 | 49 | Ripple-65B Preview +18 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 62.7/100 | D 53.3/100 |
Facts side by side
| Fact | Qwen3.6-35B-A3B | Ripple-65B Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | Ricursive Intelligence |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-04-16 | 2026-11-04 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
How the scores have moved
Ripple-65B Preview
No score change since it was listed.
Verdicts
Qwen3.6-35B-A3B
A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.
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
Qwen3.6-35B-A3B
- Start it with
ollama run qwen3.6:35b(Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory) - On the reference machine use
vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, BF16, since vLLM doesn't document FP8 compute on gfx1151 - Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later
- Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning
- Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8
- Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens
- Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none
Ripple-65B Preview
- Run Ricursive's launcher from inference/server with
uv run --script serve.pyon two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default - The launcher turns on
--reasoning-parser rippleand--tool-call-parser ripple; without them tool calls come back as raw text inside tool_call tags - For
tool_choice: "required"setstrict: trueandadditionalProperties: falseon each function, which the README says stopped extra argument fields - Leave room in
max_tokensfor reasoning as well as the answer, since thinking is always on - Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support
Questions
Which is better for AI agents, Qwen3.6-35B-A3B or Ripple-65B Preview?
Qwen3.6-35B-A3B scores 62.7 (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 Qwen3.6-35B-A3B or Ripple-65B Preview
- Agents-A1-4B vs Qwen3.6-35B-A3B
- Agents-A1-4B vs Ripple-65B Preview
- Apertus v1.5 8B vs Qwen3.6-35B-A3B
- Apertus v1.5 8B vs Ripple-65B Preview
- Apodex-1.1-mini vs Qwen3.6-35B-A3B
- Apodex-1.1-mini vs Ripple-65B Preview
- BigBang-v1 vs Qwen3.6-35B-A3B
- BigBang-v1 vs Ripple-65B Preview
- Carbon-X vs Qwen3.6-35B-A3B
- Carbon-X vs Ripple-65B Preview
- Cosmos-Reason2-2B vs Qwen3.6-35B-A3B
- Cosmos-Reason2-2B vs Ripple-65B Preview
- Darwin-36B-Opus vs Qwen3.6-35B-A3B
- Darwin-36B-Opus vs Ripple-65B Preview
- DeepSeek-V3.2 vs Qwen3.6-35B-A3B
- DeepSeek-V3.2 vs Ripple-65B Preview
- DeepSeek V4.1 Flash vs Qwen3.6-35B-A3B
- DeepSeek V4.1 Flash vs Ripple-65B Preview
- DeepSeek-V4-Flash-0731 vs Qwen3.6-35B-A3B
- DeepSeek-V4-Flash-0731 vs Ripple-65B Preview
- DiffusionGemma 26B A4B IT vs Qwen3.6-35B-A3B
- DiffusionGemma 26B A4B IT vs Ripple-65B Preview
- Gemma 4 12B IT vs Qwen3.6-35B-A3B
- Gemma 4 12B IT vs Ripple-65B Preview
- Gemma 4 26B A4B IT vs Qwen3.6-35B-A3B
- Gemma 4 26B A4B IT vs Ripple-65B Preview
- Gemma 4 31B IT vs Qwen3.6-35B-A3B
- Gemma 4 31B IT vs Ripple-65B Preview
- Gemma 4 E2B IT vs Qwen3.6-35B-A3B
- Gemma 4 E2B IT vs Ripple-65B Preview
- Gemma 4 E4B IT vs Qwen3.6-35B-A3B
- Gemma 4 E4B IT vs Ripple-65B Preview
- GLM-4.5-Air vs Qwen3.6-35B-A3B
- GLM-4.5-Air vs Ripple-65B Preview
- GLM-4.7-Flash vs Qwen3.6-35B-A3B
- GLM-4.7-Flash vs Ripple-65B Preview
- GLM-5.3-Flash vs Qwen3.6-35B-A3B
- GLM-5.3-Flash vs Ripple-65B Preview
- GLM-5.3 vs Qwen3.6-35B-A3B
- GLM-5.3 vs Ripple-65B Preview
- gpt-oss-120b vs Qwen3.6-35B-A3B
- gpt-oss-120b vs Ripple-65B Preview
- gpt-oss-20b vs Qwen3.6-35B-A3B
- gpt-oss-20b vs Ripple-65B Preview
- Granite 4.2 3B vs Qwen3.6-35B-A3B
- Granite 4.2 3B vs Ripple-65B Preview
- Hy3 vs Qwen3.6-35B-A3B
- Hy3 vs Ripple-65B Preview
- Inkling-Small vs Qwen3.6-35B-A3B
- Inkling-Small vs Ripple-65B Preview
- Inkling vs Qwen3.6-35B-A3B
- Inkling vs Ripple-65B Preview
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.6-35B-A3B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ripple-65B Preview
- Kimi K3 vs Qwen3.6-35B-A3B
- Kimi K3 vs Ripple-65B Preview
- Kolibri-1 vs Qwen3.6-35B-A3B
- Kolibri-1 vs Ripple-65B Preview
- Kronos vs Qwen3.6-35B-A3B
- Kronos vs Ripple-65B Preview
- Laguna M.1 vs Qwen3.6-35B-A3B
- Laguna M.1 vs Ripple-65B Preview
- Laguna S 2.1 vs Qwen3.6-35B-A3B
- Laguna S 2.1 vs Ripple-65B Preview
- Laguna XS 2.1 vs Qwen3.6-35B-A3B
- Laguna XS 2.1 vs Ripple-65B Preview
- LBS-27B vs Qwen3.6-35B-A3B
- LBS-27B vs Ripple-65B Preview
- LFM2.5-2.6B vs Qwen3.6-35B-A3B
- LFM2.5-2.6B vs Ripple-65B Preview
- Ling-3.0-tiny vs Qwen3.6-35B-A3B
- Ling-3.0-tiny vs Ripple-65B Preview
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.6-35B-A3B
- MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview
- MiniCPM-o 4.5 vs Qwen3.6-35B-A3B
- MiniCPM-o 4.5 vs Ripple-65B Preview
- MiniCPM5-2B vs Qwen3.6-35B-A3B
- MiniCPM5-2B vs Ripple-65B Preview
- MiniMax-M2.5 vs Qwen3.6-35B-A3B
- MiniMax-M2.5 vs Ripple-65B Preview
- MiniMax-M2.7 vs Qwen3.6-35B-A3B
- MiniMax-M2.7 vs Ripple-65B Preview
- MiniMax-M3.1-Flash vs Qwen3.6-35B-A3B
- MiniMax-M3.1-Flash vs Ripple-65B Preview
- Mistral Large 4 vs Qwen3.6-35B-A3B
- Mistral Large 4 vs Ripple-65B Preview
- Muse Glimmer 30B vs Qwen3.6-35B-A3B
- Muse Glimmer 30B vs Ripple-65B Preview
- Nanbeige4.3-33B vs Qwen3.6-35B-A3B
- Nanbeige4.3-33B vs Ripple-65B Preview
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.6-35B-A3B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ripple-65B Preview
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-35B-A3B
- Nemotron 3.5 Lightning 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 30B A3B vs Qwen3.6-35B-A3B
- Nemotron 3 Nano 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 4B vs Qwen3.6-35B-A3B
- Nemotron 3 Nano 4B vs Ripple-65B Preview
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.6-35B-A3B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Ripple-65B Preview
- Nemotron 3 Super 120B A12B vs Qwen3.6-35B-A3B
- Nemotron 3 Super 120B A12B vs Ripple-65B Preview
- Nemotron 3 Ultra 550B A55B vs Qwen3.6-35B-A3B
- Nemotron 3 Ultra 550B A55B vs Ripple-65B Preview
- occamy-1.0 vs Qwen3.6-35B-A3B
- occamy-1.0 vs Ripple-65B Preview
- Olmo 3 7B Think vs Qwen3.6-35B-A3B
- Olmo 3 7B Think vs Ripple-65B Preview
- Ornith-1.5-35B-A3B vs Qwen3.6-35B-A3B
- Ornith-1.5-35B-A3B vs Ripple-65B Preview
- Ornith-1.5-397B vs Qwen3.6-35B-A3B
- Ornith-1.5-397B vs Ripple-65B Preview
- Ornith-1.5-9B vs Qwen3.6-35B-A3B
- Ornith-1.5-9B vs Ripple-65B Preview
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-35B-A3B
- Qwen3-30B-A3B-Instruct-2507 vs Ripple-65B Preview
- Qwen3-4B-Instruct-2507 vs Qwen3.6-35B-A3B
- Qwen3-4B-Instruct-2507 vs Ripple-65B Preview
- Qwen3.5-0.8B vs Qwen3.6-35B-A3B
- Qwen3.5-0.8B vs Ripple-65B Preview
- Qwen3.5-122B-A10B vs Qwen3.6-35B-A3B
- Qwen3.5-122B-A10B vs Ripple-65B Preview
- Qwen3.5-2B vs Qwen3.6-35B-A3B
- Qwen3.5-2B vs Ripple-65B Preview
- Qwen3.5-397B-A17B vs Qwen3.6-35B-A3B
- Qwen3.5-397B-A17B vs Ripple-65B Preview
- Qwen3.5-4B vs Qwen3.6-35B-A3B
- Qwen3.5-4B vs Ripple-65B Preview
- Qwen3.5-9B vs Qwen3.6-35B-A3B
- Qwen3.5-9B vs Ripple-65B Preview
- Qwen3.6-27B vs Qwen3.6-35B-A3B
- Qwen3.6-27B vs Ripple-65B Preview
- Qwen3.6-35B-A3B vs Qwen3.8-27B
- Qwen3.6-35B-A3B vs Qwen3.8-Flash-Next
- Qwen3.6-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-Coder-Next
- Qwen3.6-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-2B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-32B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-4B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-8B-Instruct
- Qwen3.6-35B-A3B vs Qwopus3.8-27B-Flash-V2
- Qwen3.6-35B-A3B vs Recursive Flint
- Qwen3.6-35B-A3B vs SmolLM3-3B
- Qwen3.6-35B-A3B vs Spark-X2.6-4B
- Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B
- Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.6-35B-A3B vs Baidu Unlimited-OCR-2
- Qwen3.8-27B vs Ripple-65B Preview
- Qwen3.8-Flash-Next vs Ripple-65B Preview
- Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-Coder-Next vs Ripple-65B Preview
- Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-VL-2B-Instruct vs Ripple-65B Preview
- Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-VL-32B-Instruct vs Ripple-65B Preview
- Qwen3-VL-4B-Instruct vs Ripple-65B Preview
- Qwen3-VL-8B-Instruct vs Ripple-65B Preview
- Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview
- Recursive Flint vs Ripple-65B Preview
- Ripple-65B Preview vs SmolLM3-3B
- Ripple-65B Preview vs Spark-X2.6-4B
- Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B
- Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next
- Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)
- Ripple-65B Preview vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/qwen3-6-35b-a3b-vs-ripple-65b-preview.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-6-35b-a3b.json·/api/v1/tools/ripple-65b-preview.json - From a terminal
anchor compare qwen3-6-35b-a3b ripple-65b-preview(the CLI) - Over MCP
compare_tools {"a": "qwen3-6-35b-a3b", "b": "ripple-65b-preview"}at/mcp, no key