Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3.5-397B-A17B vs Ripple-65B Preview
Qwen3.5-397B-A17B scores 64.1 (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 access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.
Ahead on
- Reach, 70 against 45
Watch for
No build fits a 256 GB machine, and Ollama has no local build
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 34
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 Qwen3.5-397B-A17B +25
Qwen3.5-397B-A17B 70
Memory scores 0, since even the smallest 4-bit build, LM Studio's MLX 4-bit at 223.9 GB, needs about 269 GB with a fifth added, and Alibaba's own GPTQ-Int4 about 283 GB. Runtimes reach the 25 cap on released llama.cpp, vLLM, SGLang and Transformers and LM Studio's catalogue, though Ollama has no local build. Alibaba's FP8 and GPTQ-Int4 builds give builds 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's Model Studio plus five live providers on Hugging Face give hosted too its 5.
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 +15
Qwen3.5-397B-A17B 34
The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Alibaba answered none of the 40 most recent questions and reports, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and two template reports and a template fix still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.
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
Qwen3.5-397B-A17B 70
The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model.
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 | Qwen3.5-397B-A17B | Ripple-65B Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 70 | 45 | Qwen3.5-397B-A17B +25 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 75 | Ripple-65B Preview +5 |
| Documentation and upkeep | 10%10 | 34 | 49 | Ripple-65B Preview +15 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 64.1/100 | D 53.3/100 |
Facts side by side
| Fact | Qwen3.5-397B-A17B | 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 | 2026-02-16 | 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
Qwen3.5-397B-A17B
Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.
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
Qwen3.5-397B-A17B
- The card's server command is
vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, and the FP8 build halves the memory - It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B)
- On Model Studio pass
"enable_thinking": falsedirectly, while vLLM and SGLang take"chat_template_kwargs": {"enable_thinking": false} - Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without
- For multi-token prediction in vLLM add
--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}' - Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
- Validate any JSON it returns, since structured output isn't documented
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, Qwen3.5-397B-A17B or Ripple-65B Preview?
Qwen3.5-397B-A17B scores 64.1 (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 Qwen3.5-397B-A17B or Ripple-65B Preview
- Agents-A1-4B vs Qwen3.5-397B-A17B
- Agents-A1-4B vs Ripple-65B Preview
- Apertus v1.5 8B vs Qwen3.5-397B-A17B
- Apertus v1.5 8B vs Ripple-65B Preview
- Apodex-1.1-mini vs Qwen3.5-397B-A17B
- Apodex-1.1-mini vs Ripple-65B Preview
- BigBang-v1 vs Qwen3.5-397B-A17B
- BigBang-v1 vs Ripple-65B Preview
- Carbon-X vs Qwen3.5-397B-A17B
- Carbon-X vs Ripple-65B Preview
- Cosmos-Reason2-2B vs Qwen3.5-397B-A17B
- Cosmos-Reason2-2B vs Ripple-65B Preview
- Darwin-36B-Opus vs Qwen3.5-397B-A17B
- Darwin-36B-Opus vs Ripple-65B Preview
- DeepSeek-V3.2 vs Qwen3.5-397B-A17B
- DeepSeek-V3.2 vs Ripple-65B Preview
- DeepSeek V4.1 Flash vs Qwen3.5-397B-A17B
- DeepSeek V4.1 Flash vs Ripple-65B Preview
- DeepSeek-V4-Flash-0731 vs Qwen3.5-397B-A17B
- DeepSeek-V4-Flash-0731 vs Ripple-65B Preview
- DiffusionGemma 26B A4B IT vs Qwen3.5-397B-A17B
- DiffusionGemma 26B A4B IT vs Ripple-65B Preview
- Gemma 4 12B IT vs Qwen3.5-397B-A17B
- Gemma 4 12B IT vs Ripple-65B Preview
- Gemma 4 26B A4B IT vs Qwen3.5-397B-A17B
- Gemma 4 26B A4B IT vs Ripple-65B Preview
- Gemma 4 31B IT vs Qwen3.5-397B-A17B
- Gemma 4 31B IT vs Ripple-65B Preview
- Gemma 4 E2B IT vs Qwen3.5-397B-A17B
- Gemma 4 E2B IT vs Ripple-65B Preview
- Gemma 4 E4B IT vs Qwen3.5-397B-A17B
- Gemma 4 E4B IT vs Ripple-65B Preview
- GLM-4.5-Air vs Qwen3.5-397B-A17B
- GLM-4.5-Air vs Ripple-65B Preview
- GLM-4.7-Flash vs Qwen3.5-397B-A17B
- GLM-4.7-Flash vs Ripple-65B Preview
- GLM-5.3-Flash vs Qwen3.5-397B-A17B
- GLM-5.3-Flash vs Ripple-65B Preview
- GLM-5.3 vs Qwen3.5-397B-A17B
- GLM-5.3 vs Ripple-65B Preview
- gpt-oss-120b vs Qwen3.5-397B-A17B
- gpt-oss-120b vs Ripple-65B Preview
- gpt-oss-20b vs Qwen3.5-397B-A17B
- gpt-oss-20b vs Ripple-65B Preview
- Granite 4.2 3B vs Qwen3.5-397B-A17B
- Granite 4.2 3B vs Ripple-65B Preview
- Hy3 vs Qwen3.5-397B-A17B
- Hy3 vs Ripple-65B Preview
- Inkling-Small vs Qwen3.5-397B-A17B
- Inkling-Small vs Ripple-65B Preview
- Inkling vs Qwen3.5-397B-A17B
- Inkling vs Ripple-65B Preview
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-397B-A17B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ripple-65B Preview
- Kimi K3 vs Qwen3.5-397B-A17B
- Kimi K3 vs Ripple-65B Preview
- Kolibri-1 vs Qwen3.5-397B-A17B
- Kolibri-1 vs Ripple-65B Preview
- Kronos vs Qwen3.5-397B-A17B
- Kronos vs Ripple-65B Preview
- Laguna M.1 vs Qwen3.5-397B-A17B
- Laguna M.1 vs Ripple-65B Preview
- Laguna S 2.1 vs Qwen3.5-397B-A17B
- Laguna S 2.1 vs Ripple-65B Preview
- Laguna XS 2.1 vs Qwen3.5-397B-A17B
- Laguna XS 2.1 vs Ripple-65B Preview
- LBS-27B vs Qwen3.5-397B-A17B
- LBS-27B vs Ripple-65B Preview
- LFM2.5-2.6B vs Qwen3.5-397B-A17B
- LFM2.5-2.6B vs Ripple-65B Preview
- Ling-3.0-tiny vs Qwen3.5-397B-A17B
- Ling-3.0-tiny vs Ripple-65B Preview
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-397B-A17B
- MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview
- MiniCPM-o 4.5 vs Qwen3.5-397B-A17B
- MiniCPM-o 4.5 vs Ripple-65B Preview
- MiniCPM5-2B vs Qwen3.5-397B-A17B
- MiniCPM5-2B vs Ripple-65B Preview
- MiniMax-M2.5 vs Qwen3.5-397B-A17B
- MiniMax-M2.5 vs Ripple-65B Preview
- MiniMax-M2.7 vs Qwen3.5-397B-A17B
- MiniMax-M2.7 vs Ripple-65B Preview
- MiniMax-M3.1-Flash vs Qwen3.5-397B-A17B
- MiniMax-M3.1-Flash vs Ripple-65B Preview
- Mistral Large 4 vs Qwen3.5-397B-A17B
- Mistral Large 4 vs Ripple-65B Preview
- Muse Glimmer 30B vs Qwen3.5-397B-A17B
- Muse Glimmer 30B vs Ripple-65B Preview
- Nanbeige4.3-33B vs Qwen3.5-397B-A17B
- Nanbeige4.3-33B vs Ripple-65B Preview
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-397B-A17B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ripple-65B Preview
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-397B-A17B
- Nemotron 3.5 Lightning 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 30B A3B vs Qwen3.5-397B-A17B
- Nemotron 3 Nano 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 4B vs Qwen3.5-397B-A17B
- Nemotron 3 Nano 4B vs Ripple-65B Preview
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.5-397B-A17B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Ripple-65B Preview
- Nemotron 3 Super 120B A12B vs Qwen3.5-397B-A17B
- Nemotron 3 Super 120B A12B vs Ripple-65B Preview
- Nemotron 3 Ultra 550B A55B vs Qwen3.5-397B-A17B
- Nemotron 3 Ultra 550B A55B vs Ripple-65B Preview
- occamy-1.0 vs Qwen3.5-397B-A17B
- occamy-1.0 vs Ripple-65B Preview
- Olmo 3 7B Think vs Qwen3.5-397B-A17B
- Olmo 3 7B Think vs Ripple-65B Preview
- Ornith-1.5-35B-A3B vs Qwen3.5-397B-A17B
- Ornith-1.5-35B-A3B vs Ripple-65B Preview
- Ornith-1.5-397B vs Qwen3.5-397B-A17B
- Ornith-1.5-397B vs Ripple-65B Preview
- Ornith-1.5-9B vs Qwen3.5-397B-A17B
- Ornith-1.5-9B vs Ripple-65B Preview
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-397B-A17B
- Qwen3-30B-A3B-Instruct-2507 vs Ripple-65B Preview
- Qwen3-4B-Instruct-2507 vs Qwen3.5-397B-A17B
- Qwen3-4B-Instruct-2507 vs Ripple-65B Preview
- Qwen3.5-0.8B vs Qwen3.5-397B-A17B
- Qwen3.5-0.8B vs Ripple-65B Preview
- Qwen3.5-122B-A10B vs Qwen3.5-397B-A17B
- Qwen3.5-122B-A10B vs Ripple-65B Preview
- Qwen3.5-2B vs Qwen3.5-397B-A17B
- Qwen3.5-2B vs Ripple-65B Preview
- Qwen3.5-397B-A17B vs Qwen3.5-4B
- Qwen3.5-397B-A17B vs Qwen3.5-9B
- Qwen3.5-397B-A17B vs Qwen3.6-27B
- Qwen3.5-397B-A17B vs Qwen3.6-35B-A3B
- Qwen3.5-397B-A17B vs Qwen3.8-27B
- Qwen3.5-397B-A17B vs Qwen3.8-Flash-Next
- Qwen3.5-397B-A17B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-Coder-Next
- Qwen3.5-397B-A17B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-2B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-32B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-4B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-8B-Instruct
- Qwen3.5-397B-A17B vs Qwopus3.8-27B-Flash-V2
- Qwen3.5-397B-A17B vs Recursive Flint
- Qwen3.5-397B-A17B vs SmolLM3-3B
- Qwen3.5-397B-A17B vs Spark-X2.6-4B
- Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-397B-A17B vs Baidu Unlimited-OCR-2
- Qwen3.5-4B vs Ripple-65B Preview
- Qwen3.5-9B vs Ripple-65B Preview
- Qwen3.6-27B vs Ripple-65B Preview
- Qwen3.6-35B-A3B vs Ripple-65B Preview
- 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/qwen3-5-397b-a17b-vs-ripple-65b-preview.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-5-397b-a17b.json·/api/v1/tools/ripple-65b-preview.json - From a terminal
anchor compare qwen3-5-397b-a17b ripple-65b-preview(the CLI) - Over MCP
compare_tools {"a": "qwen3-5-397b-a17b", "b": "ripple-65b-preview"}at/mcp, no key