# Qwen3-VL-4B-Instruct vs Ripple-65B Preview (slim)

> Ripple-65B Preview and Qwen3-VL-4B-Instruct score within a point of each other for open-weight models you run yourself, 53.3 and 52.6 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.md (~7,850 tokens) · this version ~1,280 tokens · JSON https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.json · canonical https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-04
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Ripple-65B Preview and Qwen3-VL-4B-Instruct score within a point of each other on agent readiness, 53.3 (D) and 52.6 (D). Qwen3-VL-4B-Instruct leads on reach. Both do open-weight models you run yourself.

- Qwen3-VL-4B-Instruct: D 52.6, rank #105 of 121 open-weight models · https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.min.md
- Ripple-65B Preview: D 53.3, rank #103 of 121 open-weight models · https://www.anchorterminal.com/tools/ripple-65b-preview.min.md
- Qwen3-VL-4B-Instruct, good for An agent with 4 GB or more to spare that needs a local model to read images, documents and video Ahead on Reach, 95 against 45.
- Ripple-65B Preview, good 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 60; Documentation and upkeep, 49 against 31.

| Category | Qwen3-VL-4B-Instruct | Ripple-65B Preview |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 95 | 45 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 60 | 75 |
| Documentation and upkeep (10%) | 31 | 49 |

| Fact (where they differ) | Qwen3-VL-4B-Instruct | Ripple-65B Preview |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Ricursive Intelligence |
| Last release | 2025-11-01 | 2026-11-04 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, Qwen3-VL-4B-Instruct +50: Qwen3-VL-4B-Instruct 95 (Alibaba's own Q4_K_M GGUF with its vision file is 2.95 GB, so with a fifth added it needs about 3.5 GB and fits well within 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama through its pinned llama.cpp build, which reaches the 25 cap (25). Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Only Featherless is live in the Hub's provider mapping, so it isn't hosted too (0). 30+25+15+15+10+0 = 95.); 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-VL-4B-Instruct 31 (The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025 (5). Qwen answered none of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026 (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31.); 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 +15: Qwen3-VL-4B-Instruct 60 (A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn, per the family's technical report (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60.); 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.)