# InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ripple-65B Preview

> Ripple-65B Preview and InternVL3.5-GPT-OSS-20B-A4B-Preview score within a point of each other for open-weight models you run yourself, 53.3 and 53.2 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ripple-65b-preview
- Markdown: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ripple-65b-preview.md (~8,650 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-04

Ripple-65B Preview and InternVL3.5-GPT-OSS-20B-A4B-Preview score within a point of each other on agent readiness, 53.3 (D) and 53.2 (D). InternVL3.5-GPT-OSS-20B-A4B-Preview leads on reach. Both do open-weight models you run yourself.

- InternVL3.5-GPT-OSS-20B-A4B-Preview: grade D, 53.2/100, rank #104 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json
- Ripple-65B Preview: grade D, 53.3/100, rank #103 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/ripple-65b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3674 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### InternVL3.5-GPT-OSS-20B-A4B-Preview (D)

Good for: Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.

Ahead on:
- Reach, 65 against 45

Watch for: No quantised build from OpenGVLab, so about 51 GB at BF16

### Ripple-65B Preview (D)

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 45
- Documentation and upkeep, 49 against 28

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

### Agent interface: Ripple-65B Preview +30

- InternVL3.5-GPT-OSS-20B-A4B-Preview (45): Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.
- 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.

### Documentation and upkeep: Ripple-65B Preview +21

- InternVL3.5-GPT-OSS-20B-A4B-Preview (28): Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.
- 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.

### Reach: InternVL3.5-GPT-OSS-20B-A4B-Preview +20

- InternVL3.5-GPT-OSS-20B-A4B-Preview (65): Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.
- 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.

## Score by category

| Category | Weight | InternVL3.5-GPT-OSS-20B-A4B-Preview | Ripple-65B Preview | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 45 | InternVL3.5-GPT-OSS-20B-A4B-Preview +20 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 45 | 75 | Ripple-65B Preview +30 |
| Documentation and upkeep | 10% (10 this run) | 28 | 49 | Ripple-65B Preview +21 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **53.2 · D** | **53.3 · D** | |

## Facts side by side

| Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | Ripple-65B Preview |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenGVLab (Shanghai AI Laboratory) | Ricursive Intelligence |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-09-08 | 2026-11-04 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 1.8/5 (4) | none |

## How the scores have moved

### InternVL3.5-GPT-OSS-20B-A4B-Preview

- +2.0 on 4 November 2026: correction

### Ripple-65B Preview

No score change since it was listed.

## Verdicts

**InternVL3.5-GPT-OSS-20B-A4B-Preview.** An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.

**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

### InternVL3.5-GPT-OSS-20B-A4B-Preview

1. Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM
2. Check the rendered prompt before relying on it, since the shipped template's <|im_start|> markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative
3. Use Transformers 4.55.0 or later, as the card requires for this size
4. For thinking, send the card's thinking system prompt and parse the reasoning out of the <think> tags, with temperature 0.6 and sampling on as the card advises
5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says

### 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, InternVL3.5-GPT-OSS-20B-A4B-Preview or Ripple-65B Preview?

Ripple-65B Preview and InternVL3.5-GPT-OSS-20B-A4B-Preview score within a point of each other on agent readiness, 53.3 (D) and 53.2 (D). InternVL3.5-GPT-OSS-20B-A4B-Preview leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ripple-65b-preview.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "internvl3-5-gpt-oss-20b-a4b-preview", "b": "ripple-65b-preview"}`. From a terminal: `anchor compare internvl3-5-gpt-oss-20b-a4b-preview ripple-65b-preview`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json

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- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.md)
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- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-8-flash-next.md)
- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-coder-30b-a3b-instruct.md)
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- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs SmolLM3-3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-smollm3-3b.md)
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- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-27b.md)
- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-flash-next.md)
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- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-unlimited-ocr-2.md)
- [Kimi K3 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kimi-k3-vs-ripple-65b-preview.md)
- [Kolibri-1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kolibri-1-vs-ripple-65b-preview.md)
- [Kronos vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kronos-vs-ripple-65b-preview.md)
- [Laguna M.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-m-1-vs-ripple-65b-preview.md)
- [Laguna S 2.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ripple-65b-preview.md)
- [Laguna XS 2.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ripple-65b-preview.md)
- [LBS-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview.md)
- [LFM2.5-2.6B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ripple-65b-preview.md)
- [Ling-3.0-tiny vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ripple-65b-preview.md)
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- [MiniCPM-o 4.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ripple-65b-preview.md)
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- [MiniMax-M2.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ripple-65b-preview.md)
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- [MiniMax-M3.1-Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ripple-65b-preview.md)
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- [Muse Glimmer 30B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ripple-65b-preview.md)
- [Nanbeige4.3-33B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ripple-65b-preview.md)
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- [Nemotron 3 Nano 30B A3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ripple-65b-preview.md)
- [Nemotron 3 Nano 4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ripple-65b-preview.md)
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- [Nemotron 3 Super 120B A12B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ripple-65b-preview.md)
- [Nemotron 3 Ultra 550B A55B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ripple-65b-preview.md)
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- [Ornith-1.5-35B-A3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ripple-65b-preview.md)
- [Ornith-1.5-397B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ripple-65b-preview.md)
- [Ornith-1.5-9B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-ripple-65b-preview.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-ripple-65b-preview.md)
- [Qwen3-4B-Instruct-2507 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-ripple-65b-preview.md)
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- [Qwen3.5-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-ripple-65b-preview.md)
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- [Qwen3.5-4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-ripple-65b-preview.md)
- [Qwen3.5-9B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview.md)
- [Qwen3.6-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview.md)
- [Qwen3.6-35B-A3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-ripple-65b-preview.md)
- [Qwen3.8-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-ripple-65b-preview.md)
- [Qwen3.8-Flash-Next vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview.md)
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- [Qwen3-VL-4B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.md)
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- [Recursive Flint vs Ripple-65B Preview](https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview.md)
- [Ripple-65B Preview vs SmolLM3-3B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.md)
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- [Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next.md)
- [Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.md)
- [Ripple-65B Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.md)

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