# Nemotron 3 Nano 30B A3B vs Ripple-65B Preview

> Nemotron 3 Nano 30B A3B scores 67.6 (B) to Ripple-65B Preview's 53.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ripple-65b-preview
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ripple-65b-preview.md (~8,750 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

Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category. Both do open-weight models you run yourself.

- Nemotron 3 Nano 30B A3B: grade B, 67.6/100, rank #12 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.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

### Nemotron 3 Nano 30B A3B (B)

Good for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.

Ahead on:
- Reach, 90 against 45
- Risk, 60 against 55
- Agent interface, 90 against 75
- Documentation and upkeep, 80 against 49

Watch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository

### 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.

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

### Reach: Nemotron 3 Nano 30B A3B +45

- Nemotron 3 Nano 30B A3B (90): Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today.
- 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: Nemotron 3 Nano 30B A3B +31

- Nemotron 3 Nano 30B A3B (80): The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for 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: Nemotron 3 Nano 30B A3B +15

- Nemotron 3 Nano 30B A3B (90): The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling.
- 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.

### Risk: Nemotron 3 Nano 30B A3B +5

- Nemotron 3 Nano 30B A3B (60): File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins.
- Ripple-65B Preview (55): File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw.

## Score by category

| Category | Weight | Nemotron 3 Nano 30B A3B | Ripple-65B Preview | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 45 | Nemotron 3 Nano 30B A3B +45 |
| Risk | 20% (20 this run) | 60 | 55 | Nemotron 3 Nano 30B A3B +5 |
| Agent interface | 10% (10 this run) | 90 | 75 | Nemotron 3 Nano 30B A3B +15 |
| Documentation and upkeep | 10% (10 this run) | 80 | 49 | Nemotron 3 Nano 30B A3B +31 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **67.6 · B** | **53.3 · D** | |

## Facts side by side

| Fact | Nemotron 3 Nano 30B A3B | Ripple-65B Preview |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Ricursive Intelligence |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-24 | 2026-11-04 |
| Terms last updated | 2025-12-15 | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | not found in the text | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | not found in the text | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | 3.3/5 (4) | none |

## How the scores have moved

### Nemotron 3 Nano 30B A3B

- +0.5 on 1 November 2026: correction

### Ripple-65B Preview

No score change since it was listed.

## Verdicts

**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.

**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

### Nemotron 3 Nano 30B A3B

1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)
2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`
3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151
4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text
5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning
6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks
7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens
8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3
9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution
10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model

### 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, Nemotron 3 Nano 30B A3B or Ripple-65B Preview?

Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ripple-65b-preview.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-30b-a3b", "b": "ripple-65b-preview"}`. From a terminal: `anchor compare nemotron-3-nano-30b-a3b ripple-65b-preview`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json

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- [Nemotron 3 Nano 30B A3B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-olmo-3-7b-think.md)
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- [Nemotron 3 Nano 30B A3B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-0-8b.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-122b-a10b.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-2b.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-397b-a17b.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-4b.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-27b.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-35b-a3b.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-27b.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-flash-next.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-coder-30b-a3b-instruct.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-coder-next.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-omni-30b-a3b-instruct.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-2b-instruct.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-32b-instruct.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-4b-instruct.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-8b-instruct.md)
- [Nemotron 3 Nano 30B A3B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwopus3-8-27b-flash-v2.md)
- [Nemotron 3 Nano 30B A3B vs Recursive Flint](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint.md)
- [Nemotron 3 Nano 30B A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-smollm3-3b.md)
- [Nemotron 3 Nano 30B A3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-spark-x2-6-4b.md)
- [Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b.md)
- [Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Nemotron 3 Nano 30B A3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b.md)
- [Nemotron 3 Nano 30B A3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-unlimited-ocr-2.md)
- [Nemotron 3 Nano 4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ripple-65b-preview.md)
- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ripple-65b-preview.md)
- [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)
- [occamy-1.0 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/occamy-1-0-vs-ripple-65b-preview.md)
- [Olmo 3 7B Think vs Ripple-65B Preview](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ripple-65b-preview.md)
- [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)
- [Qwen3.5-0.8B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-ripple-65b-preview.md)
- [Qwen3.5-122B-A10B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.md)
- [Qwen3.5-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-ripple-65b-preview.md)
- [Qwen3.5-397B-A17B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-ripple-65b-preview.md)
- [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)
- [Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview.md)
- [Qwen3-Coder-Next vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview.md)
- [Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-ripple-65b-preview.md)
- [Qwen3-VL-2B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview.md)
- [Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview.md)
- [Qwen3-VL-32B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview.md)
- [Qwen3-VL-4B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.md)
- [Qwen3-VL-8B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview.md)
- [Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview.md)
- [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)
- [Ripple-65B Preview vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b.md)
- [Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b.md)
- [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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Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp