# Agents-A1-4B vs Ripple-65B Preview

> Agents-A1-4B scores 61 (C) 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/agents-a1-4b-vs-ripple-65b-preview
- Markdown: https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.md (~7,600 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

Agents-A1-4B scores 61 (C) 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.

- Agents-A1-4B: grade C, 61/100, rank #39 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.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

### Agents-A1-4B (C)

Good for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence

Ahead on:
- Reach, 90 against 45

Watch for: The base isn't declared, and the technical report doesn't cover the 4B

### 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 70
- Documentation and upkeep, 49 against 43

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: Agents-A1-4B +45

- Agents-A1-4B (90): Memory 30, InternScience's Q4_K_M GGUF with its vision projector is 3.4 GB and needs about 4.1 GB with the fifth added. Runtimes 20, the Qwen3.5 architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, InternScience publishes FP8 and three GGUF precisions. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, one inference provider serves it and the line needs two.
- 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 +6

- Agents-A1-4B (43): Model card 8, it covers intended use, parameters and context but not the architecture, limitations, languages or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 10, the files went up on 14 July 2026, 112 days before 3 November 2026, and the GitHub repository's last commit is 16 July. Responsiveness 10, InternScience answered one of the three discussions, unchanged on 3 November 2026. Version policy 5, a first release. File health 5, the files agree on the essentials, but the config declares a prediction layer that isn't shipped and the template carries a dated default prompt.
- 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

- Agents-A1-4B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser but the format is only in the template. Trained tool calling 20, the card says it was trained for tool use and the template renders several calls in one turn. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card's reasoning parser returns it apart from the answer. Context 10, 262,144 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.

## Score by category

| Category | Weight | Agents-A1-4B | Ripple-65B Preview | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 45 | Agents-A1-4B +45 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 70 | 75 | Ripple-65B Preview +5 |
| Documentation and upkeep | 10% (10 this run) | 43 | 49 | Ripple-65B Preview +6 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **53.3 · D** | |

## Facts side by side

| Fact | Agents-A1-4B | Ripple-65B Preview |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | InternScience (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 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-14 | 2026-11-04 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3/5 (4) | none |

## Verdicts

**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.

**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

### Agents-A1-4B

1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector
2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work
3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14
4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them
5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention
6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding
7. Validate JSON output yourself, since structured output isn't documented

### 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, Agents-A1-4B or Ripple-65B Preview?

Agents-A1-4B scores 61 (C) 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.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "agents-a1-4b", "b": "ripple-65b-preview"}`. From a terminal: `anchor compare agents-a1-4b ripple-65b-preview`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json

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- [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)
- [MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ripple-65b-preview.md)
- [MiniCPM-o 4.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ripple-65b-preview.md)
- [MiniCPM5-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm5-2b-vs-ripple-65b-preview.md)
- [MiniMax-M2.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ripple-65b-preview.md)
- [MiniMax-M2.7 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview.md)
- [MiniMax-M3.1-Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ripple-65b-preview.md)
- [Mistral Large 4 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/mistral-large-4-vs-ripple-65b-preview.md)
- [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)
- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-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)
- [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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