# LFM3-Audio-2B vs Ripple-65B Preview

> LFM3-Audio-2B 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/lfm3-audio-2b-vs-ripple-65b-preview
- Markdown: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ripple-65b-preview.md (~9,350 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: 2026-11-10

LFM3-Audio-2B scores 61 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 3 of 4 scored categories. Ripple-65B Preview leads on risk. Both do open-weight models you run yourself.

- LFM3-Audio-2B: grade C, 61/100, rank #70 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-audio-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json
- Ripple-65B Preview: grade D, 53.3/100, rank #135 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### LFM3-Audio-2B (C)

Good for: Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model

Ahead on:
- Reach, 80 against 45
- Agent interface, 85 against 75

Watch for: Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang

### 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:
- Risk, 55 against 40

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: LFM3-Audio-2B +35

- LFM3-Audio-2B (80): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 audio projector and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example that starts this model), and Ollama's library has an lfm3-audio entry. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-audio-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.
- 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.

### Risk: Ripple-65B Preview +15

- LFM3-Audio-2B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.
- 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.

### Agent interface: LFM3-Audio-2B +10

- LFM3-Audio-2B (85): Chat template 20, chat_template.jinja in the repository (9,328 characters), with roles, audio, tools, deferred tools and tool results. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card names turning spoken requests into tool calls as one of its four uses. Structured output 5, the card says the model is designed for structured outputs without saying how to ask for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, the card documents reasoning per request for text replies: the caller prefills the assistant turn with an open think tag to let the model reason, or a closed one for a direct answer, with a use_thinking example. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 5 + 10 + 5 = 85.
- 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 | LFM3-Audio-2B | Ripple-65B Preview | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 80 | 45 | LFM3-Audio-2B +35 |
| Risk | 20% (20 this run) | 40 | 55 | Ripple-65B Preview +15 |
| Agent interface | 10% (10 this run) | 85 | 75 | LFM3-Audio-2B +10 |
| Documentation and upkeep | 10% (10 this run) | 52 | 49 | LFM3-Audio-2B +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **53.3 · D** | |

## Facts side by side

| Fact | LFM3-Audio-2B | Ripple-65B Preview |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | Ricursive Intelligence |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-11-04 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |

## Verdicts

**LFM3-Audio-2B.** A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.

**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

### LFM3-Audio-2B

1. Start it with `llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-audio`
2. llama.cpp ships examples/realtime_client.py for live speech with this model
3. Prefill the assistant turn with an open think tag to let it reason before a text reply
4. The context is 32K
5. Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales
6. Liquid doesn't recommend it for agentic coding or complex multi-step reasoning

### 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, LFM3-Audio-2B or Ripple-65B Preview?

LFM3-Audio-2B scores 61 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 3 of 4 scored categories. Ripple-65B Preview leads on risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ripple-65b-preview.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lfm3-audio-2b", "b": "ripple-65b-preview"}`. From a terminal: `anchor compare lfm3-audio-2b ripple-65b-preview`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json

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- [LFM3-Audio-2B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ling-3-1-flash-1019.md)
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- [LFM3-Audio-2B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-distill-qwen-9b.md)
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- [LFM3-Audio-2B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm-v-4-7-35b-a3b.md)
- [LFM3-Audio-2B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm5-2b.md)
- [LFM3-Audio-2B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minimax-m2-5.md)
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- [LFM3-Audio-2B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minimax-m3-1-flash.md)
- [LFM3-Audio-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mistral-large-4.md)
- [LFM3-Audio-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-muse-glimmer-30b.md)
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- [LFM3-Audio-2B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-30b-a3b.md)
- [LFM3-Audio-2B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-4b.md)
- [LFM3-Audio-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)
- [LFM3-Audio-2B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-super-120b-a12b.md)
- [LFM3-Audio-2B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-ultra-550b-a55b.md)
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- [LFM3-Audio-2B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-olmo-3-7b-think.md)
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- [LFM3-Audio-2B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-397b.md)
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- [LFM3-Audio-2B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-30b-a3b-instruct-2507.md)
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- [LFM3-Audio-2B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-0-8b.md)
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- [LFM3-Audio-2B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-2b.md)
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- [LFM3-Audio-2B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-4b.md)
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- [LFM3-Audio-2B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-8-27b.md)
- [LFM3-Audio-2B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-8-flash-next.md)
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- [LFM3-Audio-2B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-coder-next.md)
- [LFM3-Audio-2B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-omni-30b-a3b-instruct.md)
- [LFM3-Audio-2B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-2b-instruct.md)
- [LFM3-Audio-2B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-30b-a3b-instruct.md)
- [LFM3-Audio-2B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-32b-instruct.md)
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- [LFM3-Audio-2B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-8b-instruct.md)
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- [LFM3-Audio-2B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-recursive-flint.md)
- [LFM3-Audio-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-sensenova-loop-9b-preview.md)
- [LFM3-Audio-2B vs SmolLM3-3B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-smollm3-3b.md)
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- [LFM3-Audio-2B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-flash-next.md)
- [LFM3-Audio-2B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-thinkingcap-qwen3-8-27b.md)
- [LFM3-Audio-2B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-unlimited-ocr-2.md)
- [LFM3-Audio-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b.md)
- [LFM3-Audio-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-4b.md)
- [LFM3-Audio-2B vs ZAYA1-8B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-zaya1-8b.md)
- [LFM3-Omni-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ripple-65b-preview.md)
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- [MiMo-V2.6-Flash-RL vs Ripple-65B Preview](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ripple-65b-preview.md)
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- [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)
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- [Muse Glimmer 30B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ripple-65b-preview.md)
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- [Nemotron 3.5 Lightning 30B A3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-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)
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- [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)
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- [Ripple-65B Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b.md)
- [Ripple-65B Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.md)
- [Ripple-65B Preview vs ZAYA1-8B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b.md)

---

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