# Arete-27B vs SenseNova-Loop-9B-Preview

> Arete-27B scores 55.1 (C) to SenseNova-Loop-9B-Preview's 46.8 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview
- Markdown: https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.md (~9,200 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

Arete-27B scores 55.1 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json
- SenseNova-Loop-9B-Preview: grade D, 46.8/100, rank #155 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-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

### Arete-27B (C)

Good for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine

Ahead on:
- Reach, 50 against 40
- Risk, 55 against 50
- Documentation and upkeep, 63 against 44

Watch for: No quantised build from Nous Research, so the GGUF route depends on a third party

### SenseNova-Loop-9B-Preview (D)

Good for: Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today

Watch for: No released runtime runs it. vLLM and SGLang need unmerged pull requests


## Why the edge

### Documentation and upkeep: Arete-27B +19

- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.
- SenseNova-Loop-9B-Preview (44): Model card 14, Intended use, the architecture, the parameter counts, the training context and the languages (in the metadata) are on the card. Training data 5, one general sentence on the recipes, with no sources, sizes or cut-off. Last change 20, the last commit, on 2 November 2026, reworked the speculative decoding section of the card, 8 days before this check. Responsiveness 0, none of the 5 discussions that aren't pull requests (30 October to 9 November 2026) has a reply from a sensenova member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, labelled a preview, with dated news on the card. File health 0, the files contradict the card on precision. 14 + 5 + 20 + 0 + 5 + 0 = 44.

### Reach: Arete-27B +10

- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.
- SenseNova-Loop-9B-Preview (40): Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.

### Risk: Arete-27B +5

- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.
- SenseNova-Loop-9B-Preview (50): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50.

## Score by category

| Category | Weight | Arete-27B | SenseNova-Loop-9B-Preview | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 40 | Arete-27B +10 |
| Risk | 20% (20 this run) | 55 | 50 | Arete-27B +5 |
| Agent interface | 10% (10 this run) | 60 | 60 | even |
| Documentation and upkeep | 10% (10 this run) | 63 | 44 | Arete-27B +19 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.1 · C** | **46.8 · D** | |

## Facts side by side

| Fact | Arete-27B | SenseNova-Loop-9B-Preview |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Nous Research | SenseTime (SenseNova) |
| 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 | 2026-11-06 | 2026-11-02 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |

## Verdicts

**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.

**SenseNova-Loop-9B-Preview.** An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.

## Before you call either

### Arete-27B

1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM
2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's
3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off
4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for
5. Validate JSON output yourself, since structured output isn't documented

### SenseNova-Loop-9B-Preview

1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it
2. Pin transformers 5.18.0, the version the card checked
3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size
4. Pass enable_thinking=False to apply_chat_template for a direct answer
5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser
6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API

## Questions

### Which is better for AI agents, Arete-27B or SenseNova-Loop-9B-Preview?

Arete-27B scores 55.1 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "arete-27b", "b": "sensenova-loop-9b-preview"}`. From a terminal: `anchor compare arete-27b sensenova-loop-9b-preview`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json

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- [Arete-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-27b.md)
- [Arete-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Arete-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/arete-27b-vs-thinkingcap-qwen3-8-27b.md)
- [Arete-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/arete-27b-vs-unlimited-ocr-2.md)
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- [Nemotron 3.5 Lightning 30B A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-sensenova-loop-9b-preview.md)
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- [Nemotron 3 Super 120B A12B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-sensenova-loop-9b-preview.md)
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- [Qwen3-30B-A3B-Instruct-2507 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-sensenova-loop-9b-preview.md)
- [Qwen3-4B-Instruct-2507 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-sensenova-loop-9b-preview.md)
- [Qwen3.5-0.8B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-sensenova-loop-9b-preview.md)
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- [Qwen3.5-397B-A17B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-sensenova-loop-9b-preview.md)
- [Qwen3.5-4B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-sensenova-loop-9b-preview.md)
- [Qwen3.5-9B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview.md)
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- [Qwen3.8-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-sensenova-loop-9b-preview.md)
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- [Qwen3-Coder-Next vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-sensenova-loop-9b-preview.md)
- [Qwen3-Omni-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-sensenova-loop-9b-preview.md)
- [Qwen3-VL-2B-Instruct vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-sensenova-loop-9b-preview.md)
- [Qwen3-VL-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-sensenova-loop-9b-preview.md)
- [Qwen3-VL-32B-Instruct vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-sensenova-loop-9b-preview.md)
- [Qwen3-VL-4B-Instruct vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview.md)
- [Qwen3-VL-8B-Instruct vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview.md)
- [Qwopus3.8-27B-Flash-V2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview.md)
- [Recursive Flint vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview.md)
- [Ripple-65B Preview vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview.md)
- [SenseNova-Loop-9B-Preview vs SmolLM3-3B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b.md)
- [SenseNova-Loop-9B-Preview vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b.md)
- [SenseNova-Loop-9B-Preview vs Step 3.7 Flash](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash.md)
- [SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b.md)
- [SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next.md)
- [SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b.md)
- [SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.md)
- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.md)
- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.md)
- [SenseNova-Loop-9B-Preview vs ZAYA1-8B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.md)

---

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