# BigBang-v1 vs SenseNova-Loop-9B-Preview

> BigBang-v1 scores 56.3 (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/bigbang-v1-vs-sensenova-loop-9b-preview
- Markdown: https://www.anchorterminal.com/compare/bigbang-v1-vs-sensenova-loop-9b-preview.md (~9,000 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

BigBang-v1 scores 56.3 (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.

- BigBang-v1: grade C, 56.3/100, rank #116 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/bigbang-v1.md · JSON https://www.anchorterminal.com/api/v1/tools/bigbang-v1.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

### BigBang-v1 (C)

Good for: Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.

Ahead on:
- Reach, 60 against 40
- Risk, 55 against 50

Watch for: BF16 is the only build from the publisher, needing about 86 GB

### 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

### Reach: BigBang-v1 +20

- BigBang-v1 (60): Memory 10 of 30, the only build from the publisher is BF16 at 71.9 GB, 86.3 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes 25, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama 0.40.14 runs through the llama.cpp build it pins. Builds 0, no quantised build from the publisher. Terms 15, Apache 2.0 like its base. Download 10, not gated. Hosted 0, one inference provider, Featherless AI, where the line needs two. 10 + 25 + 0 + 15 + 10 + 0 = 60.
- 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: BigBang-v1 +5

- BigBang-v1 (55): File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the publisher's own organisation with a SHA-256 for every weight file. Safety evaluation 0. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no website or security contact. 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 | BigBang-v1 | SenseNova-Loop-9B-Preview | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 60 | 40 | BigBang-v1 +20 |
| Risk | 20% (20 this run) | 55 | 50 | BigBang-v1 +5 |
| Agent interface | 10% (10 this run) | 60 | 60 | even |
| Documentation and upkeep | 10% (10 this run) | 46 | 44 | BigBang-v1 +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **56.3 · C** | **46.8 · D** | |

## Facts side by side

| Fact | BigBang-v1 | SenseNova-Loop-9B-Preview |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | The Endless Frontier (endless-frontier) | 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 | 2026-08-06 | 2026-11-02 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.3/5 (4) | none |

## How the scores have moved

### BigBang-v1

- +1.9 on 4 November 2026: correction

### SenseNova-Loop-9B-Preview

No score change since it was listed.

## Verdicts

**BigBang-v1.** A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.

**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

### BigBang-v1

1. On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
2. The card says to keep at least 128K of context so its thinking isn't cut short
3. Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output
4. For Ollama or llama.cpp you'll need someone else's GGUF; the publisher ships BF16 safetensors only
5. Validate JSON output yourself or use the runtime's guided decoding

### 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, BigBang-v1 or SenseNova-Loop-9B-Preview?

BigBang-v1 scores 56.3 (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/bigbang-v1-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-sensenova-loop-9b-preview.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "bigbang-v1", "b": "sensenova-loop-9b-preview"}`. From a terminal: `anchor compare bigbang-v1 sensenova-loop-9b-preview`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json and https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json

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- [Qwen3-VL-4B-Instruct vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview.md)
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- [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)
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- [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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