Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3.5-2B vs SenseNova-Loop-9B-Preview
Qwen3.5-2B scores 50.5 (D) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU
Ahead on
- Reach, 80 against 40
- Risk, 55 against 50
Watch for
No quantised build from Alibaba, and only one inference provider
Best for Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today
Ahead on
- Documentation and upkeep, 44 against 37
Watch for
No released runtime runs it. vLLM and SGLang need unmerged pull requests
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach Qwen3.5-2B +40
Qwen3.5-2B 80
Memory earns the full 30, since LM Studio's Q4_K_M GGUF with its vision projector is 1.94 GB, about 2.3 GB with the fifth added (Ollama's Q4_K_M build was 1.9 GB on 21 October). Alibaba publishes no quantised 2B, so builds scores 0. Runtimes reach the 25 cap, with llama.cpp b11963 and Ollama through the build it pins re-read today. Apache 2.0 with nothing added gives terms 15 and the ungated download 10. Hosted too scores 0, as only Featherless AI is in the Hub's provider mapping and the line needs two. 30+25+0+15+10+0 = 80.
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.
Documentation and upkeep SenseNova-Loop-9B-Preview +7
Qwen3.5-2B 37
The card covers six of the seven items, missing only a cut-off (17), because it states the intended uses and warns about thinking loops. Training data is a general description without a cut-off (5). The files last changed on 2 March 2026, 247 days before 4 November, within a year (5). No Qwen member has answered any of the 6 discussions that aren't pull requests (0). Qwen3-1.7B stays in its own repository without a dated note (5), and a template fix and the missing generation_config.json wait unanswered (5). 17+5+5+0+5+5 = 37.
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.
Risk Qwen3.5-2B +5
Qwen3.5-2B 55
Safetensors only with no remote code gives file safety its 25, and files in the Qwen organisation the publisher's GitHub links to, each with a hash on the Hub, give provenance 15. The Apache 2.0 licence carries none of the deductions, so licence risk keeps 15. Safety evaluation, agent guidance and a security programme score 0, since the card has no safety results or agent cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so those are scored as absent and left open. 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 this run | Qwen3.5-2B | SenseNova-Loop-9B-Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 40 | Qwen3.5-2B +40 |
| Risk | 20%20 | 55 | 50 | Qwen3.5-2B +5 |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 37 | 44 | SenseNova-Loop-9B-Preview +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 50.5/100 | D 46.8/100 |
Facts side by side
| Fact | Qwen3.5-2B | SenseNova-Loop-9B-Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | 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 | 2026-03-02 | 2026-11-02 |
| 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
Qwen3.5-2B
A small vision-language model for prototypes, fine-tuning and narrow local steps such as tool calls, tagging or reading an image. It runs in about 2 GB at 4 bits in six runtimes under plain Apache 2.0. Keep thinking off, since the card warns it loops. The card has no safety results, and Qwen has answered none of its discussions.
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
Qwen3.5-2B
- Smallest start is
ollama run qwen3.5:2b-q4_K_M(1.9 GB); plainqwen3.5:2bpulls the 2.7 GB Q8_0 build - Full precision
vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder - Thinking is off by default; if you turn it on with
"chat_template_kwargs": {"enable_thinking": true}, stream and cut off runaway output, since the card warns of thinking loops, and add--reasoning-parser qwen3in vLLM or SGLang to split the reasoning out - There's no generation_config.json, so pass the card's sampling yourself, for example
temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0for text without thinking - Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests
- Validate any JSON it returns, since structured output isn't documented
SenseNova-Loop-9B-Preview
- Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it
- Pin transformers 5.18.0, the version the card checked
- Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size
- Pass enable_thinking=False to apply_chat_template for a direct answer
- Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser
- 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, Qwen3.5-2B or SenseNova-Loop-9B-Preview?
Qwen3.5-2B scores 50.5 (D) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/qwen3-5-2b-vs-sensenova-loop-9b-preview.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-5-2b.json·/api/v1/tools/sensenova-loop-9b-preview.json - From a terminal
anchor compare qwen3-5-2b sensenova-loop-9b-preview(the CLI) - Over MCP
compare_tools {"a": "qwen3-5-2b", "b": "sensenova-loop-9b-preview"}at/mcp, no key