Head to head · Open-weight models you run yourself · October 2026 research run
Ling-3.0-tiny vs SenseNova-Loop-9B-Preview
Ling-3.0-tiny scores 56.2 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. 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 Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT
Ahead on
- Reach, 95 against 40
- Documentation and upkeep, 56 against 44
Watch for
No inference provider is listed on the Hub and no inclusionAI API serves it
Best for Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today
No category where it leads by five points or more, and no fact that sets it apart.
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 Ling-3.0-tiny +55
Ling-3.0-tiny 95
Memory earns the full 30, since inclusionAI's own Q4_K_M GGUF is 4.8 GB, about 6 GB with a fifth added. Runtimes earn the full 25, from released Transformers, vLLM, SGLang and llama.cpp, and Ollama, whose v0.40.14 (2 November 2026) pins llama.cpp b11963 with the bailingmoe3 architecture (Ollama v0.40.7 of 20 October already pinned b11738, which has it). LM Studio has nothing. Builds earn 15 for FP8, INT4 and four GGUF precisions from inclusionAI. MIT with nothing added gives terms the full 15 and the ungated repositories give download the full 10. Hosted too scores 0, because the Hub's model page lists no inference provider and no inclusionAI or Ant Group API serving it was found.
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 Ling-3.0-tiny +12
Ling-3.0-tiny 56
Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 15, a data card with sources, size, cut-off and filtering. Last change 15, the newest commit is still the card change of 24 September 2026, 41 days before 4 November. Responsiveness 10, six of 16 discussions answered by an inclusionAI member, some. Version policy 5, a first release at this size. File health 0, the card's MTP recipe contradicts the files and a broken index size waits without a reply.
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.
Score by category
| Category | Weight this run | Ling-3.0-tiny | SenseNova-Loop-9B-Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 40 | Ling-3.0-tiny +55 |
| Risk | 20%20 | 50 | 50 | even |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 56 | 44 | Ling-3.0-tiny +12 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.2/100 | D 46.8/100 |
Facts side by side
| Fact | Ling-3.0-tiny | SenseNova-Loop-9B-Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | SenseTime (SenseNova) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-31 | 2026-11-02 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
How the scores have moved
SenseNova-Loop-9B-Preview
No score change since it was listed.
Verdicts
Ling-3.0-tiny
A small, quick hybrid reasoning model for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with no host. The risks are a card that doesn't match the files in places, a vLLM route running repository code, and no safety results.
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
Ling-3.0-tiny
- Start it with
llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768, which needs llama.cpp b11772 or a recent release with bailingmoe3 - With vLLM 0.32.0,
vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3, which runs the repository's config code - With SGLang 0.5.22, its cookbook uses
--reasoning-parser deepseek-r1 --tool-call-parser glm45for this model, and drop the card's NEXTN flags since tiny has no MTP layer - Thinking is on by default, pass
chat_template_kwargswithenable_thinkingfalse for short tasks - Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
- The template keeps earlier reasoning in the prompt, which grows long agent sessions
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
- For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches
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, Ling-3.0-tiny or SenseNova-Loop-9B-Preview?
Ling-3.0-tiny scores 56.2 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories.
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