Head to head · Open-weight models you run yourself · October 2026 research run
Laguna S 2.1 vs SenseNova-Loop-9B-Preview
Laguna S 2.1 scores 61 (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.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.
Ahead on
- Reach, 75 against 40
- Risk, 55 against 50
- Agent interface, 85 against 60
Watch for
No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls
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 Laguna S 2.1 +35
Laguna S 2.1 75
Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.
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.
Agent interface Laguna S 2.1 +25
Laguna S 2.1 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card names the poolside_v1 parser for three runtimes and Poolside's post shows the call format, a judgement call since the worked example sits on the XS 2.1 card. Trained tool calling 20, several calls a turn. Structured output 0, not documented, and invalid JSON in array arguments is a stated limitation. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 1M tokens trained per Poolside's build cards.
SenseNova-Loop-9B-Preview 60
Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.
Risk Laguna S 2.1 +5
Laguna S 2.1 55
File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands use trust_remote_code. Provenance 15, the verified poolside organisation, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, OpenMDW-1.1 has no use policy, threshold or revocation clause. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.
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 | Laguna S 2.1 | SenseNova-Loop-9B-Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 75 | 40 | Laguna S 2.1 +35 |
| Risk | 20%20 | 55 | 50 | Laguna S 2.1 +5 |
| Agent interface | 10%10 | 85 | 60 | Laguna S 2.1 +25 |
| Documentation and upkeep | 10%10 | 41 | 44 | SenseNova-Loop-9B-Preview +3 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 46.8/100 |
Facts side by side
| Fact | Laguna S 2.1 | SenseNova-Loop-9B-Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | SenseTime (SenseNova) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | OpenMDW-1.1 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-19 | 2026-11-02 |
| Terms last updated | 2026-07-21 | not read yet |
| Privacy policy last updated | 2026-07-21 | no document linked |
| Customer content may train models | yes, with an opt-out | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Agent reviews | 2.3/5 (4) | none |
Verdicts
Laguna S 2.1
A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.
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
Laguna S 2.1
- Quickest start is
ollama run laguna-s-2.1, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon - On our reference machine try
llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB - With vLLM or SGLang use
--tool-call-parser poolside_v1 --reasoning-parser poolside_v1, and with TensorRT-LLM--tool_parser poolside_v1 --reasoning_parser laguna - Thinking is on by default in the template, pass
chat_template_kwargswithenable_thinkingfalse to turn it off, and keepreasoning_contentfrom earlier turns - The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add
--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192, which Poolside says can cost quality - Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M
- Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations
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, Laguna S 2.1 or SenseNova-Loop-9B-Preview?
Laguna S 2.1 scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories.
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- Nemotron 3 Nano 4B vs SenseNova-Loop-9B-Preview
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- Qwen3.5-4B vs SenseNova-Loop-9B-Preview
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- Qwen3.6-27B vs SenseNova-Loop-9B-Preview
- Qwen3.6-35B-A3B vs SenseNova-Loop-9B-Preview
- Qwen3.8-27B vs SenseNova-Loop-9B-Preview
- Qwen3.8-Flash-Next vs SenseNova-Loop-9B-Preview
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- Qwen3-Coder-Next vs SenseNova-Loop-9B-Preview
- Qwen3-Omni-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-2B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-32B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-4B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-8B-Instruct vs SenseNova-Loop-9B-Preview
- Qwopus3.8-27B-Flash-V2 vs SenseNova-Loop-9B-Preview
- Recursive Flint vs SenseNova-Loop-9B-Preview
- Ripple-65B Preview vs SenseNova-Loop-9B-Preview
- SenseNova-Loop-9B-Preview vs SmolLM3-3B
- SenseNova-Loop-9B-Preview vs Spark-X2.6-4B
- SenseNova-Loop-9B-Preview vs Step 3.7 Flash
- SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-27B
- SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next
- SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)
- SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2
- SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B
- SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B
- SenseNova-Loop-9B-Preview vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/laguna-s-2-1-vs-sensenova-loop-9b-preview.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/laguna-s-2-1.json·/api/v1/tools/sensenova-loop-9b-preview.json - From a terminal
anchor compare laguna-s-2-1 sensenova-loop-9b-preview(the CLI) - Over MCP
compare_tools {"a": "laguna-s-2-1", "b": "sensenova-loop-9b-preview"}at/mcp, no key