Head to head · Open-weight models you run yourself · October 2026 research run
GLM-4.5-Air vs SenseNova-Loop-9B-Preview
GLM-4.5-Air scores 60.3 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 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 Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.
Ahead on
- Reach, 70 against 40
- Risk, 65 against 50
- Agent interface, 85 against 60
Watch for
Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own
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 38
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 GLM-4.5-Air +30
GLM-4.5-Air 70
Memory 5 of 30, Z.ai's smallest build is FP8 at 112.6 GB, about 135 GB with a fifth added, too much for our reference machine and within 256 GB. Runtimes 25 (the cap), released llama.cpp, vLLM, SGLang and Transformers run it, and Ollama 0.40.14 (2 November 2026) builds llama.cpp b11963, which has the glm4moe architecture, as kolibri-1 and occamy-1-0 are credited for Ollama's pin. Ollama and LM Studio have no library entry. Builds 10, one quantised build (FP8). Terms 15, MIT with nothing added. Download 10, no gate. Hosted 5, Z.ai and Novita serve it on Hugging Face.
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 GLM-4.5-Air +25
GLM-4.5-Air 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, Z.ai names the glm45 parser for vLLM and SGLang, says to declare tools in OpenAI style and shows calls coming back. Trained tool calling 20, one or more calls per turn. Structured output 0, documented only for Z.ai's hosted API. Reasoning control 10, enable_thinking false turns thinking off per request and the glm45 reasoning parser returns it separately. Context 10, 128K native.
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 GLM-4.5-Air +15
GLM-4.5-Air 65
File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the organisation Z.ai's GitHub links to, with a SHA-256 on every LFS file. Safety evaluation 10, Z.ai's own SafetyBench results in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain MIT. Security programme 0, no security.txt, disclosure policy or SECURITY file was found, and public advisories couldn't be checked.
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.
Documentation and upkeep SenseNova-Loop-9B-Preview +6
GLM-4.5-Air 38
Model card 8 of 20, intended use, parameters and languages only. Training data 5, the technical report describes the sources and filtering with no cut-off. Last change 5, the weights' files date from August 2025 (Hub head a24ceef6, 11 August 2025, re-read on 4 November) and Z.ai's GitHub repository last changed on 1 February 2026, 276 days before this check, so within a year. Responsiveness 10, Z.ai answered 4 of the 14 questions, all in the first three days, recounted by script on 4 November. Version policy 5, a first release. File health 5, the config and tokenizer disagree on the context and a tokenisation report has no 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 | GLM-4.5-Air | SenseNova-Loop-9B-Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 70 | 40 | GLM-4.5-Air +30 |
| Risk | 20%20 | 65 | 50 | GLM-4.5-Air +15 |
| Agent interface | 10%10 | 85 | 60 | GLM-4.5-Air +25 |
| Documentation and upkeep | 10%10 | 38 | 44 | SenseNova-Loop-9B-Preview +6 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 60.3/100 | D 46.8/100 |
Facts side by side
| Fact | GLM-4.5-Air | SenseNova-Loop-9B-Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | 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 | 2025-08-11 | 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
GLM-4.5-Air
An English and Chinese mixture-of-experts model for agent, coding and tool-calling work, with switchable thinking and released support in llama.cpp, vLLM, SGLang and Transformers. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. Its files date from August 2025, with no Z.ai reply since July 2025.
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
GLM-4.5-Air
- Z.ai's FP8 recipe is
python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45, and 2 H100s are enough below the full 128K context - Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
- Thinking is on by default, pass
chat_template_kwargswithenable_thinkingfalse for short tasks, which makes the template add /nothink and an empty think block - Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
- Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights
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, GLM-4.5-Air or SenseNova-Loop-9B-Preview?
GLM-4.5-Air scores 60.3 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep.
Other comparisons with GLM-4.5-Air or SenseNova-Loop-9B-Preview
- Agents-A1-4B vs GLM-4.5-Air
- Agents-A1-4B vs SenseNova-Loop-9B-Preview
- Apertus v1.5 8B vs GLM-4.5-Air
- Apertus v1.5 8B vs SenseNova-Loop-9B-Preview
- Apodex-1.1-mini vs GLM-4.5-Air
- Apodex-1.1-mini vs SenseNova-Loop-9B-Preview
- Arete-27B vs GLM-4.5-Air
- Arete-27B vs SenseNova-Loop-9B-Preview
- BigBang-v1 vs GLM-4.5-Air
- BigBang-v1 vs SenseNova-Loop-9B-Preview
- Carbon-X vs GLM-4.5-Air
- Carbon-X vs SenseNova-Loop-9B-Preview
- Cola2 vs GLM-4.5-Air
- Cola2 vs SenseNova-Loop-9B-Preview
- Cosmos-Reason2-2B vs GLM-4.5-Air
- Cosmos-Reason2-2B vs SenseNova-Loop-9B-Preview
- Darwin-36B-Opus vs GLM-4.5-Air
- Darwin-36B-Opus vs SenseNova-Loop-9B-Preview
- DeepSeek-V3.2 vs GLM-4.5-Air
- DeepSeek-V3.2 vs SenseNova-Loop-9B-Preview
- DeepSeek V4.1 Flash vs GLM-4.5-Air
- DeepSeek V4.1 Flash vs SenseNova-Loop-9B-Preview
- DeepSeek-V4-Flash-0731 vs GLM-4.5-Air
- DeepSeek-V4-Flash-0731 vs SenseNova-Loop-9B-Preview
- Devstral Small 2 vs GLM-4.5-Air
- Devstral Small 2 vs SenseNova-Loop-9B-Preview
- DiffusionGemma 26B A4B IT vs GLM-4.5-Air
- DiffusionGemma 26B A4B IT vs SenseNova-Loop-9B-Preview
- EXAONE 4.5 33B vs GLM-4.5-Air
- EXAONE 4.5 33B vs SenseNova-Loop-9B-Preview
- Gemma 4 12B IT vs GLM-4.5-Air
- Gemma 4 12B IT vs SenseNova-Loop-9B-Preview
- Gemma 4 26B A4B IT vs GLM-4.5-Air
- Gemma 4 26B A4B IT vs SenseNova-Loop-9B-Preview
- Gemma 4 31B IT vs GLM-4.5-Air
- Gemma 4 31B IT vs SenseNova-Loop-9B-Preview
- Gemma 4 E2B IT vs GLM-4.5-Air
- Gemma 4 E2B IT vs SenseNova-Loop-9B-Preview
- Gemma 4 E4B IT vs GLM-4.5-Air
- Gemma 4 E4B IT vs SenseNova-Loop-9B-Preview
- GLM-4.5-Air vs GLM-4.7-Flash
- GLM-4.5-Air vs GLM-5.3
- GLM-4.5-Air vs GLM-5.3-Flash
- GLM-4.5-Air vs gpt-oss-120b
- GLM-4.5-Air vs gpt-oss-20b
- GLM-4.5-Air vs Granite 4.2 30B
- GLM-4.5-Air vs Granite 4.2 3B
- GLM-4.5-Air vs Granite 4.2 8B
- GLM-4.5-Air vs Hy-Memory-30B-A3B
- GLM-4.5-Air vs Hy3
- GLM-4.5-Air vs Inkling
- GLM-4.5-Air vs Inkling-Small
- GLM-4.5-Air vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- GLM-4.5-Air vs Kimi K3
- GLM-4.5-Air vs Kimi K3.1 Pro
- GLM-4.5-Air vs Kolibri-1
- GLM-4.5-Air vs Kronos
- GLM-4.5-Air vs Laguna M.1
- GLM-4.5-Air vs Laguna S 2.1
- GLM-4.5-Air vs Laguna XS 2.1
- GLM-4.5-Air vs LBS-27B
- GLM-4.5-Air vs LFM2.5-2.6B
- GLM-4.5-Air vs LFM3-1.7B
- GLM-4.5-Air vs LFM3-Audio-2B
- GLM-4.5-Air vs LFM3-Omni-2B
- GLM-4.5-Air vs LFM3-VL-2B
- GLM-4.5-Air vs Ling-3.0-tiny
- GLM-4.5-Air vs Ling-3.1-flash-1019
- GLM-4.5-Air vs LongCat-2.5
- GLM-4.5-Air vs MiMo-V2.6 Distill Qwen 9B
- GLM-4.5-Air vs MiMo-V2.6-Flash-RL
- GLM-4.5-Air vs MiniCPM-o 4.5
- GLM-4.5-Air vs MiniCPM-V 4.7 1B
- GLM-4.5-Air vs MiniCPM-V 4.7 35B-A3B
- GLM-4.5-Air vs MiniCPM5-2B
- GLM-4.5-Air vs MiniMax-M2.5
- GLM-4.5-Air vs MiniMax-M2.7
- GLM-4.5-Air vs MiniMax-M3.1-Flash
- GLM-4.5-Air vs Mistral Large 4
- GLM-4.5-Air vs Muse Glimmer 30B
- GLM-4.5-Air vs Nanbeige4.2-3B
- GLM-4.5-Air vs Nanbeige4.3-33B
- GLM-4.5-Air vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- GLM-4.5-Air vs Nemotron 3.5 Lightning 30B A3B
- GLM-4.5-Air vs Nemotron 3 Nano 30B A3B
- GLM-4.5-Air vs Nemotron 3 Nano 4B
- GLM-4.5-Air vs Nemotron 3 Nano Omni 30B A3B Reasoning
- GLM-4.5-Air vs Nemotron 3 Super 120B A12B
- GLM-4.5-Air vs Nemotron 3 Ultra 550B A55B
- GLM-4.5-Air vs North Mini Code 1.0
- GLM-4.5-Air vs occamy-1.0
- GLM-4.5-Air vs Olmo 3 7B Think
- GLM-4.5-Air vs Ornith-1.5-122B
- GLM-4.5-Air vs Ornith-1.5-35B-A3B
- GLM-4.5-Air vs Ornith-1.5-397B
- GLM-4.5-Air vs Ornith-1.5-9B
- GLM-4.5-Air vs OTel-2.0-LLM-31B-IT
- GLM-4.5-Air vs Qwen-AgentWorld-35B-A3B
- GLM-4.5-Air vs Qwen3-30B-A3B-Instruct-2507
- GLM-4.5-Air vs Qwen3-4B-Instruct-2507
- GLM-4.5-Air vs Qwen3.5-0.8B
- GLM-4.5-Air vs Qwen3.5-122B-A10B
- GLM-4.5-Air vs Qwen3.5-2B
- GLM-4.5-Air vs Qwen3.5-397B-A17B
- GLM-4.5-Air vs Qwen3.5-4B
- GLM-4.5-Air vs Qwen3.5-9B
- GLM-4.5-Air vs Qwen3.6-27B
- GLM-4.5-Air vs Qwen3.6-35B-A3B
- GLM-4.5-Air vs Qwen3.8-27B
- GLM-4.5-Air vs Qwen3.8-Flash-Next
- GLM-4.5-Air vs Qwen3-Coder-30B-A3B-Instruct
- GLM-4.5-Air vs Qwen3-Coder-Next
- GLM-4.5-Air vs Qwen3-Omni-30B-A3B-Instruct
- GLM-4.5-Air vs Qwen3-VL-2B-Instruct
- GLM-4.5-Air vs Qwen3-VL-30B-A3B-Instruct
- GLM-4.5-Air vs Qwen3-VL-32B-Instruct
- GLM-4.5-Air vs Qwen3-VL-4B-Instruct
- GLM-4.5-Air vs Qwen3-VL-8B-Instruct
- GLM-4.5-Air vs Qwopus3.8-27B-Flash-V2
- GLM-4.5-Air vs Recursive Flint
- GLM-4.5-Air vs Ripple-65B Preview
- GLM-4.5-Air vs SmolLM3-3B
- GLM-4.5-Air vs Spark-X2.6-4B
- GLM-4.5-Air vs Step 3.7 Flash
- GLM-4.5-Air vs Swift 1.5 Qwen3.8-27B
- GLM-4.5-Air vs Swift 1.5 Qwen3.8-Flash-Next
- GLM-4.5-Air vs ThinkingCap (Qwen3.8 27B)
- GLM-4.5-Air vs Baidu Unlimited-OCR-2
- GLM-4.5-Air vs WeVisOne-VL-2B
- GLM-4.5-Air vs WeVisOne-VL-4B
- GLM-4.5-Air vs ZAYA1-8B
- GLM-4.7-Flash vs SenseNova-Loop-9B-Preview
- GLM-5.3-Flash vs SenseNova-Loop-9B-Preview
- GLM-5.3 vs SenseNova-Loop-9B-Preview
- gpt-oss-120b vs SenseNova-Loop-9B-Preview
- gpt-oss-20b vs SenseNova-Loop-9B-Preview
- Granite 4.2 30B vs SenseNova-Loop-9B-Preview
- Granite 4.2 3B vs SenseNova-Loop-9B-Preview
- Granite 4.2 8B vs SenseNova-Loop-9B-Preview
- Hy-Memory-30B-A3B vs SenseNova-Loop-9B-Preview
- Hy3 vs SenseNova-Loop-9B-Preview
- Inkling-Small vs SenseNova-Loop-9B-Preview
- Inkling vs SenseNova-Loop-9B-Preview
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs SenseNova-Loop-9B-Preview
- Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview
- Kimi K3 vs SenseNova-Loop-9B-Preview
- Kolibri-1 vs SenseNova-Loop-9B-Preview
- Kronos vs SenseNova-Loop-9B-Preview
- Laguna M.1 vs SenseNova-Loop-9B-Preview
- Laguna S 2.1 vs SenseNova-Loop-9B-Preview
- Laguna XS 2.1 vs SenseNova-Loop-9B-Preview
- LBS-27B vs SenseNova-Loop-9B-Preview
- LFM2.5-2.6B vs SenseNova-Loop-9B-Preview
- LFM3-1.7B vs SenseNova-Loop-9B-Preview
- LFM3-Audio-2B vs SenseNova-Loop-9B-Preview
- LFM3-Omni-2B vs SenseNova-Loop-9B-Preview
- LFM3-VL-2B vs SenseNova-Loop-9B-Preview
- Ling-3.0-tiny vs SenseNova-Loop-9B-Preview
- Ling-3.1-flash-1019 vs SenseNova-Loop-9B-Preview
- LongCat-2.5 vs SenseNova-Loop-9B-Preview
- MiMo-V2.6 Distill Qwen 9B vs SenseNova-Loop-9B-Preview
- MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview
- MiniCPM-o 4.5 vs SenseNova-Loop-9B-Preview
- MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview
- MiniCPM-V 4.7 35B-A3B vs SenseNova-Loop-9B-Preview
- MiniCPM5-2B vs SenseNova-Loop-9B-Preview
- MiniMax-M2.5 vs SenseNova-Loop-9B-Preview
- MiniMax-M2.7 vs SenseNova-Loop-9B-Preview
- MiniMax-M3.1-Flash vs SenseNova-Loop-9B-Preview
- Mistral Large 4 vs SenseNova-Loop-9B-Preview
- Muse Glimmer 30B vs SenseNova-Loop-9B-Preview
- Nanbeige4.2-3B vs SenseNova-Loop-9B-Preview
- Nanbeige4.3-33B vs SenseNova-Loop-9B-Preview
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SenseNova-Loop-9B-Preview
- Nemotron 3.5 Lightning 30B A3B vs SenseNova-Loop-9B-Preview
- Nemotron 3 Nano 30B A3B vs SenseNova-Loop-9B-Preview
- Nemotron 3 Nano 4B vs SenseNova-Loop-9B-Preview
- Nemotron 3 Nano Omni 30B A3B Reasoning vs SenseNova-Loop-9B-Preview
- Nemotron 3 Super 120B A12B vs SenseNova-Loop-9B-Preview
- Nemotron 3 Ultra 550B A55B vs SenseNova-Loop-9B-Preview
- North Mini Code 1.0 vs SenseNova-Loop-9B-Preview
- occamy-1.0 vs SenseNova-Loop-9B-Preview
- Olmo 3 7B Think vs SenseNova-Loop-9B-Preview
- Ornith-1.5-122B vs SenseNova-Loop-9B-Preview
- Ornith-1.5-35B-A3B vs SenseNova-Loop-9B-Preview
- Ornith-1.5-397B vs SenseNova-Loop-9B-Preview
- Ornith-1.5-9B vs SenseNova-Loop-9B-Preview
- OTel-2.0-LLM-31B-IT vs SenseNova-Loop-9B-Preview
- Qwen-AgentWorld-35B-A3B vs SenseNova-Loop-9B-Preview
- Qwen3-30B-A3B-Instruct-2507 vs SenseNova-Loop-9B-Preview
- Qwen3-4B-Instruct-2507 vs SenseNova-Loop-9B-Preview
- Qwen3.5-0.8B vs SenseNova-Loop-9B-Preview
- Qwen3.5-122B-A10B vs SenseNova-Loop-9B-Preview
- Qwen3.5-2B vs SenseNova-Loop-9B-Preview
- Qwen3.5-397B-A17B vs SenseNova-Loop-9B-Preview
- Qwen3.5-4B vs SenseNova-Loop-9B-Preview
- Qwen3.5-9B vs SenseNova-Loop-9B-Preview
- 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
- Qwen3-Coder-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- 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/glm-4-5-air-vs-sensenova-loop-9b-preview.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/glm-4-5-air.json·/api/v1/tools/sensenova-loop-9b-preview.json - From a terminal
anchor compare glm-4-5-air sensenova-loop-9b-preview(the CLI) - Over MCP
compare_tools {"a": "glm-4-5-air", "b": "sensenova-loop-9b-preview"}at/mcp, no key