# GLM-5.3 vs SenseNova-Loop-9B-Preview > GLM-5.3 scores 64.6 (B) 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/glm-5-3-vs-sensenova-loop-9b-preview - Markdown: https://www.anchorterminal.com/compare/glm-5-3-vs-sensenova-loop-9b-preview.md (~9,450 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 .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. 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 GLM-5.3 scores 64.6 (B) 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. - GLM-5.3: grade B, 64.6/100, rank #21 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3.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 ### GLM-5.3 (B) Good for: Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider Ahead on: - Reach, 55 against 40 - Agent interface, 70 against 60 - Documentation and upkeep, 58 against 44 Watch for: Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build ### 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: GLM-5.3 +15 - GLM-5.3 (55): Memory earns nothing, since Z.ai's smallest build is the 755.6 GB FP8 one, about 907 GB with the fifth added, and Ollama only lists a cloud entry. Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio have no local build. Z.ai publishes one quantised build, FP8, for 10, since the BF16 repository is the full precision. The GLM-5.3 License is a custom licence that is free for commercial use below a revenue threshold, for 10. The files download with no account or gate, for 10, and Z.ai's own API plus eight live providers on Hugging Face serve it, for 5. - 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: GLM-5.3 +14 - GLM-5.3 (58): The card covers intended use, languages and context and misses limitations, architecture, parameters and cut-off, for 8. Training data is 5, a description of the GLM-5 corpus in the cited report with no cut-off. The last change, a chat template speed-up on 14 October in the FP8 repository and on 15 October in the BF16 one, is within 30 days on 2 November, for 20. Responsiveness is 5, as zai-org answered 2 of the 16 discussions that aren't pull requests, the last on 8 September, which is a few, and no discussion has been opened since 10 October. Version policy is the full 10, with each GLM-5 release in its own repository and a dated blog saying what changed from GLM-5.2. File health is the full 10, with files and card agreeing and no broken-file report waiting by title. - 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. ### Agent interface: GLM-5.3 +10 - GLM-5.3 (70): A Jinja chat template sits in the repository, for 20. The tool-calling format is 10, since it is defined only by the template and the glm47 parser is named by vLLM and SGLang rather than by Z.ai. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and JSON documented only for the hosted API. Reasoning control is 10, a per-request budget through reasoning_effort with the reasoning in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rope scaling. - 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. ## Score by category | Category | Weight | GLM-5.3 | SenseNova-Loop-9B-Preview | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 40 | GLM-5.3 +15 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 70 | 60 | GLM-5.3 +10 | | Documentation and upkeep | 10% (10 this run) | 58 | 44 | GLM-5.3 +14 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **64.6 · B** | **46.8 · D** | | ## Facts side by side | Fact | GLM-5.3 | 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 | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-15 | 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 | ## Verdicts **GLM-5.3.** A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off. **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-5.3 1. Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe) 2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text 3. Pass `chat_template_kwargs: {"reasoning_effort": "low"}` for short tasks; anything other than low or high falls back to max 4. Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default 5. Don't send `enable_thinking: false`; the template ignores it and always opens a think block 6. Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API 7. No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe ### 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, GLM-5.3 or SenseNova-Loop-9B-Preview? GLM-5.3 scores 64.6 (B) 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/glm-5-3-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-vs-sensenova-loop-9b-preview.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-5-3", "b": "sensenova-loop-9b-preview"}`. 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