# Qwen3-Coder-Next vs SenseNova-Loop-9B-Preview > Qwen3-Coder-Next scores 57.6 (C) 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/qwen3-coder-next-vs-sensenova-loop-9b-preview - Markdown: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-sensenova-loop-9b-preview.md (~9,600 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 Qwen3-Coder-Next scores 57.6 (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. - Qwen3-Coder-Next: grade C, 57.6/100, rank #107 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.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 ### Qwen3-Coder-Next (C) Good for: Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine. Ahead on: - Reach, 85 against 40 - Agent interface, 75 against 60 Watch for: No LICENSE file anywhere and a broken licence link on the card ### 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: Qwen3-Coder-Next +45 - Qwen3-Coder-Next (85): Memory earns 15 of 30, since Alibaba's own Q4_K_M GGUF is 48.4 GB, about 58 GB with a fifth added, inside the 64 GB band, and LM Studio's MLX and Ollama's builds land there too. Runtimes reach the 25 cap on released vLLM (v0.33.0), SGLang (v0.5.23), llama.cpp, Ollama (its library entry, and llama.cpp b11963 pinned by Ollama 0.40.14 has qwen3next), LM Studio and Transformers. Alibaba's FP8 and GGUF builds give builds 15. The Apache 2.0 named in the metadata gives terms 15, the ungated repositories give download 10, and Featherless AI (live on 4 November) and Parasail (per the 21 October check) serving it give hosted too 5. 15+25+15+15+10+5 = 85. - 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: Qwen3-Coder-Next +15 - Qwen3-Coder-Next (75): The template ships in the repository (20). Tool calling is the model's strength, and Alibaba documents how tools are declared, names and ships the qwen3_coder parser and describes the format in its report (25), with several calls per turn in a model trained on 21 harness formats (20). Context is 262,144 tokens native (10). Structured output scores 0, as nothing documents JSON output, and reasoning control scores 0 because the model has no thinking mode to control. - 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 | Qwen3-Coder-Next | SenseNova-Loop-9B-Preview | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 40 | Qwen3-Coder-Next +45 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 75 | 60 | Qwen3-Coder-Next +15 | | Documentation and upkeep | 10% (10 this run) | 41 | 44 | SenseNova-Loop-9B-Preview +3 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **57.6 · C** | **46.8 · D** | | ## Facts side by side | Fact | Qwen3-Coder-Next | 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 | 2026-02-03 | 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 **Qwen3-Coder-Next.** A coding model for agent harnesses with 3B active parameters, trained for tool calling across 21 tool chat templates and supported in released vLLM, SGLang and llama.cpp. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine. Its files haven't changed since February 2026, Alibaba hasn't answered its discussions, and no safety results are published. **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-Coder-Next 1. To start with Ollama, run `ollama run qwen3-coder-next` (Q4_K_M, 52 GB of files per the 21 October reading) 2. On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with `llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072` 3. Serve with `vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder` (vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36 4. The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start 5. There's no thinking mode, so don't pass `enable_thinking` or wait for reasoning_content 6. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40 7. The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf 8. Validate any JSON it returns, since structured output isn't documented ### 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, Qwen3-Coder-Next or SenseNova-Loop-9B-Preview? Qwen3-Coder-Next scores 57.6 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-sensenova-loop-9b-preview.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-coder-next", "b": "sensenova-loop-9b-preview"}`. 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