Head to head · Open-weight models you run yourself · October 2026 research run
Carbon-X vs SenseNova-Loop-9B-Preview
Carbon-X scores 61 (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 agent interface. 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
Carbon-X C
Best for Agents that need a second opinion on DNA variants and regulatory regions on a 16 to 24 GB machine, without sending sequences to a hosted model.
Ahead on
- Reach, 90 against 40
- Risk, 65 against 50
- Documentation and upkeep, 72 against 44
Watch for
Not clinically validated and no wet-lab validation reported
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
- Agent interface, 60 against 35
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 Carbon-X +50
Carbon-X 90
Memory 30, the publisher's Q4_K_M GGUF is 9.6 GB, or 11.6 GB with a fifth added. Runtimes 20, released llama.cpp (from b11742, which added the DNA tokenizer), vLLM, SGLang and Transformers all run it, with no Ollama or LM Studio entry. Builds 15, Hugging Face publishes GGUFs at four precisions. Terms 15, Apache 2.0 on the model and on its Qwen3-14B base. Download 10, nothing is gated. Hosted 0, no inference provider or Hugging Face API serves it.
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 Carbon-X +28
Carbon-X 72
Model card 17, everything but a knowledge cut-off. Training data 15, the mid-training sources and shares are listed, the instruction and fine-tuning data aren't named, and there's no cut-off. Last change 20, 28 October 2026, when config.json, generation_config.json, tokenizer_config.json and tokenizer.py were fixed. Responsiveness 10, organisation members replied to 2 of the 4 questions so far. Version policy 5, a first release whose history was squashed without a note. File health 5, the card now sends llama.cpp users to the publisher's GGUF repository, but the tokenizer config's 131,072 still contradicts the card's 40,960. 17 + 15 + 20 + 10 + 5 + 5 = 72.
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 SenseNova-Loop-9B-Preview +25
Carbon-X 35
Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the inherited template shows the format and no parser is named. Trained tool calling 0, nothing says it was trained to call tools. Structured output 0, not documented. Reasoning control 0, thinking can be switched off but was never trained, and the card asks you to keep it off. Context 5, 40,960 tokens from the base, though Hugging Face trained at 8,192 and under 16K. Most of what the card documents for agents is Carbon-X as a tool another model calls, which this checklist doesn't read.
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 Carbon-X +15
Carbon-X 65
File safety 15, safetensors and GGUF only, but the documented loading path runs the repository's tokenizer.py through trust_remote_code. Provenance 15, the files are in HuggingFaceBio, which Hugging Face's GitHub repository links, with a checksum on every file. Safety evaluation 10, Hugging Face reports its own dual-use check on sequence editing in the launch post, without numbers or a harness. Agent guidance 5, general cautions only. Licence risk 15, plain Apache 2.0. Security programme 5, Hugging Face's security.txt is a reporting route, 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.
Score by category
| Category | Weight this run | Carbon-X | SenseNova-Loop-9B-Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 40 | Carbon-X +50 |
| Risk | 20%20 | 65 | 50 | Carbon-X +15 |
| Agent interface | 10%10 | 35 | 60 | SenseNova-Loop-9B-Preview +25 |
| Documentation and upkeep | 10%10 | 72 | 44 | Carbon-X +28 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 46.8/100 |
Facts side by side
| Fact | Carbon-X | SenseNova-Loop-9B-Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Hugging Face (HuggingFaceBio) | 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 | 2026-10-28 | 2026-11-02 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3/5 (4) | none |
How the scores have moved
SenseNova-Loop-9B-Preview
No score change since it was listed.
Verdicts
Carbon-X
A DNA specialist cousin that another agent calls for variant-effect and sequence questions, with DNA kept on the machine. Hugging Face's Q4_K_M GGUF needs about 12 GB and Q8_0 about 19 GB, on llama.cpp b11742 or later. It isn't clinically validated, isn't trained for tool calls or reasoning, and its Python tokenizer runs repository code.
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
Carbon-X
- Quickest start is
llama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja --chat-template-kwargs '{"enable_thinking": false}' --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0, on llama.cpp b11742 or later - With vLLM or Transformers pass trust_remote_code, and expect the tokenizer to fetch Qwen/Qwen3-4B-Base at a pinned revision on first load
- Keep enable_thinking false and the card's sampling (temperature 0.6, top_p 0.95, top_k 20), the settings every reported result used
- Keep DNA in files and pass sequences by name, compute positions and base changes in code and send them as context, the card says the model miscounts long sequences
- Only A, C, G and T form 6-mers, so filter N and other IUPAC codes first or they become a single oov token
- Strip the dna tags from answers if you need bare sequence, llama.cpp keeps them in the text
- Cap max_tokens for next-base questions, long DNA generation is slow and degrades
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, Carbon-X or SenseNova-Loop-9B-Preview?
Carbon-X scores 61 (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 agent interface.
Other comparisons with Carbon-X or SenseNova-Loop-9B-Preview
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Machine-readable
- This page as Markdown
/compare/carbon-x-vs-sensenova-loop-9b-preview.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/carbon-x.json·/api/v1/tools/sensenova-loop-9b-preview.json - From a terminal
anchor compare carbon-x sensenova-loop-9b-preview(the CLI) - Over MCP
compare_tools {"a": "carbon-x", "b": "sensenova-loop-9b-preview"}at/mcp, no key