Head to head · Open-weight models you run yourself · October 2026 research run
SenseNova-Loop-9B-Preview vs Spark-X2.6-4B
Spark-X2.6-4B 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 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
Best for Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.
Ahead on
- Reach, 85 against 40
- Agent interface, 70 against 60
- Documentation and upkeep, 51 against 44
Watch for
No safety results and no agent or prompt-injection guidance
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 Spark-X2.6-4B +45
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.
Spark-X2.6-4B 85
Memory 30, SparkLLM's Q4_K_M GGUF is 3.19 GB, about 3.8 GB with a fifth added, which fits 8 GB. Runtimes 15 of 25, released llama.cpp, SGLang (to v0.5.23) and Ollama run the spark2_5 architecture X2.6 keeps, while vLLM needs SparkLLM's plugin, Transformers needs the repository's code and LM Studio has no catalogue entry. Builds 15, SparkLLM publishes FP8, INT8, INT4 and GGUF builds. Terms 15, Apache 2.0 with nothing added, on its own base. Download 10, nothing is gated. Hosted 0, no provider or SparkLLM API serves it.
Agent interface Spark-X2.6-4B +10
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.
Spark-X2.6-4B 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the spark25 parser name only in the SGLang command. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, a low or high effort is set in the template and the qwen3 parser returns the reasoning apart. Context 10, 1,048,576 tokens with no scaling.
Documentation and upkeep Spark-X2.6-4B +7
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.
Spark-X2.6-4B 51
Model card 11 of 20, intended use, parameters, context and languages, with no limitations, cut-off or architecture description of its own. Training data 0, the X2.6 card describes none. Last change 20, the card changed on 1 November 2026. Responsiveness 10, SparkLLM members answered 11 of 40 discussions. Version policy 5, X2.5 stays in place with no note. File health 5, the tokenizer config's 131,072 disagrees with the 1M context.
Score by category
| Category | Weight this run | SenseNova-Loop-9B-Preview | Spark-X2.6-4B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 40 | 85 | Spark-X2.6-4B +45 |
| Risk | 20%20 | 50 | 50 | even |
| Agent interface | 10%10 | 60 | 70 | Spark-X2.6-4B +10 |
| Documentation and upkeep | 10%10 | 44 | 51 | Spark-X2.6-4B +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 46.8/100 | C 61/100 |
Facts side by side
| Fact | SenseNova-Loop-9B-Preview | Spark-X2.6-4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | SenseTime (SenseNova) | SparkLLM (XHToken) |
| 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 | 2026-11-02 | 2026-10-20 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
SenseNova-Loop-9B-Preview
No score change since it was listed.
Verdicts
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.
Spark-X2.6-4B
A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data.
Before you call either
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
Spark-X2.6-4B
- Quickest start is
llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja, which needs llama.cpp b10828 or later, orollama run SparkLLM/Spark-X2.6-4B(8.2 GB, Ollama 0.34.1 or later) - For parsed tool calls serve it with SGLang 0.5.22 or later,
--tool-call-parser spark25 --reasoning-parser qwen3, since the calls come back as tool_call blocks with arg_key and arg_value pairs - Thinking is on by default. Pass
chat_template_kwargswithenable_thinkingfalse to turn it off, orreasoning_effortlow for short thinking; the template raises an error on any effort other than high or low - SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1
- The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list
- The template raises an error if tool_call arguments are a JSON string, so pass them as an object
- Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights
Questions
Which is better for AI agents, SenseNova-Loop-9B-Preview or Spark-X2.6-4B?
Spark-X2.6-4B 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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Machine-readable
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anchor compare sensenova-loop-9b-preview spark-x2-6-4b(the CLI) - Over MCP
compare_tools {"a": "sensenova-loop-9b-preview", "b": "spark-x2-6-4b"}at/mcp, no key