Head to head · Open-weight models you run yourself · October 2026 research run
Cola2 vs Spark-X2.6-4B
Spark-X2.6-4B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 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
Cola2 F
Best for Researchers working on latent or diffusion language models who want a checkpoint to study
Ahead on
- Documentation and upkeep, 58 against 51
Watch for
No licence stated for the weights
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 25
- Risk, 50 against 40
- Agent interface, 70 against 20
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 +60
Cola2 25
Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. 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 nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.
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 +50
Cola2 20
Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.
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.
Risk Spark-X2.6-4B +10
Cola2 40
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.
Spark-X2.6-4B 50
File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python. Provenance 15, the files are in SparkLLM's own Hub organisation, which its GitHub docs name, and every file has a checksum, though neither organisation is verified and no company site was found. Safety evaluation 0 and agent guidance 0, none published. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 5 of 10, a private reporting route and email in SparkLLM's community repository, with no public advisories.
Documentation and upkeep Cola2 +7
Cola2 58
Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.
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 | Cola2 | Spark-X2.6-4B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 25 | 85 | Spark-X2.6-4B +60 |
| Risk | 20%20 | 40 | 50 | Spark-X2.6-4B +10 |
| Agent interface | 10%10 | 20 | 70 | Spark-X2.6-4B +50 |
| Documentation and upkeep | 10%10 | 58 | 51 | Cola2 +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | F 33.9/100 | C 61/100 |
Facts side by side
| Fact | Cola2 | Spark-X2.6-4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | ByteDance (Seed) | SparkLLM (XHToken) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | None stated for the weights (the code is Apache 2.0) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-06 | 2026-10-20 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
Cola2
No score change since it was listed.
Verdicts
Cola2
A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.
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
Cola2
- Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions
- Don't use it commercially on the strength of this listing. The weights have no stated licence
- If you study it, read modeling_cola2.py first: trust_remote_code runs it
- Pin Transformers below 5, as the card requires
- Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding
- Check the download with
sha256sum -c SHA256SUMS
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, Cola2 or Spark-X2.6-4B?
Spark-X2.6-4B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.
Other comparisons with Cola2 or Spark-X2.6-4B
- Agents-A1-4B vs Cola2
- Agents-A1-4B vs Spark-X2.6-4B
- Apertus v1.5 8B vs Cola2
- Apertus v1.5 8B vs Spark-X2.6-4B
- Apodex-1.1-mini vs Cola2
- Apodex-1.1-mini vs Spark-X2.6-4B
- Arete-27B vs Cola2
- Arete-27B vs Spark-X2.6-4B
- BigBang-v1 vs Cola2
- BigBang-v1 vs Spark-X2.6-4B
- Carbon-X vs Cola2
- Carbon-X vs Spark-X2.6-4B
- Cola2 vs Cosmos-Reason2-2B
- Cola2 vs Darwin-36B-Opus
- Cola2 vs DeepSeek-V3.2
- Cola2 vs DeepSeek V4.1 Flash
- Cola2 vs DeepSeek-V4-Flash-0731
- Cola2 vs Devstral Small 2
- Cola2 vs DiffusionGemma 26B A4B IT
- Cola2 vs EXAONE 4.5 33B
- Cola2 vs Gemma 4 12B IT
- Cola2 vs Gemma 4 26B A4B IT
- Cola2 vs Gemma 4 31B IT
- Cola2 vs Gemma 4 E2B IT
- Cola2 vs Gemma 4 E4B IT
- Cola2 vs GLM-4.5-Air
- Cola2 vs GLM-4.7-Flash
- Cola2 vs GLM-5.3
- Cola2 vs GLM-5.3-Flash
- Cola2 vs gpt-oss-120b
- Cola2 vs gpt-oss-20b
- Cola2 vs Granite 4.2 30B
- Cola2 vs Granite 4.2 3B
- Cola2 vs Granite 4.2 8B
- Cola2 vs Hy-Memory-30B-A3B
- Cola2 vs Hy3
- Cola2 vs Inkling
- Cola2 vs Inkling-Small
- Cola2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Cola2 vs Kimi K3
- Cola2 vs Kimi K3.1 Pro
- Cola2 vs Kolibri-1
- Cola2 vs Kronos
- Cola2 vs Laguna M.1
- Cola2 vs Laguna S 2.1
- Cola2 vs Laguna XS 2.1
- Cola2 vs LBS-27B
- Cola2 vs LFM2.5-2.6B
- Cola2 vs LFM3-1.7B
- Cola2 vs LFM3-Audio-2B
- Cola2 vs LFM3-Omni-2B
- Cola2 vs LFM3-VL-2B
- Cola2 vs Ling-3.0-tiny
- Cola2 vs Ling-3.1-flash-1019
- Cola2 vs LongCat-2.5
- Cola2 vs MiMo-V2.6 Distill Qwen 9B
- Cola2 vs MiMo-V2.6-Flash-RL
- Cola2 vs MiniCPM-o 4.5
- Cola2 vs MiniCPM-V 4.7 1B
- Cola2 vs MiniCPM-V 4.7 35B-A3B
- Cola2 vs MiniCPM5-2B
- Cola2 vs MiniMax-M2.5
- Cola2 vs MiniMax-M2.7
- Cola2 vs MiniMax-M3.1-Flash
- Cola2 vs Mistral Large 4
- Cola2 vs Muse Glimmer 30B
- Cola2 vs Nanbeige4.2-3B
- Cola2 vs Nanbeige4.3-33B
- Cola2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Cola2 vs Nemotron 3.5 Lightning 30B A3B
- Cola2 vs Nemotron 3 Nano 30B A3B
- Cola2 vs Nemotron 3 Nano 4B
- Cola2 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Cola2 vs Nemotron 3 Super 120B A12B
- Cola2 vs Nemotron 3 Ultra 550B A55B
- Cola2 vs North Mini Code 1.0
- Cola2 vs occamy-1.0
- Cola2 vs Olmo 3 7B Think
- Cola2 vs Ornith-1.5-122B
- Cola2 vs Ornith-1.5-35B-A3B
- Cola2 vs Ornith-1.5-397B
- Cola2 vs Ornith-1.5-9B
- Cola2 vs OTel-2.0-LLM-31B-IT
- Cola2 vs Qwen-AgentWorld-35B-A3B
- Cola2 vs Qwen3-30B-A3B-Instruct-2507
- Cola2 vs Qwen3-4B-Instruct-2507
- Cola2 vs Qwen3.5-0.8B
- Cola2 vs Qwen3.5-122B-A10B
- Cola2 vs Qwen3.5-2B
- Cola2 vs Qwen3.5-397B-A17B
- Cola2 vs Qwen3.5-4B
- Cola2 vs Qwen3.5-9B
- Cola2 vs Qwen3.6-27B
- Cola2 vs Qwen3.6-35B-A3B
- Cola2 vs Qwen3.8-27B
- Cola2 vs Qwen3.8-Flash-Next
- Cola2 vs Qwen3-Coder-30B-A3B-Instruct
- Cola2 vs Qwen3-Coder-Next
- Cola2 vs Qwen3-Omni-30B-A3B-Instruct
- Cola2 vs Qwen3-VL-2B-Instruct
- Cola2 vs Qwen3-VL-30B-A3B-Instruct
- Cola2 vs Qwen3-VL-32B-Instruct
- Cola2 vs Qwen3-VL-4B-Instruct
- Cola2 vs Qwen3-VL-8B-Instruct
- Cola2 vs Qwopus3.8-27B-Flash-V2
- Cola2 vs Recursive Flint
- Cola2 vs Ripple-65B Preview
- Cola2 vs SenseNova-Loop-9B-Preview
- Cola2 vs SmolLM3-3B
- Cola2 vs Step 3.7 Flash
- Cola2 vs Swift 1.5 Qwen3.8-27B
- Cola2 vs Swift 1.5 Qwen3.8-Flash-Next
- Cola2 vs ThinkingCap (Qwen3.8 27B)
- Cola2 vs Baidu Unlimited-OCR-2
- Cola2 vs WeVisOne-VL-2B
- Cola2 vs WeVisOne-VL-4B
- Cola2 vs ZAYA1-8B
- Cosmos-Reason2-2B vs Spark-X2.6-4B
- Darwin-36B-Opus vs Spark-X2.6-4B
- DeepSeek-V3.2 vs Spark-X2.6-4B
- DeepSeek V4.1 Flash vs Spark-X2.6-4B
- DeepSeek-V4-Flash-0731 vs Spark-X2.6-4B
- Devstral Small 2 vs Spark-X2.6-4B
- DiffusionGemma 26B A4B IT vs Spark-X2.6-4B
- EXAONE 4.5 33B vs Spark-X2.6-4B
- Gemma 4 12B IT vs Spark-X2.6-4B
- Gemma 4 26B A4B IT vs Spark-X2.6-4B
- Gemma 4 31B IT vs Spark-X2.6-4B
- Gemma 4 E2B IT vs Spark-X2.6-4B
- Gemma 4 E4B IT vs Spark-X2.6-4B
- GLM-4.5-Air vs Spark-X2.6-4B
- GLM-4.7-Flash vs Spark-X2.6-4B
- GLM-5.3-Flash vs Spark-X2.6-4B
- GLM-5.3 vs Spark-X2.6-4B
- gpt-oss-120b vs Spark-X2.6-4B
- gpt-oss-20b vs Spark-X2.6-4B
- Granite 4.2 30B vs Spark-X2.6-4B
- Granite 4.2 3B vs Spark-X2.6-4B
- Granite 4.2 8B vs Spark-X2.6-4B
- Hy-Memory-30B-A3B vs Spark-X2.6-4B
- Hy3 vs Spark-X2.6-4B
- Inkling-Small vs Spark-X2.6-4B
- Inkling vs Spark-X2.6-4B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Spark-X2.6-4B
- Kimi K3.1 Pro vs Spark-X2.6-4B
- Kimi K3 vs Spark-X2.6-4B
- Kolibri-1 vs Spark-X2.6-4B
- Kronos vs Spark-X2.6-4B
- Laguna M.1 vs Spark-X2.6-4B
- Laguna S 2.1 vs Spark-X2.6-4B
- Laguna XS 2.1 vs Spark-X2.6-4B
- LBS-27B vs Spark-X2.6-4B
- LFM2.5-2.6B vs Spark-X2.6-4B
- LFM3-1.7B vs Spark-X2.6-4B
- LFM3-Audio-2B vs Spark-X2.6-4B
- LFM3-Omni-2B vs Spark-X2.6-4B
- LFM3-VL-2B vs Spark-X2.6-4B
- Ling-3.0-tiny vs Spark-X2.6-4B
- Ling-3.1-flash-1019 vs Spark-X2.6-4B
- LongCat-2.5 vs Spark-X2.6-4B
- MiMo-V2.6 Distill Qwen 9B vs Spark-X2.6-4B
- MiMo-V2.6-Flash-RL vs Spark-X2.6-4B
- MiniCPM-o 4.5 vs Spark-X2.6-4B
- MiniCPM-V 4.7 1B vs Spark-X2.6-4B
- MiniCPM-V 4.7 35B-A3B vs Spark-X2.6-4B
- MiniCPM5-2B vs Spark-X2.6-4B
- MiniMax-M2.5 vs Spark-X2.6-4B
- MiniMax-M2.7 vs Spark-X2.6-4B
- MiniMax-M3.1-Flash vs Spark-X2.6-4B
- Mistral Large 4 vs Spark-X2.6-4B
- Muse Glimmer 30B vs Spark-X2.6-4B
- Nanbeige4.2-3B vs Spark-X2.6-4B
- Nanbeige4.3-33B vs Spark-X2.6-4B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Spark-X2.6-4B
- Nemotron 3.5 Lightning 30B A3B vs Spark-X2.6-4B
- Nemotron 3 Nano 30B A3B vs Spark-X2.6-4B
- Nemotron 3 Nano 4B vs Spark-X2.6-4B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Spark-X2.6-4B
- Nemotron 3 Super 120B A12B vs Spark-X2.6-4B
- Nemotron 3 Ultra 550B A55B vs Spark-X2.6-4B
- North Mini Code 1.0 vs Spark-X2.6-4B
- occamy-1.0 vs Spark-X2.6-4B
- Olmo 3 7B Think vs Spark-X2.6-4B
- Ornith-1.5-122B vs Spark-X2.6-4B
- Ornith-1.5-35B-A3B vs Spark-X2.6-4B
- Ornith-1.5-397B vs Spark-X2.6-4B
- Ornith-1.5-9B vs Spark-X2.6-4B
- OTel-2.0-LLM-31B-IT vs Spark-X2.6-4B
- Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B
- Qwen3-30B-A3B-Instruct-2507 vs Spark-X2.6-4B
- Qwen3-4B-Instruct-2507 vs Spark-X2.6-4B
- Qwen3.5-0.8B vs Spark-X2.6-4B
- Qwen3.5-122B-A10B vs Spark-X2.6-4B
- Qwen3.5-2B vs Spark-X2.6-4B
- Qwen3.5-397B-A17B vs Spark-X2.6-4B
- Qwen3.5-4B vs Spark-X2.6-4B
- Qwen3.5-9B vs Spark-X2.6-4B
- Qwen3.6-27B vs Spark-X2.6-4B
- Qwen3.6-35B-A3B vs Spark-X2.6-4B
- Qwen3.8-27B vs Spark-X2.6-4B
- Qwen3.8-Flash-Next vs Spark-X2.6-4B
- Qwen3-Coder-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-Coder-Next vs Spark-X2.6-4B
- Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-2B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-32B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-4B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-8B-Instruct vs Spark-X2.6-4B
- Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B
- Recursive Flint vs Spark-X2.6-4B
- Ripple-65B Preview vs Spark-X2.6-4B
- SenseNova-Loop-9B-Preview vs Spark-X2.6-4B
- SmolLM3-3B vs Spark-X2.6-4B
- Spark-X2.6-4B vs Step 3.7 Flash
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next
- Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)
- Spark-X2.6-4B vs Baidu Unlimited-OCR-2
- Spark-X2.6-4B vs WeVisOne-VL-2B
- Spark-X2.6-4B vs WeVisOne-VL-4B
- Spark-X2.6-4B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/cola2-vs-spark-x2-6-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/cola2.json·/api/v1/tools/spark-x2-6-4b.json - From a terminal
anchor compare cola2 spark-x2-6-4b(the CLI) - Over MCP
compare_tools {"a": "cola2", "b": "spark-x2-6-4b"}at/mcp, no key