Head to head · Open-weight models you run yourself · October 2026 research run
Cola2 vs Ling-3.0-tiny
Ling-3.0-tiny scores 56.2 (C) on agent readiness against Cola2's 33.9 (F), 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
Cola2 F
Best for Researchers working on latent or diffusion language models who want a checkpoint to study
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No licence stated for the weights
Best for Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT
Ahead on
- Reach, 95 against 25
- Risk, 50 against 40
- Agent interface, 60 against 20
Watch for
No inference provider is listed on the Hub and no inclusionAI API serves it
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 Ling-3.0-tiny +70
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.
Ling-3.0-tiny 95
Memory earns the full 30, since inclusionAI's own Q4_K_M GGUF is 4.8 GB, about 6 GB with a fifth added. Runtimes earn the full 25, from released Transformers, vLLM, SGLang and llama.cpp, and Ollama, whose v0.40.14 (2 November 2026) pins llama.cpp b11963 with the bailingmoe3 architecture (Ollama v0.40.7 of 20 October already pinned b11738, which has it). LM Studio has nothing. Builds earn 15 for FP8, INT4 and four GGUF precisions from inclusionAI. MIT with nothing added gives terms the full 15 and the ungated repositories give download the full 10. Hosted too scores 0, because the Hub's model page lists no inference provider and no inclusionAI or Ant Group API serving it was found.
Agent interface Ling-3.0-tiny +40
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.
Ling-3.0-tiny 60
Chat template 20, a Jinja file in the repository. Tool-calling format 10, the format is only in the template and the card names the ling3 parser in commands without describing it, while SGLang uses another parser. Trained tool calling 20, several calls a turn and agentic results on the card. Structured output 0, not documented. Reasoning control 0, thinking turns off per request but the card doesn't say where the reasoning comes back. Context 10, 131,072 tokens native.
Risk Ling-3.0-tiny +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.
Ling-3.0-tiny 50
File safety gets 15, safetensors and GGUF only, but the card's vLLM command runs the repository's config code with --trust-remote-code. Provenance gets 15 for files in inclusionAI's own organisation with a checksum on each. Safety evaluation is 0, since SingProbe's results grade the probe, not the model. Agent guidance gets 5 for the SingProbe guardrail inclusionAI publishes for this model, with no statement of trained refusals. MIT carries no licence deductions (15). No security contact or disclosure policy was found (0).
Score by category
| Category | Weight this run | Cola2 | Ling-3.0-tiny | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 25 | 95 | Ling-3.0-tiny +70 |
| Risk | 20%20 | 40 | 50 | Ling-3.0-tiny +10 |
| Agent interface | 10%10 | 20 | 60 | Ling-3.0-tiny +40 |
| Documentation and upkeep | 10%10 | 58 | 56 | Cola2 +2 |
| Negative events | ≤15 | 0 | 0 | |
| Total | F 33.9/100 | C 56.2/100 |
Facts side by side
| Fact | Cola2 | Ling-3.0-tiny |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | ByteDance (Seed) | inclusionAI (Ant Group) |
| 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) | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-06 | 2026-08-31 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (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.
Ling-3.0-tiny
A small, quick hybrid reasoning model for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with no host. The risks are a card that doesn't match the files in places, a vLLM route running repository code, and no safety results.
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
Ling-3.0-tiny
- Start it with
llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768, which needs llama.cpp b11772 or a recent release with bailingmoe3 - With vLLM 0.32.0,
vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3, which runs the repository's config code - With SGLang 0.5.22, its cookbook uses
--reasoning-parser deepseek-r1 --tool-call-parser glm45for this model, and drop the card's NEXTN flags since tiny has no MTP layer - Thinking is on by default, pass
chat_template_kwargswithenable_thinkingfalse for short tasks - Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
- The template keeps earlier reasoning in the prompt, which grows long agent sessions
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
- For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches
Questions
Which is better for AI agents, Cola2 or Ling-3.0-tiny?
Ling-3.0-tiny scores 56.2 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories.
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Machine-readable
- This page as Markdown
/compare/cola2-vs-ling-3-0-tiny.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/cola2.json·/api/v1/tools/ling-3-0-tiny.json - From a terminal
anchor compare cola2 ling-3-0-tiny(the CLI) - Over MCP
compare_tools {"a": "cola2", "b": "ling-3-0-tiny"}at/mcp, no key