# Cola2 vs Qwen3-4B-Instruct-2507 > Qwen3-4B-Instruct-2507 scores 54.5 (C) to Cola2's 33.9 (F) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/cola2-vs-qwen3-4b-instruct-2507 - Markdown: https://www.anchorterminal.com/compare/cola2-vs-qwen3-4b-instruct-2507.md (~9,400 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 Qwen3-4B-Instruct-2507 scores 54.5 (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. - Cola2: grade F, 33.9/100, rank #160 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/cola2.md · JSON https://www.anchorterminal.com/api/v1/tools/cola2.json - Qwen3-4B-Instruct-2507: grade C, 54.5/100, rank #130 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.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 ### Cola2 (F) Good for: Researchers working on latent or diffusion language models who want a checkpoint to study Ahead on: - Documentation and upkeep, 58 against 26 Watch for: No licence stated for the weights ### Qwen3-4B-Instruct-2507 (C) Good for: Agents that want a small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context. Ahead on: - Reach, 90 against 25 - Risk, 55 against 40 - Agent interface, 75 against 20 Watch for: No safety results, prompt-injection guidance or security contact ## Why the edge ### Reach: Qwen3-4B-Instruct-2507 +65 - 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. - Qwen3-4B-Instruct-2507 (90): Memory 30, LM Studio's and Ollama's 4-bit builds are 2.3 to 2.5 GB, about 3 GB with a fifth added, inside 16 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3 architecture, Ollama's released build pins a llama.cpp that has it, and LM Studio's catalogue carries it. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 0, on 4 November 2026 the Hub's provider mapping showed only Featherless AI with status error, Nscale was gone and Alibaba's Model Studio doesn't list it. 30 + 25 + 10 + 15 + 10 + 0 = 90. ### Agent interface: Qwen3-4B-Instruct-2507 +55 - 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. - Qwen3-4B-Instruct-2507 (75): Chat template 20, a Jinja template in tokenizer_config.json. Tool-calling format 25, Qwen's own Function Calling guide documents the Hermes-style declaration and how calls come back, and names vLLM's hermes parser. Trained tool calling 20, the template allows one or more calls a turn. Structured output 0, not documented for the model, and Qwen's docs only point to vLLM's generic guided decoding. Reasoning control 0, this edition has no thinking. Context 10, 262,144 tokens native. Template and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75. ### Documentation and upkeep: Cola2 +32 - 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. - Qwen3-4B-Instruct-2507 (26): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't. Training data 5, the cited Qwen3 report describes the data in general terms without a cut-off. Last change 0, the files last changed on 17 September 2025 (Hub lastModified on 4 November), more than a year ago. Responsiveness 0, counted by script on 4 November, Qwen answered none of the 20 questions and reports. Version policy 5, the older Qwen3-4B stays in its own repository without a note. File health 5, the files agree with the card in substance but reports wait without a reply. 11 + 5 + 0 + 0 + 5 + 5 = 26. ### Risk: Qwen3-4B-Instruct-2507 +15 - 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. - Qwen3-4B-Instruct-2507 (55): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent. Files unchanged on 4 November, and QwenLM/Qwen3 still has no SECURITY.md. 25 + 15 + 0 + 0 + 15 + 0 = 55. ## Score by category | Category | Weight | Cola2 | Qwen3-4B-Instruct-2507 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 25 | 90 | Qwen3-4B-Instruct-2507 +65 | | Risk | 20% (20 this run) | 40 | 55 | Qwen3-4B-Instruct-2507 +15 | | Agent interface | 10% (10 this run) | 20 | 75 | Qwen3-4B-Instruct-2507 +55 | | Documentation and upkeep | 10% (10 this run) | 58 | 26 | Cola2 +32 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **33.9 · F** | **54.5 · C** | | ## Facts side by side | Fact | Cola2 | Qwen3-4B-Instruct-2507 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | ByteDance (Seed) | Alibaba (Qwen team) | | 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 | 2025-09-17 | | 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. ### Qwen3-4B-Instruct-2507 - -1.2 on 4 November 2026: product ## 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. **Qwen3-4B-Instruct-2507.** A small dense instruct model for instruction following, short tool-calling jobs and long documents, with no thinking step. Its 4-bit builds need about 3 GB under Apache 2.0, but no live inference provider was found for it on 4 November 2026. Its files haven't changed in over a year, Alibaba hasn't answered its discussions, and no safety results are published. ## Before you call either ### Cola2 1. Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions 2. Don't use it commercially on the strength of this listing. The weights have no stated licence 3. If you study it, read modeling_cola2.py first: trust_remote_code runs it 4. Pin Transformers below 5, as the card requires 5. Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding 6. Check the download with `sha256sum -c SHA256SUMS` ### Qwen3-4B-Instruct-2507 1. Quickest start is `ollama run qwen3:4b-instruct`, 2.5 GB. Plain `qwen3:4b` is the Thinking-2507 edition, not this one 2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes` 3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags 4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed 5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition 6. Drop the context to 32,768 tokens if memory runs out, as the card suggests 7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented ## Questions ### Which is better for AI agents, Cola2 or Qwen3-4B-Instruct-2507? Qwen3-4B-Instruct-2507 scores 54.5 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/cola2-vs-qwen3-4b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-qwen3-4b-instruct-2507.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cola2", "b": "qwen3-4b-instruct-2507"}`. 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[Qwen3-4B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-thinkingcap-qwen3-8-27b.md) - [Qwen3-4B-Instruct-2507 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-unlimited-ocr-2.md) - [Qwen3-4B-Instruct-2507 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b.md) - [Qwen3-4B-Instruct-2507 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-4b.md) - [Qwen3-4B-Instruct-2507 vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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