# Cola2 vs Kolibri-1 > Kolibri-1 scores 61 (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-kolibri-1 - Markdown: https://www.anchorterminal.com/compare/cola2-vs-kolibri-1.md (~8,750 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 Kolibri-1 scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category. 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 - Kolibri-1: grade C, 61/100, rank #44 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.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 Watch for: No licence stated for the weights ### Kolibri-1 (C) Good for: German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters. Ahead on: - Reach, 65 against 25 - Risk, 65 against 40 - Agent interface, 90 against 20 - Documentation and upkeep, 70 against 58 Watch for: The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine ## Why the edge ### Agent interface: Kolibri-1 +70 - 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. - Kolibri-1 (90): The chat template earns 20 as Jinja in tokenizer_config.json. The tool-calling format earns 25, because the card documents how tools are declared and how calls come back and names the kolibri1 parser for vLLM with a full example. Trained tool calling earns 20, from training on tool environments and a template built for several calls in one turn. Structured output earns 5, since the card claims it without saying how to ask. Reasoning control earns 10 for effort levels and an off switch per request, with the reasoning returned in a separate field. Context earns 10 for 262,144 tokens trained. ### Reach: Kolibri-1 +40 - 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. - Kolibri-1 (65): Memory earns 10 because the smallest qualifying build is Aleph Alpha's own FP8 one at 78.8 GB, which comes to 94.6 GB with the fifth added and so fits only the reference machine's band. There is no publisher 4-bit or GGUF build and no Ollama library entry to bring it lower. Runtimes earn 20, for released Transformers 5.20.0, released llama.cpp builds from b11709, Ollama 0.40.14, which builds llama.cpp b11963 with kolibri1, and vLLM, through Aleph Alpha's plugin for 0.29 and natively in 0.33.0 through its Transformers backend. LM Studio's latest runtime predates kolibri1 and SGLang 0.5.23 has no model file. Builds earn 10 for the one FP8 quantisation beside BF16. Terms earn 15 for Apache 2.0 with no use policy. Download earns 10 because neither repository is gated. Hosted earns 0, since no inference provider is listed on the Hub and Aleph Alpha names no API serving it. ### Risk: Kolibri-1 +25 - 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. - Kolibri-1 (65): File safety earns 25 for safetensors only, with no repository code run on load. Provenance earns 15, for the Aleph-Alpha organisation the launch post links and a SHA-256 for every weight shard. Safety evaluation earns 0, because the card says refusals were measured and neither the card nor the technical report publishes results. Agent guidance earns 10, since the card says what the trained refusals cover and which safeguards to add around the model, without prompt-injection guidance. Licence risk earns the full 15 under unchanged Apache 2.0. The security programme earns 0, as no contact, policy or advisories were found and security.txt answered 403 on 3 November, so it is scored as absent and left open. ### Documentation and upkeep: Kolibri-1 +12 - 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. - Kolibri-1 (70): The model card earns 20 with all seven items. Training data earns 15 for a data card with the mix, the filtering and the cut-off, short of a dataset list with sizes. The last change earns 20, for template and card commits on 20 October 2026. Responsiveness earns 5, since Aleph Alpha replied in 2 of 14 discussions across both repositories and none since 13 October. Version policy earns 5 for a first release. File health earns 5, because the files agree but a report of a broken tool-call parser in the publisher's container image has waited since 7 October without a reply. ## Score by category | Category | Weight | Cola2 | Kolibri-1 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 25 | 65 | Kolibri-1 +40 | | Risk | 20% (20 this run) | 40 | 65 | Kolibri-1 +25 | | Agent interface | 10% (10 this run) | 20 | 90 | Kolibri-1 +70 | | Documentation and upkeep | 10% (10 this run) | 58 | 70 | Kolibri-1 +12 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **33.9 · F** | **61 · C** | | ## Facts side by side | Fact | Cola2 | Kolibri-1 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | ByteDance (Seed) | Aleph Alpha | | 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 | 2026-10-19 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 3.3/5 (4) | ## How the scores have moved ### Cola2 No score change since it was listed. ### Kolibri-1 - no change on 3 November 2026: product - -14.8 on 21 October 2026 (BB to C): checklist ## 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. **Kolibri-1.** Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM and Transformers 5.20; llama.cpp and Ollama 0.40.14 need a GGUF. The main risk is no published safety results or security contact, and few repository answers. ## 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` ### Kolibri-1 1. Start it with pip install 'aleph-alpha-inference>=1' then vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice 2. Without the kolibri1 tool-call parser and enable-auto-tool-choice, tool calls come back as text in tool_call tags 3. The plugin supports one vLLM minor version, 0.29. vLLM 0.33.0 loads the model without it, but has no kolibri1 tool or reasoning parser 4. Thinking is on by default at high effort. Pass reasoning_effort none or enable_thinking false in chat_template_kwargs for short tasks 5. Recommended sampling is temperature 1.0, top_p 0.97 and top_k 128 6. Past 262,144 tokens, add --max-model-len 1048576 and the max_position_embeddings override in --hf-overrides, though Aleph Alpha recommends staying at or below 262,144 7. With Transformers 5.20 or later, tokenizer.parse_response returns the answer in content and the reasoning in thinking 8. On the reference machine the FP8 build fits by size, but no document confirms vLLM runs FP8 on gfx1151, and Aleph Alpha ships no GGUF for llama.cpp or Ollama 9. Validate tool results in your own code, as the card asks ## Questions ### Which is better for AI agents, Cola2 or Kolibri-1? Kolibri-1 scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/cola2-vs-kolibri-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-kolibri-1.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cola2", "b": "kolibri-1"}`. 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[Kolibri-1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kolibri-1-vs-spark-x2-6-4b.md) - [Kolibri-1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kolibri-1-vs-step-3-7-flash.md) - [Kolibri-1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b.md) - [Kolibri-1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-flash-next.md) - [Kolibri-1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/kolibri-1-vs-thinkingcap-qwen3-8-27b.md) - [Kolibri-1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2.md) - [Kolibri-1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b.md) - [Kolibri-1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b.md) - [Kolibri-1 vs ZAYA1-8B](https://www.anchorterminal.com/compare/kolibri-1-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp