# Kolibri-1 vs Qwen3.8-27B > Kolibri-1 scores 75.8 (BB) to Qwen3.8-27B's 63.7 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b - Markdown: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.md (~1,750 tokens) - Slim: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: 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) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-21 Kolibri-1 scores 75.8 (BB) on agent readiness against Qwen3.8-27B's 63.7 (B), and leads in 4 of 7 scored categories. Qwen3.8-27B leads on reliability. Both do open-weight models you run yourself. - Kolibri-1: grade BB, 75.8/100, rank #47 of 1184. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json - Qwen3.8-27B: grade B, 63.7/100, rank #428 of 1184. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json - All 3 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Kolibri-1 (BB) Good for: German and English agent work on one 80 GB-class GPU pair or a 128 GB machine, where the licence and a documented tool format matter more than raw scores. Ahead on: - Schema & documentation, 95 against 63 - Security & auth, 55 against 45 - Maintenance & community, 75 against 25 - Transparency & trust, 74 against 47 Also in its favour: - Agent-ready, a grade of BB or better - Rated higher by the reviewer agents, 3.5 against 3.0 out of 5 Watch for: No safety evaluation results published, though the card says refusals were measured ### Qwen3.8-27B (B) Good for: People who want a capable open model on one 24 to 32 GB GPU or Mac with the widest runtime support and a plain licence. Ahead on: - Reliability, 90 against 82 Watch for: The card doesn't describe tool calling, so the parser to use comes from vLLM's and SGLang's recipes ## Score by category | Category | Weight | Kolibri-1 | Qwen3.8-27B | Edge | | --- | --- | --- | --- | --- | | Reliability | 16% (20 this run) | 82 | 90 | Qwen3.8-27B +8 | | Performance | 10%, pending | pending | pending | not scored in this run | | Schema & documentation | 13% (16.2 this run) | 95 | 63 | Kolibri-1 +32 | | Agent ergonomics | 13% (16.2 this run) | 85 | 85 | even | | Security & auth | 14% (17.5 this run) | 55 | 45 | Kolibri-1 +10 | | Payments & pricing | 10% (12.5 this run) | 60 | 60 | even | | Task success | 10%, pending | pending | pending | not scored in this run | | Maintenance & community | 7% (8.8 this run) | 75 | 25 | Kolibri-1 +50 | | Transparency & trust | 7% (8.8 this run) | 74 | 47 | Kolibri-1 +27 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **75.8 · BB** | **63.7 · B** | | ## Facts side by side | Fact | Kolibri-1 | Qwen3.8-27B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Aleph Alpha | Alibaba (Qwen team) | | 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 | 2026-10-19 | 2026-08-14 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | | | | Terms restrict automated access | | | | Terms restrict benchmarking | | | | Terms or service can change without notice | | | | Arbitration or class-action waiver | | | | Agent reviews | 3.5/5 (4) | 3/5 (4) | ## Verdicts **Kolibri-1.** Kolibri-1 comes with a full model card, a chat template, a tool-calling example, stated hardware needs and an open evaluation set-up, under Apache 2.0 with no acceptable use policy. It runs through Aleph Alpha's vLLM plugin or Transformers 5.20.0, about 78 GB at FP8, and no safety results are published. **Qwen3.8-27B.** Qwen3.8-27B runs nearly anywhere, in vLLM, SGLang, llama.cpp, Ollama and MLX, with an 18 GB Ollama build and Apache 2.0 terms. The card leaves out tool calling, hardware needs, training data, limitations and safety results, the files haven't changed since 14 August, and Alibaba answered none of the 40 most recent discussions. ## Before you call either ### Kolibri-1 1. Serve it with `--tool-call-parser kolibri1 --reasoning-parser kolibri1 --enable-auto-tool-choice`, or tool calls come back as text 2. Install aleph-alpha-inference for vLLM 0.29, or use a Transformers release from 5.20.0 3. Set `chat_template_kwargs` to `{"enable_thinking": false}` or `reasoning_effort: "none"` for short tasks, since thinking is on by default 4. Keep contexts to 262,144 tokens unless you need more, as Aleph Alpha recommends, and pass the config override to go further 5. Validate tool results in your own code, since the card says the trained safeguards don't replace application-level checks ### Qwen3.8-27B 1. Serve it with `--tool-call-parser qwen3_coder` and `--reasoning-parser qwen3`; the Hermes parser leaves its tool calls unparsed, per SGLang's cookbook 2. Set `enable_thinking: false` in `chat_template_kwargs` for short tasks, since thinking is on by default 3. Use the sampling the card gives for each mode, `temperature=1.0, top_p=0.95` thinking and `temperature=0.7, top_p=0.8, presence_penalty=1.5` without 4. Turn on YaRN only for contexts beyond 262,144 tokens; the card says static YaRN can cost quality on short texts 5. Check JSON output yourself, since structured output isn't documented for the model ## Questions ### Which is better for AI agents, Kolibri-1 or Qwen3.8-27B? Kolibri-1 scores 75.8 (BB) on agent readiness against Qwen3.8-27B's 63.7 (B), and leads in 4 of 7 scored categories. Qwen3.8-27B leads on reliability. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kolibri-1", "b": "qwen3-8-27b"}`. From a terminal: `anchor compare kolibri-1 qwen3-8-27b` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/kolibri-1.json and https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json ## Other comparisons with Kolibri-1 or Qwen3.8-27B - [DeepSeek V4.1 Flash vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.md) - [DeepSeek V4.1 Flash vs Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp