# Kolibri-1 vs Nanbeige4.3-33B > Kolibri-1 and Nanbeige4.3-33B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b - Markdown: https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.md (~6,450 tokens) - Slim: https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.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: 2026-10-25 Kolibri-1 and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself. - Kolibri-1: grade C, 61/100, rank #40 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json - Nanbeige4.3-33B: grade C, 61/100, rank #56 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 3341 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### 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, 60 against 55 - Risk, 65 against 60 - Agent interface, 90 against 85 - Documentation and upkeep, 70 against 56 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 ### Nanbeige4.3-33B (C) Good for: Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results. Watch for: No independent benchmark result yet; every agentic number on the card is Nanbeige's own run ## Score by category | Category | Weight | Kolibri-1 | Nanbeige4.3-33B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 55 | Kolibri-1 +5 | | Risk | 20% (20 this run) | 65 | 60 | Kolibri-1 +5 | | Agent interface | 10% (10 this run) | 90 | 85 | Kolibri-1 +5 | | Documentation and upkeep | 10% (10 this run) | 70 | 56 | Kolibri-1 +14 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | Kolibri-1 | Nanbeige4.3-33B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Aleph Alpha | Nanbeige LLM Lab (BOSS Zhipin) | | 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 (Hub metadata only; the repository has no LICENSE file) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-19 | 2026-10-24 | | 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 | 3.3/5 (4) | none | ## Verdicts **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 through its plugin, Transformers 5.20 or recent llama.cpp. The main risk is no published safety results or security contact, and few repository answers. **Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file. ## Before you call either ### 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, so don't upgrade vLLM while it's installed 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 9. Validate tool results in your own code, as the card asks ### Nanbeige4.3-33B 1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers 2. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes 3. Pass `chat_template_kwargs` `{"preserve_thinking": true}` for multi-turn tool use and coding, and `{"enable_thinking": false}` to skip reasoning, as the card recommends 4. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card 5. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed 6. Send one system message at the start only, as the template raises an error for a later one 7. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed ## Questions ### Which is better for AI agents, Kolibri-1 or Nanbeige4.3-33B? Kolibri-1 and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 61 (C). ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kolibri-1", "b": "nanbeige-4-3-33b"}`. 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[Nanbeige4.3-33B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b.md) - [Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b.md) - [Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-flash-next.md) - [Nanbeige4.3-33B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-thinkingcap-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