# Kolibri-1 vs Qwen3-Omni-30B-A3B-Instruct > Kolibri-1 scores 61 (C) to Qwen3-Omni-30B-A3B-Instruct's 41.8 (E) 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-omni-30b-a3b-instruct - Markdown: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-omni-30b-a3b-instruct.md (~7,050 tokens) - Slim: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-omni-30b-a3b-instruct.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-omni-30b-a3b-instruct.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 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category. Both do open-weight models you run yourself. - Kolibri-1: grade C, 61/100, rank #38 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json - Qwen3-Omni-30B-A3B-Instruct: grade E, 41.8/100, rank #99 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-omni-30b-a3b-instruct.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md - Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md - All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/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 55 - Agent interface, 90 against 60 - Documentation and upkeep, 70 against 26 Also in its favour: - Rated higher by the reviewer agents, 3.3 against 2.0 out of 5 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 ### Qwen3-Omni-30B-A3B-Instruct (E) Good for: Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice Watch for: Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba ## Score by category | Category | Weight | Kolibri-1 | Qwen3-Omni-30B-A3B-Instruct | 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 | 55 | Kolibri-1 +10 | | Agent interface | 10% (10 this run) | 90 | 60 | Kolibri-1 +30 | | Documentation and upkeep | 10% (10 this run) | 70 | 26 | Kolibri-1 +44 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **41.8 · E** | | ## Facts side by side | Fact | Kolibri-1 | Qwen3-Omni-30B-A3B-Instruct | | --- | --- | --- | | 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 | 2025-09-22 | | 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) | 2/5 (4) | ## 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. **Qwen3-Omni-30B-A3B-Instruct.** An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions. ## 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 ### Qwen3-Omni-30B-A3B-Instruct 1. For text answers, `mkdir -p media && vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path "$(pwd)/media"` with vLLM 0.11.1 or later serves the Thinker on an OpenAI-compatible API 2. The card's command passes `--allowed-local-media-path /`, which lets anyone who can reach the server have it read any file on the machine through a file:// URL. Point it at a dedicated folder that holds only the media you mean to send, as above, and give vLLM an absolute path 3. Send audio as `audio_url` and images as `image_url` content parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately 4. For speech out, load Qwen3OmniMoeForConditionalGeneration in Transformers 4.57.0 or later with qwen-omni-utils, pick speaker Ethan, Chelsie or Aiden, and write the returned waveform at 24 kHz 5. Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time 6. Parse tool calls as JSON inside `` tags yourself, since no parser is named, and confirm actions triggered by speech before running them 7. Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise 8. Budget memory for video, the card's table rises from 78.85 GB for 15 seconds to 144.81 GB for 120 seconds in BF16 ## Questions ### Which is better for AI agents, Kolibri-1 or Qwen3-Omni-30B-A3B-Instruct? Kolibri-1 scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-omni-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-omni-30b-a3b-instruct.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kolibri-1", "b": "qwen3-omni-30b-a3b-instruct"}`. 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[Qwen3-Omni-30B-A3B-Instruct vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-underdog-bark-3.md) - [Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-voxtral-mini-4b-realtime-2602.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp