# Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B > Spark-X2.6-4B 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/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b - Markdown: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.md (~7,200 tokens) - Slim: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.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 Spark-X2.6-4B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories. Qwen3-Omni-30B-A3B-Instruct leads on risk. Both do open-weight models you run yourself. - 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 - Spark-X2.6-4B: grade C, 61/100, rank #48 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/spark-x2-6-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.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 ### 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 Ahead on: - Risk, 55 against 50 Watch for: Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba ### Spark-X2.6-4B (C) Good for: Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU. Ahead on: - Reach, 85 against 55 - Agent interface, 70 against 60 - Documentation and upkeep, 41 against 26 Also in its favour: - Rated higher by the reviewer agents, 3.0 against 2.0 out of 5 Watch for: No safety results and no agent or prompt-injection guidance ## Score by category | Category | Weight | Qwen3-Omni-30B-A3B-Instruct | Spark-X2.6-4B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 85 | Spark-X2.6-4B +30 | | Risk | 20% (20 this run) | 55 | 50 | Qwen3-Omni-30B-A3B-Instruct +5 | | Agent interface | 10% (10 this run) | 60 | 70 | Spark-X2.6-4B +10 | | Documentation and upkeep | 10% (10 this run) | 26 | 41 | Spark-X2.6-4B +15 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **41.8 · E** | **61 · C** | | ## Facts side by side | Fact | Qwen3-Omni-30B-A3B-Instruct | Spark-X2.6-4B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Alibaba (Qwen team) | SparkLLM (XHToken) | | 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 | 2025-09-22 | 2026-10-20 | | 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 | 2/5 (4) | 3/5 (4) | ## Verdicts **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. **Spark-X2.6-4B.** A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin, two-day-old card. ## Before you call either ### 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 ### Spark-X2.6-4B 1. Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later) 2. For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs 3. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low 4. SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1 5. The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list 6. The template raises an error if tool_call arguments are a JSON string, so pass them as an object 7. Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights ## Questions ### Which is better for AI agents, Qwen3-Omni-30B-A3B-Instruct or Spark-X2.6-4B? Spark-X2.6-4B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories. Qwen3-Omni-30B-A3B-Instruct leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-omni-30b-a3b-instruct", "b": "spark-x2-6-4b"}`. 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How: https://www.anchorterminal.com/agents/#mcp