# Spark-X2.6-4B vs WeVisOne-VL-2B > Spark-X2.6-4B and WeVisOne-VL-2B 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/spark-x2-6-4b-vs-wevisone-vl-2b - Markdown: https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.md (~8,800 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 Spark-X2.6-4B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk. Both do open-weight models you run yourself. - Spark-X2.6-4B: grade C, 61/100, rank #56 of 160 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 - WeVisOne-VL-2B: grade C, 61/100, rank #83 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.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 ### 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 70 Watch for: No safety results and no agent or prompt-injection guidance ### WeVisOne-VL-2B (C) Good for: Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence Ahead on: - Risk, 55 against 50 Watch for: The pixel decoder ships as a pickle file ## Why the edge ### Reach: Spark-X2.6-4B +15 - Spark-X2.6-4B (85): Memory 30, SparkLLM's Q4_K_M GGUF is 3.19 GB, about 3.8 GB with a fifth added, which fits 8 GB. Runtimes 15 of 25, released llama.cpp, SGLang (to v0.5.23) and Ollama run the spark2_5 architecture X2.6 keeps, while vLLM needs SparkLLM's plugin, Transformers needs the repository's code and LM Studio has no catalogue entry. Builds 15, SparkLLM publishes FP8, INT8, INT4 and GGUF builds. Terms 15, Apache 2.0 with nothing added, on its own base. Download 10, nothing is gated. Hosted 0, no provider or SparkLLM API serves it. - WeVisOne-VL-2B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. 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. 30 + 15 + 0 + 15 + 10 + 0 = 70. ### Risk: WeVisOne-VL-2B +5 - Spark-X2.6-4B (50): File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python. Provenance 15, the files are in SparkLLM's own Hub organisation, which its GitHub docs name, and every file has a checksum, though neither organisation is verified and no company site was found. Safety evaluation 0 and agent guidance 0, none published. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 5 of 10, a private reporting route and email in SparkLLM's community repository, with no public advisories. - WeVisOne-VL-2B (55): File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55. ## Score by category | Category | Weight | Spark-X2.6-4B | WeVisOne-VL-2B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 70 | Spark-X2.6-4B +15 | | Risk | 20% (20 this run) | 50 | 55 | WeVisOne-VL-2B +5 | | Agent interface | 10% (10 this run) | 70 | 70 | even | | Documentation and upkeep | 10% (10 this run) | 51 | 48 | Spark-X2.6-4B +3 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | Spark-X2.6-4B | WeVisOne-VL-2B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | SparkLLM (XHToken) | Tencent (WeChat Vision) | | 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 | 2026-10-20 | 2026-10-28 | | Terms last updated | no document linked | not read yet | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 3/5 (4) | none | ## How the scores have moved ### Spark-X2.6-4B - no change on 3 November 2026: new evidence ### WeVisOne-VL-2B No score change since it was listed. ## Verdicts **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 card with no training data. **WeVisOne-VL-2B.** A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results. ## Before you call either ### 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 ### WeVisOne-VL-2B 1. Use Tencent's package: `WeVisOneVL("tencent/WeVisOne-VL-2B", backend="vllm")` then `model.predict(image, task=..., query=...)` 2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting 3. A None answer means the target isn't in the image. Treat it as a result, not an error 4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's 5. Send one image a turn. Video grids are rejected by the decoder 6. The card says it isn't for identifying individuals or for high-impact decisions on its own ## Questions ### Which is better for AI agents, Spark-X2.6-4B or WeVisOne-VL-2B? Spark-X2.6-4B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "spark-x2-6-4b", "b": "wevisone-vl-2b"}`. 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