# Agents-A1-4B vs WeVisOne-VL-2B > Agents-A1-4B 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/agents-a1-4b-vs-wevisone-vl-2b - Markdown: https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b.md (~8,650 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 Agents-A1-4B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on documentation and upkeep. Both do open-weight models you run yourself. - Agents-A1-4B: grade C, 61/100, rank #39 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.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 ### Agents-A1-4B (C) Good for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence Ahead on: - Reach, 90 against 70 Watch for: The base isn't declared, and the technical report doesn't cover the 4B ### 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: - Documentation and upkeep, 48 against 43 Watch for: The pixel decoder ships as a pickle file ## Why the edge ### Reach: Agents-A1-4B +20 - Agents-A1-4B (90): Memory 30, InternScience's Q4_K_M GGUF with its vision projector is 3.4 GB and needs about 4.1 GB with the fifth added. Runtimes 20, the Qwen3.5 architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, InternScience publishes FP8 and three GGUF precisions. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, one inference provider serves it and the line needs two. - 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. ### Documentation and upkeep: WeVisOne-VL-2B +5 - Agents-A1-4B (43): Model card 8, it covers intended use, parameters and context but not the architecture, limitations, languages or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 10, the files went up on 14 July 2026, 112 days before 3 November 2026, and the GitHub repository's last commit is 16 July. Responsiveness 10, InternScience answered one of the three discussions, unchanged on 3 November 2026. Version policy 5, a first release. File health 5, the files agree on the essentials, but the config declares a prediction layer that isn't shipped and the template carries a dated default prompt. - WeVisOne-VL-2B (48): Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48. ## Score by category | Category | Weight | Agents-A1-4B | WeVisOne-VL-2B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 70 | Agents-A1-4B +20 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 70 | 70 | even | | Documentation and upkeep | 10% (10 this run) | 43 | 48 | WeVisOne-VL-2B +5 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | Agents-A1-4B | WeVisOne-VL-2B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | InternScience (Shanghai AI Laboratory) | 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 | 2026-07-14 | 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 | ## Verdicts **Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none. **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 ### Agents-A1-4B 1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector 2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work 3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14 4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them 5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention 6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding 7. Validate JSON output yourself, since structured output isn't documented ### 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, Agents-A1-4B or WeVisOne-VL-2B? Agents-A1-4B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "agents-a1-4b", "b": "wevisone-vl-2b"}`. 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[Recursive Flint vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b.md) - [Ripple-65B Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b.md) - [SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.md) - [SmolLM3-3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b.md) - [Spark-X2.6-4B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.md) - [Step 3.7 Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.md) - [Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-2b.md) - [Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b.md) - [ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b.md) - [WeVisOne-VL-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.md) - [WeVisOne-VL-2B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.md) - [Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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