# InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna XS 2.1 > Laguna XS 2.1 scores 61 (C) to InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-xs-2-1 - Markdown: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-xs-2-1.md (~6,950 tokens) - Slim: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-xs-2-1.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-xs-2-1.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 Laguna XS 2.1 scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself. - InternVL3.5-GPT-OSS-20B-A4B-Preview: grade D, 51.2/100, rank #90 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json - Laguna XS 2.1: grade C, 61/100, rank #40 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.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 ## Which one, for what ### InternVL3.5-GPT-OSS-20B-A4B-Preview (D) Good for: Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview. Watch for: No quantised build from OpenGVLab, so about 51 GB at BF16 ### Laguna XS 2.1 (C) Good for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence. Ahead on: - Reach, 85 against 60 - Agent interface, 85 against 45 - Documentation and upkeep, 46 against 28 Also in its favour: - Rated higher by the reviewer agents, 2.8 against 1.8 out of 5 Watch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code ## Score by category | Category | Weight | InternVL3.5-GPT-OSS-20B-A4B-Preview | Laguna XS 2.1 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 85 | Laguna XS 2.1 +25 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 45 | 85 | Laguna XS 2.1 +40 | | Documentation and upkeep | 10% (10 this run) | 28 | 46 | Laguna XS 2.1 +18 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **51.2 · D** | **61 · C** | | ## Facts side by side | Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | Laguna XS 2.1 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenGVLab (Shanghai AI Laboratory) | Poolside | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | OpenMDW-1.1 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2025-09-08 | 2026-08-19 | | Terms last updated | no document linked | | | Privacy policy last updated | 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 | 1.8/5 (4) | 2.8/5 (4) | ## Verdicts **InternVL3.5-GPT-OSS-20B-A4B-Preview.** An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results. **Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code. ## Before you call either ### InternVL3.5-GPT-OSS-20B-A4B-Preview 1. Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM 2. Check the rendered prompt before relying on it, since the shipped template's <|im_start|> markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative 3. Use Transformers 4.55.0 or later, as the card requires for this size 4. For thinking, send the card's thinking system prompt and parse the reasoning out of the tags, with temperature 0.6 and sampling on as the card advises 5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says ### Laguna XS 2.1 1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF 2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB) 3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds 4. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding 5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped 6. Sampling per the card is temperature 1.0 and top_k 20 7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships 8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented ## Questions ### Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or Laguna XS 2.1? Laguna XS 2.1 scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-xs-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-xs-2-1.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "internvl3-5-gpt-oss-20b-a4b-preview", "b": "laguna-xs-2-1"}`. 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