# InternVL3.5-GPT-OSS-20B-A4B-Preview vs ThinkingCap (Qwen3.8 27B) > ThinkingCap 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-thinkingcap-qwen3-8-27b - Markdown: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.md (~7,400 tokens) - Slim: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.min.md (~580 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.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 ThinkingCap (Qwen3.8 27B) 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. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk. 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 - ThinkingCap (Qwen3.8 27B): grade C, 61/100, rank #50 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.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. Ahead on: - Risk, 55 against 45 Watch for: No quantised build from OpenGVLab, so about 51 GB at BF16 ### ThinkingCap (Qwen3.8 27B) (C) Good for: Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac. Ahead on: - Reach, 75 against 60 - Agent interface, 70 against 45 - Documentation and upkeep, 53 against 28 Also in its favour: - Rated higher by the reviewer agents, 2.5 against 1.8 out of 5 Watch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue ## Score by category | Category | Weight | InternVL3.5-GPT-OSS-20B-A4B-Preview | ThinkingCap (Qwen3.8 27B) | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 75 | ThinkingCap (Qwen3.8 27B) +15 | | Risk | 20% (20 this run) | 55 | 45 | InternVL3.5-GPT-OSS-20B-A4B-Preview +10 | | Agent interface | 10% (10 this run) | 45 | 70 | ThinkingCap (Qwen3.8 27B) +25 | | Documentation and upkeep | 10% (10 this run) | 28 | 53 | ThinkingCap (Qwen3.8 27B) +25 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **51.2 · D** | **61 · C** | | ## Facts side by side | Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | ThinkingCap (Qwen3.8 27B) | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenGVLab (Shanghai AI Laboratory) | BottleCap AI | | 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) | PolyForm-Small-Business-1.0.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2025-09-08 | 2026-10-06 | | Terms last updated | no document linked | 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) | 2.5/5 (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. **ThinkingCap (Qwen3.8 27B).** A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description. ## 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 ### ThinkingCap (Qwen3.8 27B) 1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap 2. For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB 3. On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder` 4. Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error 5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop 6. The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor ## Questions ### Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or ThinkingCap (Qwen3.8 27B)? ThinkingCap (Qwen3.8 27B) 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. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "internvl3-5-gpt-oss-20b-a4b-preview", "b": "thinkingcap-qwen3-8-27b"}`. 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