# InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-2B

> InternVL3.5-GPT-OSS-20B-A4B-Preview and Qwen3.5-2B score within a point of each other for open-weight models you run yourself, 51.2 and 50.5 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-2b
- Markdown: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-2b.md (~6,800 tokens)
- Slim: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-2b.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-qwen3-5-2b.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

InternVL3.5-GPT-OSS-20B-A4B-Preview and Qwen3.5-2B score within a point of each other on agent readiness, 51.2 (D) and 50.5 (D). Qwen3.5-2B leads on reach, agent interface and documentation and upkeep. 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
- Qwen3.5-2B: grade D, 50.5/100, rank #92 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.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

### Qwen3.5-2B (D)

Good for: Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU

Ahead on:
- Reach, 80 against 60
- Agent interface, 60 against 45
- Documentation and upkeep, 37 against 28

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 1.8 out of 5

Watch for: No quantised build from Alibaba, and only one inference provider


## Score by category

| Category | Weight | InternVL3.5-GPT-OSS-20B-A4B-Preview | Qwen3.5-2B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 60 | 80 | Qwen3.5-2B +20 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 45 | 60 | Qwen3.5-2B +15 |
| Documentation and upkeep | 10% (10 this run) | 28 | 37 | Qwen3.5-2B +9 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **51.2 · D** | **50.5 · D** | |

## Facts side by side

| Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | Qwen3.5-2B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenGVLab (Shanghai AI Laboratory) | Alibaba (Qwen team) |
| 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) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-09-08 | 2026-03-02 |
| 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 | 1.8/5 (4) | 3/5 (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.

**Qwen3.5-2B.** A small vision-language cousin for prototypes, fine-tuning and light local steps such as tool calls, tagging or reading an image, running in about 2 GB at 4 bits in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no safety results or answers from Alibaba since March.

## 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 <think> 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

### Qwen3.5-2B

1. Smallest start is `ollama run qwen3.5:2b-q4_K_M` (1.9 GB); plain `qwen3.5:2b` pulls the 2.7 GB Q8_0 build
2. Full precision `vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. Thinking is off by default; if you turn it on with `"chat_template_kwargs": {"enable_thinking": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out
4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking
5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests
6. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or Qwen3.5-2B?

InternVL3.5-GPT-OSS-20B-A4B-Preview and Qwen3.5-2B score within a point of each other on agent readiness, 51.2 (D) and 50.5 (D). Qwen3.5-2B leads on reach, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-2b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "internvl3-5-gpt-oss-20b-a4b-preview", "b": "qwen3-5-2b"}`. From a terminal: `anchor compare internvl3-5-gpt-oss-20b-a4b-preview qwen3-5-2b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.json

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- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-8b-instruct.md)
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- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs SmolLM3-3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-smollm3-3b.md)
- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-spark-x2-6-4b.md)
- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-27b.md)
- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-flash-next.md)
- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.md)
- [Kimi K3 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-2b.md)
- [Kolibri-1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-2b.md)
- [Laguna M.1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-2b.md)
- [Laguna S 2.1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-2b.md)
- [Laguna XS 2.1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-2b.md)
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- [MiniMax-M2.5 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-2b.md)
- [MiniMax-M2.7 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-2b.md)
- [Muse Glimmer 30B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-2b.md)
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- [Nemotron 3 Nano 4B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-2b.md)
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- [Nemotron 3 Super 120B A12B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-2b.md)
- [Nemotron 3 Ultra 550B A55B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-2b.md)
- [occamy-1.0 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b.md)
- [Olmo 3 7B Think vs Qwen3.5-2B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-2b.md)
- [Ornith-1.5-35B-A3B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-2b.md)
- [Ornith-1.5-397B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b.md)
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- [Qwen3-4B-Instruct-2507 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-2b.md)
- [Qwen3.5-0.8B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-2b.md)
- [Qwen3.5-122B-A10B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-2b.md)
- [Qwen3.5-2B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-397b-a17b.md)
- [Qwen3.5-2B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-4b.md)
- [Qwen3.5-2B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b.md)
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- [Qwen3.5-2B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-flash-next.md)
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- [Qwen3.5-2B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct.md)
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- [Qwen3.5-2B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b.md)

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