# DeepSeek-V3.2 vs Qwen3-VL-2B-Instruct

> Qwen3-VL-2B-Instruct scores 61 (C) to DeepSeek-V3.2's 58.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-2b-instruct
- Markdown: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-2b-instruct.md (~6,250 tokens)
- Slim: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-2b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-2b-instruct.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

Qwen3-VL-2B-Instruct scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.

- DeepSeek-V3.2: grade C, 58.6/100, rank #65 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v3-2.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json
- Qwen3-VL-2B-Instruct: grade C, 61/100, rank #45 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-2b-instruct.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

### DeepSeek-V3.2 (C)

Good for: Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider

Watch for: 689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB

### Qwen3-VL-2B-Instruct (C)

Good for: An agent on a laptop or small edge device that needs a local model to read images, documents and video in about 2 GB

Ahead on:
- Reach, 95 against 60
- Risk, 55 against 50

Also in its favour:
- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5

Watch for: No safety evaluation, agent-use guidance or security contact


## Score by category

| Category | Weight | DeepSeek-V3.2 | Qwen3-VL-2B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 60 | 95 | Qwen3-VL-2B-Instruct +35 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3-VL-2B-Instruct +5 |
| Agent interface | 10% (10 this run) | 60 | 60 | even |
| Documentation and upkeep | 10% (10 this run) | 34 | 31 | DeepSeek-V3.2 +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **58.6 · C** | **61 · C** | |

## Facts side by side

| Fact | DeepSeek-V3.2 | Qwen3-VL-2B-Instruct |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-12-01 | 2025-11-01 |
| 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 | 2.3/5 (4) | 2.8/5 (4) |

## Verdicts

**DeepSeek-V3.2.** DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.

**Qwen3-VL-2B-Instruct.** The smallest Qwen3-VL cousin, for reading screenshots, documents, charts and video on a laptop or edge box, running in about 2 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. Qwen hasn't answered repository questions or published safety work, so keep a human or a check in the loop before it drives a GUI.

## Before you call either

### DeepSeek-V3.2

1. Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3
2. It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead
3. Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only
4. Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content
5. Send developer messages only for search-agent prompts, the only use DeepSeek gives that role
6. Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens

### Qwen3-VL-2B-Instruct

1. Start it with `llama-server -hf Qwen/Qwen3-VL-2B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:2b-instruct`
2. In Ollama the plain `qwen3-vl:2b` tag is the Thinking edition, so name `2b-instruct`
3. With the GGUF, load the mmproj vision file too, or images won't be read
4. Parse tool calls as JSON inside `<tool_call>` tags, or use SGLang's `--tool-call-parser qwen`
5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text
6. Validate JSON output yourself, since structured output isn't documented
7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later

## Questions

### Which is better for AI agents, DeepSeek-V3.2 or Qwen3-VL-2B-Instruct?

Qwen3-VL-2B-Instruct scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 2 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-2b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-2b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v3-2", "b": "qwen3-vl-2b-instruct"}`. From a terminal: `anchor compare deepseek-v3-2 qwen3-vl-2b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-2b-instruct.json

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- [Inkling vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-2b-instruct.md)
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- [Laguna S 2.1 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-2b-instruct.md)
- [Laguna XS 2.1 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-2b-instruct.md)
- [LFM2.5-2.6B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct.md)
- [Ling-3.0-tiny vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-2b-instruct.md)
- [MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-2b-instruct.md)
- [MiniCPM-o 4.5 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct.md)
- [MiniCPM5-2B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-2b-instruct.md)
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- [MiniMax-M2.7 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-2b-instruct.md)
- [Muse Glimmer 30B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-2b-instruct.md)
- [Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-2b-instruct.md)
- [Nemotron 3 Nano 30B A3B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-2b-instruct.md)
- [Nemotron 3 Nano 4B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-2b-instruct.md)
- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-vl-2b-instruct.md)
- [Nemotron 3 Super 120B A12B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-2b-instruct.md)
- [Nemotron 3 Ultra 550B A55B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-2b-instruct.md)
- [occamy-1.0 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct.md)
- [Olmo 3 7B Think vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-2b-instruct.md)
- [Ornith-1.5-35B-A3B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-2b-instruct.md)
- [Ornith-1.5-397B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct.md)
- [Ornith-1.5-9B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-2b-instruct.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-2b-instruct.md)
- [Qwen3-4B-Instruct-2507 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-2b-instruct.md)
- [Qwen3.5-0.8B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-2b-instruct.md)
- [Qwen3.5-122B-A10B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-2b-instruct.md)
- [Qwen3.5-2B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-2b-instruct.md)
- [Qwen3.5-397B-A17B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-2b-instruct.md)
- [Qwen3.5-4B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-2b-instruct.md)
- [Qwen3.5-9B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-2b-instruct.md)
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- [Qwen3.8-Flash-Next vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-2b-instruct.md)
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- [Qwen3-Coder-Next vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-2b-instruct.md)
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- [Qwen3-VL-2B-Instruct vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-32b-instruct.md)
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- [Qwen3-VL-2B-Instruct vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-8b-instruct.md)
- [Qwen3-VL-2B-Instruct vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3-VL-2B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-2B-Instruct vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-spark-x2-6-4b.md)
- [Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-flash-next.md)
- [Qwen3-VL-2B-Instruct vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-thinkingcap-qwen3-8-27b.md)

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