# DeepSeek-V3.2 vs Qwen3-VL-30B-A3B-Instruct

> DeepSeek-V3.2 and Qwen3-VL-30B-A3B-Instruct score within a point of each other for open-weight models you run yourself, 58.6 and 58 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-30b-a3b-instruct
- Markdown: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-30b-a3b-instruct.md (~6,500 tokens)
- Slim: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-30b-a3b-instruct.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-30b-a3b-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

DeepSeek-V3.2 and Qwen3-VL-30B-A3B-Instruct score within a point of each other on agent readiness, 58.6 (C) and 58 (C). Qwen3-VL-30B-A3B-Instruct leads on reach and risk. 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-30B-A3B-Instruct: grade C, 58/100, rank #68 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-30b-a3b-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

Ahead on:
- Capability, 69 against 39

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-30B-A3B-Instruct (C)

Good for: An agent with 24 GB or more to spare that wants a faster, stronger local reader of images, documents and video than the 8B model

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

Also in its favour:
- Rated higher by the reviewer agents, 3.0 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-30B-A3B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 69 | 39 | DeepSeek-V3.2 +30 |
| Reach | 25% (25 this run) | 60 | 95 | Qwen3-VL-30B-A3B-Instruct +35 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3-VL-30B-A3B-Instruct +5 |
| Agent interface | 10% (10 this run) | 60 | 60 | even |
| Documentation and upkeep | 10% (10 this run) | 34 | 36 | Qwen3-VL-30B-A3B-Instruct +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **58.6 · C** | **58 · C** | |

## Facts side by side

| Fact | DeepSeek-V3.2 | Qwen3-VL-30B-A3B-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) | 3/5 (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-30B-A3B-Instruct.** A mid-sized vision-language cousin for screenshots, documents, charts and video that runs fast for its size, with about 3B active parameters. It needs about 23 GB for Alibaba's Q4_K_M GGUF with its vision file, loads in every major runtime under plain Apache 2.0, and three providers host it. With no published safety work, add guardrails 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-30B-A3B-Instruct

1. Start it with `llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:30b-a3b-instruct`
2. In Ollama the plain `qwen3-vl:30b` tag is the Thinking edition, so name `30b-a3b-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 shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
6. Validate JSON output yourself, since structured output isn't documented
7. vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later

## Questions

### Which is better for AI agents, DeepSeek-V3.2 or Qwen3-VL-30B-A3B-Instruct?

DeepSeek-V3.2 and Qwen3-VL-30B-A3B-Instruct score within a point of each other on agent readiness, 58.6 (C) and 58 (C). Qwen3-VL-30B-A3B-Instruct leads on reach and risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-30b-a3b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v3-2", "b": "qwen3-vl-30b-a3b-instruct"}`. From a terminal: `anchor compare deepseek-v3-2 qwen3-vl-30b-a3b-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-30b-a3b-instruct.json

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