Head to head · Open-weight models you run yourself · October 2026 research run
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 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.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
DeepSeek-V3.2DeepSeek · Open-weight model
C58.6/100#65 of 100 open-weight models
Qwen3-VL-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model
C58/100#68 of 100 open-weight models
Which one, for what
Best 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
Best 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 this run | DeepSeek-V3.2 | Qwen3-VL-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 69 | 39 | DeepSeek-V3.2 +30 |
| Reach | 25%25 | 60 | 95 | Qwen3-VL-30B-A3B-Instruct +35 |
| Risk | 20%20 | 50 | 55 | Qwen3-VL-30B-A3B-Instruct +5 |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 34 | 36 | Qwen3-VL-30B-A3B-Instruct +2 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 58.6/100 | C 58/100 |
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
- 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
- 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
- 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
- Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content
- Send developer messages only for search-agent prompts, the only use DeepSeek gives that role
- Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens
Qwen3-VL-30B-A3B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:30b-a3b-instruct - In Ollama the plain
qwen3-vl:30btag is the Thinking edition, so name30b-a3b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
- Validate JSON output yourself, since structured output isn't documented
- 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.
Other comparisons with DeepSeek-V3.2 or Qwen3-VL-30B-A3B-Instruct
- Agents-A1-4B vs DeepSeek-V3.2
- Agents-A1-4B vs Qwen3-VL-30B-A3B-Instruct
- Apertus v1.5 8B vs DeepSeek-V3.2
- Apertus v1.5 8B vs Qwen3-VL-30B-A3B-Instruct
- Apodex-1.1-mini vs DeepSeek-V3.2
- Apodex-1.1-mini vs Qwen3-VL-30B-A3B-Instruct
- BigBang-v1 vs DeepSeek-V3.2
- BigBang-v1 vs Qwen3-VL-30B-A3B-Instruct
- Cosmos-Reason2-2B vs DeepSeek-V3.2
- Cosmos-Reason2-2B vs Qwen3-VL-30B-A3B-Instruct
- Darwin-36B-Opus vs DeepSeek-V3.2
- Darwin-36B-Opus vs Qwen3-VL-30B-A3B-Instruct
- DeepSeek-V3.2 vs DeepSeek V4.1 Flash
- DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731
- DeepSeek-V3.2 vs DiffusionGemma 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 12B IT
- DeepSeek-V3.2 vs Gemma 4 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 31B IT
- DeepSeek-V3.2 vs Gemma 4 E2B IT
- DeepSeek-V3.2 vs Gemma 4 E4B IT
- DeepSeek-V3.2 vs GLM-4.5-Air
- DeepSeek-V3.2 vs GLM-4.7-Flash
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs GLM-5.3-Flash
- DeepSeek-V3.2 vs gpt-oss-120b
- DeepSeek-V3.2 vs gpt-oss-20b
- DeepSeek-V3.2 vs Granite 4.2 3B
- DeepSeek-V3.2 vs Hy3
- DeepSeek-V3.2 vs Inkling
- DeepSeek-V3.2 vs Inkling-Small
- DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- DeepSeek-V3.2 vs Kimi K3
- DeepSeek-V3.2 vs Kolibri-1
- DeepSeek-V3.2 vs Laguna M.1
- DeepSeek-V3.2 vs Laguna S 2.1
- DeepSeek-V3.2 vs Laguna XS 2.1
- DeepSeek-V3.2 vs LFM2.5-2.6B
- DeepSeek-V3.2 vs Ling-3.0-tiny
- DeepSeek-V3.2 vs MiMo-V2.6 Distill Qwen 9B
- DeepSeek-V3.2 vs MiniCPM-o 4.5
- DeepSeek-V3.2 vs MiniCPM5-2B
- DeepSeek-V3.2 vs MiniMax-M2.5
- DeepSeek-V3.2 vs MiniMax-M2.7
- DeepSeek-V3.2 vs Muse Glimmer 30B
- DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 4B
- DeepSeek-V3.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- DeepSeek-V3.2 vs Nemotron 3 Super 120B A12B
- DeepSeek-V3.2 vs Nemotron 3 Ultra 550B A55B
- DeepSeek-V3.2 vs occamy-1.0
- DeepSeek-V3.2 vs Olmo 3 7B Think
- DeepSeek-V3.2 vs Ornith-1.5-35B-A3B
- DeepSeek-V3.2 vs Ornith-1.5-397B
- DeepSeek-V3.2 vs Ornith-1.5-9B
- DeepSeek-V3.2 vs Qwen3-30B-A3B-Instruct-2507
- DeepSeek-V3.2 vs Qwen3-4B-Instruct-2507
- DeepSeek-V3.2 vs Qwen3.5-0.8B
- DeepSeek-V3.2 vs Qwen3.5-122B-A10B
- DeepSeek-V3.2 vs Qwen3.5-2B
- DeepSeek-V3.2 vs Qwen3.5-397B-A17B
- DeepSeek-V3.2 vs Qwen3.5-4B
- DeepSeek-V3.2 vs Qwen3.5-9B
- DeepSeek-V3.2 vs Qwen3.6-27B
- DeepSeek-V3.2 vs Qwen3.6-35B-A3B
- DeepSeek-V3.2 vs Qwen3.8-27B
- DeepSeek-V3.2 vs Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs Qwen3-Coder-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-Coder-Next
- DeepSeek-V3.2 vs Qwen3-Omni-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-2B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-32B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-4B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-8B-Instruct
- DeepSeek-V3.2 vs Qwopus3.8-27B-Flash-V2
- DeepSeek-V3.2 vs SmolLM3-3B
- DeepSeek-V3.2 vs Spark-X2.6-4B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-27B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs ThinkingCap (Qwen3.8 27B)
- DeepSeek V4.1 Flash vs Qwen3-VL-30B-A3B-Instruct
- DeepSeek-V4-Flash-0731 vs Qwen3-VL-30B-A3B-Instruct
- DiffusionGemma 26B A4B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 12B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 26B A4B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 31B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 E2B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 E4B IT vs Qwen3-VL-30B-A3B-Instruct
- GLM-4.5-Air vs Qwen3-VL-30B-A3B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-30B-A3B-Instruct
- GLM-5.3-Flash vs Qwen3-VL-30B-A3B-Instruct
- GLM-5.3 vs Qwen3-VL-30B-A3B-Instruct
- gpt-oss-120b vs Qwen3-VL-30B-A3B-Instruct
- gpt-oss-20b vs Qwen3-VL-30B-A3B-Instruct
- Granite 4.2 3B vs Qwen3-VL-30B-A3B-Instruct
- Hy3 vs Qwen3-VL-30B-A3B-Instruct
- Inkling-Small vs Qwen3-VL-30B-A3B-Instruct
- Inkling vs Qwen3-VL-30B-A3B-Instruct
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-30B-A3B-Instruct
- Kimi K3 vs Qwen3-VL-30B-A3B-Instruct
- Kolibri-1 vs Qwen3-VL-30B-A3B-Instruct
- Laguna M.1 vs Qwen3-VL-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-VL-30B-A3B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-30B-A3B-Instruct
- LFM2.5-2.6B vs Qwen3-VL-30B-A3B-Instruct
- Ling-3.0-tiny vs Qwen3-VL-30B-A3B-Instruct
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-30B-A3B-Instruct
- MiniCPM-o 4.5 vs Qwen3-VL-30B-A3B-Instruct
- MiniCPM5-2B vs Qwen3-VL-30B-A3B-Instruct
- MiniMax-M2.5 vs Qwen3-VL-30B-A3B-Instruct
- MiniMax-M2.7 vs Qwen3-VL-30B-A3B-Instruct
- Muse Glimmer 30B vs Qwen3-VL-30B-A3B-Instruct
- Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-30B-A3B-Instruct
- Nemotron 3 Nano 30B A3B vs Qwen3-VL-30B-A3B-Instruct
- Nemotron 3 Nano 4B vs Qwen3-VL-30B-A3B-Instruct
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-30B-A3B-Instruct
- Nemotron 3 Super 120B A12B vs Qwen3-VL-30B-A3B-Instruct
- Nemotron 3 Ultra 550B A55B vs Qwen3-VL-30B-A3B-Instruct
- occamy-1.0 vs Qwen3-VL-30B-A3B-Instruct
- Olmo 3 7B Think vs Qwen3-VL-30B-A3B-Instruct
- Ornith-1.5-35B-A3B vs Qwen3-VL-30B-A3B-Instruct
- Ornith-1.5-397B vs Qwen3-VL-30B-A3B-Instruct
- Ornith-1.5-9B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-4B-Instruct-2507 vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-0.8B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-122B-A10B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-2B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-4B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-9B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.6-27B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Coder-Next vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-VL-2B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B
- Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/deepseek-v3-2-vs-qwen3-vl-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/deepseek-v3-2.json·/api/v1/tools/qwen3-vl-30b-a3b-instruct.json - From a terminal
anchor compare deepseek-v3-2 qwen3-vl-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "deepseek-v3-2", "b": "qwen3-vl-30b-a3b-instruct"}at/mcp, no key