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

> DeepSeek-V3.2 scores 58.6 (C) to Qwen3-Coder-30B-A3B-Instruct's 55.7 (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-coder-30b-a3b-instruct
- Markdown: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-coder-30b-a3b-instruct.md (~6,700 tokens)
- Slim: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-coder-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-coder-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 scores 58.6 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 2 of 5 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on reach, risk and agent interface. 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-Coder-30B-A3B-Instruct: grade C, 55.7/100, rank #78 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-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 33

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

### Qwen3-Coder-30B-A3B-Instruct (C)

Good for: Coding agents and local coding tools that want a fast, capable open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.

Ahead on:
- Reach, 90 against 60
- Risk, 55 against 50
- Agent interface, 75 against 60

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

Watch for: No safety results, prompt-injection guidance or security contact, though it's meant to run commands


## Score by category

| Category | Weight | DeepSeek-V3.2 | Qwen3-Coder-30B-A3B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 69 | 33 | DeepSeek-V3.2 +36 |
| Reach | 25% (25 this run) | 60 | 90 | Qwen3-Coder-30B-A3B-Instruct +30 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3-Coder-30B-A3B-Instruct +5 |
| Agent interface | 10% (10 this run) | 60 | 75 | Qwen3-Coder-30B-A3B-Instruct +15 |
| Documentation and upkeep | 10% (10 this run) | 34 | 31 | DeepSeek-V3.2 +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **58.6 · C** | **55.7 · C** | |

## Facts side by side

| Fact | DeepSeek-V3.2 | Qwen3-Coder-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 | 2025-12-03 |
| 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-Coder-30B-A3B-Instruct.** A fast coding cousin, with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is that it runs commands for agents with no safety results or guidance, and Alibaba doesn't answer users.

## 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-Coder-30B-A3B-Instruct

1. Quickest start is `ollama run qwen3-coder:30b`, 19 GB
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, 61 GB of weights fits the GPU share
3. Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON
4. Pass tool-call arguments in the history as objects, the template iterates them as a mapping
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens
6. For code completion use the fill-in-the-middle tokens `<|fim_prefix|>`, `<|fim_suffix|>` and `<|fim_middle|>` from Qwen's README
7. Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection

## Questions

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

DeepSeek-V3.2 scores 58.6 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 2 of 5 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on reach, risk and agent interface.


## For agents

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

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