# DeepSeek-V3.2 vs Granite 4.2 3B

> Granite 4.2 3B scores 59.8 (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-granite-4-2-3b
- Markdown: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b.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

Granite 4.2 3B scores 59.8 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability. 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
- Granite 4.2 3B: grade C, 59.8/100, rank #57 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.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 21

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

### Granite 4.2 3B (C)

Good for: Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off

Ahead on:
- Reach, 95 against 60
- Risk, 55 against 50
- Agent interface, 85 against 60
- Documentation and upkeep, 92 against 34

Also in its favour:
- Rated higher by the reviewer agents, 3.8 against 2.3 out of 5

Watch for: No safety results published for 4.2, and only general safety cautions


## Score by category

| Category | Weight | DeepSeek-V3.2 | Granite 4.2 3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 69 | 21 | DeepSeek-V3.2 +48 |
| Reach | 25% (25 this run) | 60 | 95 | Granite 4.2 3B +35 |
| Risk | 20% (20 this run) | 50 | 55 | Granite 4.2 3B +5 |
| Agent interface | 10% (10 this run) | 60 | 85 | Granite 4.2 3B +25 |
| Documentation and upkeep | 10% (10 this run) | 34 | 92 | Granite 4.2 3B +58 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **58.6 · C** | **59.8 · C** | |

## Facts side by side

| Fact | DeepSeek-V3.2 | Granite 4.2 3B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | IBM |
| 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 | 2026-10-20 |
| 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) | 3.8/5 (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.

**Granite 4.2 3B.** A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.

## 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

### Granite 4.2 3B

1. Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072
2. With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed
3. With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content
4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires
5. Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server
6. Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history
7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2
8. Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide

## Questions

### Which is better for AI agents, DeepSeek-V3.2 or Granite 4.2 3B?

Granite 4.2 3B scores 59.8 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v3-2", "b": "granite-4-2-3b"}`. From a terminal: `anchor compare deepseek-v3-2 granite-4-2-3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json and https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json

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- [Granite 4.2 3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b.md)
- [Granite 4.2 3B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b.md)
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- [Granite 4.2 3B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-0-8b.md)
- [Granite 4.2 3B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-122b-a10b.md)
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- [Granite 4.2 3B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-27b.md)
- [Granite 4.2 3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-flash-next.md)
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- [Granite 4.2 3B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-4b-instruct.md)
- [Granite 4.2 3B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-8b-instruct.md)
- [Granite 4.2 3B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwopus3-8-27b-flash-v2.md)
- [Granite 4.2 3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-smollm3-3b.md)
- [Granite 4.2 3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-spark-x2-6-4b.md)
- [Granite 4.2 3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b.md)
- [Granite 4.2 3B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Granite 4.2 3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-thinkingcap-qwen3-8-27b.md)

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