# DeepSeek-V3.2 vs Nemotron 3 Ultra 550B A55B

> Nemotron 3 Ultra 550B A55B scores 68.8 (B) 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-nemotron-3-ultra-550b-a55b
- Markdown: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-ultra-550b-a55b.md (~6,600 tokens)
- Slim: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-ultra-550b-a55b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-ultra-550b-a55b.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

Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on reach. 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
- Nemotron 3 Ultra 550B A55B: grade B, 68.8/100, rank #9 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.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:
- Reach, 60 against 55

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

### Nemotron 3 Ultra 550B A55B (B)

Good for: Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs

Ahead on:
- Risk, 70 against 50
- Agent interface, 90 against 60
- Documentation and upkeep, 65 against 34

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

Watch for: The smallest build is 352 GB and needs at least four B200-class GPUs


## Score by category

| Category | Weight | DeepSeek-V3.2 | Nemotron 3 Ultra 550B A55B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 69 | 73 | Nemotron 3 Ultra 550B A55B +4 |
| Reach | 25% (25 this run) | 60 | 55 | DeepSeek-V3.2 +5 |
| Risk | 20% (20 this run) | 50 | 70 | Nemotron 3 Ultra 550B A55B +20 |
| Agent interface | 10% (10 this run) | 60 | 90 | Nemotron 3 Ultra 550B A55B +30 |
| Documentation and upkeep | 10% (10 this run) | 34 | 65 | Nemotron 3 Ultra 550B A55B +31 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **58.6 · C** | **68.8 · B** | |

## Facts side by side

| Fact | DeepSeek-V3.2 | Nemotron 3 Ultra 550B A55B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-12-01 | 2026-06-04 |
| 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.

**Nemotron 3 Ultra 550B A55B.** NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results.

## 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

### Nemotron 3 Ultra 550B A55B

1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead
2. With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more
3. With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register
4. On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse
5. Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens
6. Use `temperature=1.0` and `top_p=0.95`, as the generation config sets

## Questions

### Which is better for AI agents, DeepSeek-V3.2 or Nemotron 3 Ultra 550B A55B?

Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-ultra-550b-a55b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-ultra-550b-a55b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v3-2", "b": "nemotron-3-ultra-550b-a55b"}`. From a terminal: `anchor compare deepseek-v3-2 nemotron-3-ultra-550b-a55b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json

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