# Nemotron 3 Ultra 550B A55B vs Olmo 3 7B Think

> Nemotron 3 Ultra 550B A55B scores 68.8 (B) to Olmo 3 7B Think's 52.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think.md (~6,650 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think.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: 2026-10-22

Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 5 scored categories. Olmo 3 7B Think leads on reach and risk. Both do open-weight models you run yourself.

- Nemotron 3 Ultra 550B A55B: grade B, 68.8/100, rank #9 of 102 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
- Olmo 3 7B Think: grade D, 52.3/100, rank #86 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/olmo-3-7b-think.md · JSON https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.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

### 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:
- Capability, 73 against 28
- Agent interface, 90 against 35
- Documentation and upkeep, 65 against 52

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

### Olmo 3 7B Think (D)

Good for: Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand

Ahead on:
- Reach, 75 against 55
- Risk, 75 against 70

Watch for: Not trained for tool calling, and the tool format lives only in the template


## Score by category

| Category | Weight | Nemotron 3 Ultra 550B A55B | Olmo 3 7B Think | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 73 | 28 | Nemotron 3 Ultra 550B A55B +45 |
| Reach | 25% (25 this run) | 55 | 75 | Olmo 3 7B Think +20 |
| Risk | 20% (20 this run) | 70 | 75 | Olmo 3 7B Think +5 |
| Agent interface | 10% (10 this run) | 90 | 35 | Nemotron 3 Ultra 550B A55B +55 |
| Documentation and upkeep | 10% (10 this run) | 65 | 52 | Nemotron 3 Ultra 550B A55B +13 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **68.8 · B** | **52.3 · D** | |

## Facts side by side

| Fact | Nemotron 3 Ultra 550B A55B | Olmo 3 7B Think |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Ai2 (Allen Institute for AI) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | OpenMDW-1.1 | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-06-04 | 2026-06-25 |
| Terms last updated | no document linked |  |
| Privacy policy last updated | 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 | 2.8/5 (4) | 2.8/5 (4) |

## Verdicts

**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.

**Olmo 3 7B Think.** A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.

## Before you call either

### 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

### Olmo 3 7B Think

1. Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB
2. With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend
3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long
4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags
5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available
6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling
7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens
8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research

## Questions

### Which is better for AI agents, Nemotron 3 Ultra 550B A55B or Olmo 3 7B Think?

Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 5 scored categories. Olmo 3 7B Think leads on reach and risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-ultra-550b-a55b", "b": "olmo-3-7b-think"}`. From a terminal: `anchor compare nemotron-3-ultra-550b-a55b olmo-3-7b-think`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json and https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json

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