# gpt-oss-120b vs Nemotron 3 Nano 30B A3B

> gpt-oss-120b scores 70.2 (BB) to Nemotron 3 Nano 30B A3B's 67.1 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b
- Markdown: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b.md (~6,550 tokens)
- Slim: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b.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

gpt-oss-120b scores 70.2 (BB) on agent readiness against Nemotron 3 Nano 30B A3B's 67.1 (B), and leads in 2 of 5 scored categories. Nemotron 3 Nano 30B A3B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.

- gpt-oss-120b: grade BB, 70.2/100, rank #6 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-120b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json
- Nemotron 3 Nano 30B A3B: grade B, 67.1/100, rank #12 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.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

### gpt-oss-120b (BB)

Good for: Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support

Ahead on:
- Capability, 54 against 46
- Risk, 95 against 60

Also in its favour:
- Agent-ready, a grade of BB or better

Watch for: Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision

### Nemotron 3 Nano 30B A3B (B)

Good for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.

Ahead on:
- Reach, 90 against 75
- Documentation and upkeep, 75 against 45

Watch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository


## Score by category

| Category | Weight | gpt-oss-120b | Nemotron 3 Nano 30B A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 54 | 46 | gpt-oss-120b +8 |
| Reach | 25% (25 this run) | 75 | 90 | Nemotron 3 Nano 30B A3B +15 |
| Risk | 20% (20 this run) | 95 | 60 | gpt-oss-120b +35 |
| Agent interface | 10% (10 this run) | 90 | 90 | even |
| Documentation and upkeep | 10% (10 this run) | 45 | 75 | Nemotron 3 Nano 30B A3B +30 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **70.2 · BB** | **67.1 · B** | |

## Facts side by side

| Fact | gpt-oss-120b | Nemotron 3 Nano 30B A3B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenAI | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-26 | 2026-08-24 |
| Terms last updated | 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 | 3.5/5 (4) | 3.3/5 (4) |

## Verdicts

**gpt-oss-120b.** A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.

**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.

## Before you call either

### gpt-oss-120b

1. Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits
2. On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs
3. With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony
4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise
5. Set `Reasoning: low` in the system message for short tasks, medium is the default
6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users
7. Expect one tool call per turn and loop, don't wait for parallel calls

### Nemotron 3 Nano 30B A3B

1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)
2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`
3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151
4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text
5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning
6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks
7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens
8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3
9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution
10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model

## Questions

### Which is better for AI agents, gpt-oss-120b or Nemotron 3 Nano 30B A3B?

gpt-oss-120b scores 70.2 (BB) on agent readiness against Nemotron 3 Nano 30B A3B's 67.1 (B), and leads in 2 of 5 scored categories. Nemotron 3 Nano 30B A3B leads on reach and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gpt-oss-120b", "b": "nemotron-3-nano-30b-a3b"}`. From a terminal: `anchor compare gpt-oss-120b nemotron-3-nano-30b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json

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