Head to head · Open-weight models you run yourself · October 2026 research run
gpt-oss-120b vs Nemotron 3 Nano 4B
gpt-oss-120b scores 70.2 (BB) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 5 scored categories. Nemotron 3 Nano 4B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
gpt-oss-120bOpenAI · Open-weight model
BB70.2/100#6 of 100 open-weight models
Nemotron 3 Nano 4BNVIDIA · Open-weight model
C56.8/100#73 of 100 open-weight models
Which one, for what
gpt-oss-120b BB
Best 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 25
- Risk, 95 against 65
- Agent interface, 90 against 70
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
Best for Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English
Ahead on
- Reach, 90 against 75
- Documentation and upkeep, 55 against 45
Watch for
The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository
Score by category
| Category | Weight this run | gpt-oss-120b | Nemotron 3 Nano 4B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 54 | 25 | gpt-oss-120b +29 |
| Reach | 25%25 | 75 | 90 | Nemotron 3 Nano 4B +15 |
| Risk | 20%20 | 95 | 65 | gpt-oss-120b +30 |
| Agent interface | 10%10 | 90 | 70 | gpt-oss-120b +20 |
| Documentation and upkeep | 10%10 | 45 | 55 | Nemotron 3 Nano 4B +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 70.2/100 | C 56.8/100 |
Facts side by side
| Fact | gpt-oss-120b | Nemotron 3 Nano 4B |
|---|---|---|
| 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 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-26 | 2026-03-16 |
| 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 | 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 4B
A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.
Before you call either
gpt-oss-120b
- Start it with
ollama run gpt-oss:120bon the reference machine, the build that fits - 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 - With vLLM use
vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choiceper vLLM's recipe, or parse raw output with openai-harmony - Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise
- Set
Reasoning: lowin the system message for short tasks, medium is the default - Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users
- Expect one tool call per turn and loop, don't wait for parallel calls
Nemotron 3 Nano 4B
- Quickest start is
ollama run nemotron-3-nano:4b, a 2.8 GB build - With llama.cpp use
llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja, NVIDIA's own Q4_K_M file - With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with
--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3 - The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
- Thinking is on by default. Pass
enable_thinking=Falseto the chat template for short tasks - Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says
- It's tuned for English only, and validate JSON yourself, since structured output isn't documented
Questions
Which is better for AI agents, gpt-oss-120b or Nemotron 3 Nano 4B?
gpt-oss-120b scores 70.2 (BB) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 5 scored categories. Nemotron 3 Nano 4B leads on reach and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/gpt-oss-120b-vs-nemotron-3-nano-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gpt-oss-120b.json·/api/v1/tools/nemotron-3-nano-4b.json - From a terminal
anchor compare gpt-oss-120b nemotron-3-nano-4b(the CLI) - Over MCP
compare_tools {"a": "gpt-oss-120b", "b": "nemotron-3-nano-4b"}at/mcp, no key