# gpt-oss-20b vs Nemotron 3.5 Lightning 30B A3B

> gpt-oss-20b scores 69.9 (B) to Nemotron 3.5 Lightning 30B A3B's 64.2 (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-20b-vs-nemotron-3-5-lightning-30b-a3b
- Markdown: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-lightning-30b-a3b.md (~6,700 tokens)
- Slim: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-lightning-30b-a3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-lightning-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-20b scores 69.9 (B) on agent readiness against Nemotron 3.5 Lightning 30B A3B's 64.2 (B), and leads in 3 of 5 scored categories. Nemotron 3.5 Lightning 30B A3B leads on documentation and upkeep. Both do open-weight models you run yourself.

- gpt-oss-20b: grade B, 69.9/100, rank #8 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-20b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json
- Nemotron 3.5 Lightning 30B A3B: grade B, 64.2/100, rank #21 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-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-20b (B)

Good for: Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time

Ahead on:
- Capability, 49 against 34
- Reach, 90 against 85
- Risk, 85 against 75

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5

Watch for: No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered

### Nemotron 3.5 Lightning 30B A3B (B)

Good for: Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence

Ahead on:
- Documentation and upkeep, 70 against 42

Watch for: No safety evaluation results published


## Score by category

| Category | Weight | gpt-oss-20b | Nemotron 3.5 Lightning 30B A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 49 | 34 | gpt-oss-20b +15 |
| Reach | 25% (25 this run) | 90 | 85 | gpt-oss-20b +5 |
| Risk | 20% (20 this run) | 85 | 75 | gpt-oss-20b +10 |
| Agent interface | 10% (10 this run) | 90 | 90 | even |
| Documentation and upkeep | 10% (10 this run) | 42 | 70 | Nemotron 3.5 Lightning 30B A3B +28 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **69.9 · B** | **64.2 · B** | |

## Facts side by side

| Fact | gpt-oss-20b | Nemotron 3.5 Lightning 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 | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-26 | 2026-08-11 |
| 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) | 3/5 (4) |

## Verdicts

**gpt-oss-20b.** gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.

**Nemotron 3.5 Lightning 30B A3B.** A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.

## Before you call either

### gpt-oss-20b

1. Start it with `ollama run gpt-oss:20b` (the build that fits the reference machine), or `vllm serve openai/gpt-oss-20b` with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel
2. Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it
3. Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in `<|call|>`, reasoning on the analysis channel
4. Set effort with `Reasoning: high` (or low, medium) in the system message, or `reasoning_effort` in the template; medium is the default
5. Ask for JSON by putting a JSON Schema under `# Response Formats` in the developer message
6. Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users
7. Expect one tool call per turn; run several in sequence rather than in parallel

### Nemotron 3.5 Lightning 30B A3B

1. Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform
2. On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs
3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)
4. Use `temperature=1.0` and `top_p=0.95`, as the card says
5. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks
6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content
7. Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory

## Questions

### Which is better for AI agents, gpt-oss-20b or Nemotron 3.5 Lightning 30B A3B?

gpt-oss-20b scores 69.9 (B) on agent readiness against Nemotron 3.5 Lightning 30B A3B's 64.2 (B), and leads in 3 of 5 scored categories. Nemotron 3.5 Lightning 30B A3B leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-lightning-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-lightning-30b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gpt-oss-20b", "b": "nemotron-3-5-lightning-30b-a3b"}`. From a terminal: `anchor compare gpt-oss-20b nemotron-3-5-lightning-30b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json

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