# Nanbeige4.3-33B vs Nemotron 3.5 Lightning 30B A3B > Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) to Nanbeige4.3-33B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-lightning-30b-a3b - Markdown: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-lightning-30b-a3b.md (~7,300 tokens) - Slim: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-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/nanbeige-4-3-33b-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: 2026-10-25 Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in every scored category. Both do open-weight models you run yourself. - Nanbeige4.3-33B: grade C, 61/100, rank #56 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json - Nemotron 3.5 Lightning 30B A3B: grade B, 64.2/100, rank #22 of 111 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 3341 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Nanbeige4.3-33B (C) Good for: Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results. Watch for: No independent benchmark result yet; every agentic number on the card is Nanbeige's own run ### 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: - Reach, 85 against 55 - Risk, 75 against 60 - Agent interface, 90 against 85 - Documentation and upkeep, 70 against 56 Watch for: No safety evaluation results published ## Score by category | Category | Weight | Nanbeige4.3-33B | Nemotron 3.5 Lightning 30B A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 85 | Nemotron 3.5 Lightning 30B A3B +30 | | Risk | 20% (20 this run) | 60 | 75 | Nemotron 3.5 Lightning 30B A3B +15 | | Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3.5 Lightning 30B A3B +5 | | Documentation and upkeep | 10% (10 this run) | 56 | 70 | Nemotron 3.5 Lightning 30B A3B +14 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **64.2 · B** | | ## Facts side by side | Fact | Nanbeige4.3-33B | Nemotron 3.5 Lightning 30B A3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Nanbeige LLM Lab (BOSS Zhipin) | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | OpenMDW-1.1 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-24 | 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 | none | 3/5 (4) | ## Verdicts **Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file. **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 ### Nanbeige4.3-33B 1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers 2. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes 3. Pass `chat_template_kwargs` `{"preserve_thinking": true}` for multi-turn tool use and coding, and `{"enable_thinking": false}` to skip reasoning, as the card recommends 4. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card 5. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed 6. Send one system message at the start only, as the template raises an error for a later one 7. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed ### 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, Nanbeige4.3-33B or Nemotron 3.5 Lightning 30B A3B? Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-lightning-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-lightning-30b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nanbeige-4-3-33b", "b": "nemotron-3-5-lightning-30b-a3b"}`. 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How: https://www.anchorterminal.com/agents/#mcp