# Nanbeige4.2-3B vs Nanbeige4.3-33B > Nanbeige4.2-3B and Nanbeige4.3-33B score within a point of each other for open-weight models you run yourself, 61 and 60.8 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nanbeige-4-3-33b - Markdown: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nanbeige-4-3-33b.md (~9,500 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 Nanbeige4.2-3B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on risk. Both do open-weight models you run yourself. - Nanbeige4.2-3B: grade C, 61/100, rank #78 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json - Nanbeige4.3-33B: grade C, 60.8/100, rank #86 of 160 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 - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Nanbeige4.2-3B (C) Good for: Local agents that need tool calling and a long context on a laptop or a small GPU Ahead on: - Reach, 85 against 80 - Documentation and upkeep, 72 against 56 Watch for: Its own architecture: Transformers loads it only with the repository's code ### Nanbeige4.3-33B (C) Good for: Agents with a 32 GB GPU (INT4) or more, or the 128 GB reference machine at BF16, that want an Apache-tagged 33B model for long coding and tool loops. Ahead on: - Risk, 60 against 55 Watch for: Independent results so far are Artificial Analysis's alone (Intelligence Index v5.0 20.4, Terminal-Bench 4.0 29.8 per cent); the card's other agentic numbers are Nanbeige's own runs ## Why the edge ### Documentation and upkeep: Nanbeige4.2-3B +16 - Nanbeige4.2-3B (72): Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72. - Nanbeige4.3-33B (56): The model card earns 11, with intended use, parameters, context and languages but no limitations, architecture or cut-off. Training data earns 0, as nothing describes it. Last change earns 20 for the quantised builds of 31 October 2026. Responsiveness earns 10, a Nanbeige member answering nine of 30 discussions. Version policy earns 5 for a first release, with a config change made and reverted under bare commit titles. File health earns 10, the files and the card agreeing. ### Reach: Nanbeige4.2-3B +5 - Nanbeige4.2-3B (85): Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85. - Nanbeige4.3-33B (80): Memory earns 20, since Nanbeige's INT4 build is 22.57 GB, about 27 GB with a fifth added, which fits 32 GB. Runtimes earn 20 from released vLLM, SGLang, Transformers and llama.cpp, which all run the Llama architecture, and Ollama and LM Studio have no build. Builds earn 15 for Nanbeige's INT4, INT8 and FP8 builds of 31 October 2026. Terms earn 15 for Apache 2.0 with nothing added, though only the metadata names it. Download earns 10, with no gate. Hosted earns 0, as no provider or Nanbeige API serves it. ### Risk: Nanbeige4.3-33B +5 - Nanbeige4.2-3B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55. - Nanbeige4.3-33B (60): File safety earns 25, safetensors only and no repository code, so the card's trust_remote_code has nothing to run. Provenance earns 15, the lab's own organisation as its arXiv report links it, with a hash for every file. Safety evaluation earns 0 and agent guidance 0, since neither card says anything on either. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for BOSS Zhipin's security response centre, which isn't linked to the model. ## Score by category | Category | Weight | Nanbeige4.2-3B | Nanbeige4.3-33B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 80 | Nanbeige4.2-3B +5 | | Risk | 20% (20 this run) | 55 | 60 | Nanbeige4.3-33B +5 | | Agent interface | 10% (10 this run) | 85 | 85 | even | | Documentation and upkeep | 10% (10 this run) | 72 | 56 | Nanbeige4.2-3B +16 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **60.8 · C** | | ## Facts side by side | Fact | Nanbeige4.2-3B | Nanbeige4.3-33B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Nanbeige LLM Lab (BOSS Zhipin) | Nanbeige LLM Lab (BOSS Zhipin) | | 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) | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-09-11 | 2026-10-31 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | ## How the scores have moved ### Nanbeige4.2-3B No score change since it was listed. ### Nanbeige4.3-33B - -0.2 on 3 November 2026: product, new evidence ## Verdicts **Nanbeige4.2-3B.** A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF. **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. Nanbeige's INT4 build needs about 27 GB. Artificial Analysis's first independent runs give it 20.4 on its Intelligence Index, and there are no safety results, training data notes or licence file. ## Before you call either ### Nanbeige4.2-3B 1. Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige` 2. Pass `tools` in the request. The card recommends `tool_call_format="xml"` 3. Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat 4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card 5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's 6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it ### 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. On a 32 GB GPU use Nanbeige/Nanbeige4.3-33B-INT4 with the same vLLM or SGLang flags; the INT8 and FP8 builds need about 43 GB 3. 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 4. 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 5. 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 6. 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 7. Send one system message at the start only, as the template raises an error for a later one 8. 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 ## Questions ### Which is better for AI agents, Nanbeige4.2-3B or Nanbeige4.3-33B? Nanbeige4.2-3B and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 60.8 (C). Nanbeige4.3-33B leads on risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nanbeige-4-3-33b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nanbeige-4-3-33b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nanbeige-4-2-3b", "b": "nanbeige-4-3-33b"}`. 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