Published:2026-10-26•Last updated:2026-10-26•yulius

What Is Nanbeige4.3-33B? BOSS Zhipin's 33B Open Agent Model, Explained

Quick Answer

BOSS Zhipin's 33B open agent model: 512K context, Apache-2.0, self-reported best-in-class.

On October 24, 2026, Nanbeige — the LLM lab of Chinese recruitment platform BOSS Zhipin — released Nanbeige4.3-33B on Hugging Face: 33.1 billion parameters, dense architecture, 512K context, Apache-2.0 open source, positioned for agentic coding and office-style cowork tasks. In the lab's own 20-benchmark table it leads 17 rows against Qwen3.8-27B, Muse-Glimmer-30B and Gemma4-31B, including 80.9 on Terminal-Bench 2.1 and 65.5 on DeepSWE. Two caveats up front: every score is self-reported with no independent reproduction yet, and the lab itself calls this 33B model a teacher model — the product most people will actually use is the distilled Nanbeige4.3-3B it plans to release later. This article verifies each official number, then tells you who should care now, what deployment really costs, and whether chasing it today is a good idea. Disclaimer: this site is not affiliated with Nanbeige or BOSS Zhipin. Facts were verified on 2026-10-26 and may change; the official model page is authoritative. Review scores are labeled preliminary and are based on public sources, not our own testing.

1. What it is: what 33B means, and who Nanbeige is

Start with the name. Nanbeige is the LLM lab of Kanzhun, the parent company of BOSS Zhipin, China's online recruitment platform; 4.3 is the series codename and 33B is the parameter count. The official Hugging Face API metadata is precise: 33,118,977,024 total parameters in BF16, about 66GB of weight files, LlamaForCausalLM architecture, English and Chinese, Apache-2.0 license, text-generation pipeline tag, created 2026-10-24T02:49:18Z. The key qualifier on 33B is dense, not MoE. All 33.1 billion parameters participate in every inference step, unlike mixture-of-experts models that activate only a subset of experts. That directly sets the deployment cost: BF16 weights alone need about 66GB of VRAM, plus KV cache for a 512K context, which is why the official SGLang and vLLM recipes both use 4-way tensor parallelism — a multi-GPU server setup, not a single-card toy. Nanbeige may be unfamiliar outside China, but its default system prompt states its identity plainly: a professional large language model independently developed and trained by BOSS Zhipin. The contact address is nanbeige@kanzhun.com. In other words, this is not a debut from a new startup but the lab of a listed recruitment company — and its cowork positioning is not bandwagoning: hiring, resumes and cross-team collaboration are BOSS Zhipin's home turf. The benchmark suite includes JobBench (agent tasks across 35 occupations), Automation Bench and GDPval, which makes the intent clear: serve agent scenarios from its own business first, then talk about general use. The series also includes Nanbeige4.3-33B-Base for custom post-training, and an announced distillation path: build the 33B teacher first, then distill agentic skills into smaller models, starting with Nanbeige4.3-3B. Remember that order — it drives the recommendation at the end of this article.

2. Checking the numbers: what 17/20 is worth, and three caveats

The official table has 20 rows: 8 agentic coding, 9 agentic cowork, 3 reasoning. Counting row by row, Nanbeige4.3-33B leads 17 and loses 3: SWE-Bench Pro (60.3 vs Qwen3.8-27B's 61.7), GDPval (84.8 vs 86.4, again to Qwen) and GPQA Diamond (89.5 vs Gemma4-31B's 91.3). The 17/20 figure here is counted from the official table, not copied from a headline — at least one secondary report wrote 18/20, which does not match the table. The two biggest leads are worth recomputing: Terminal-Bench 2.1, 80.9 vs Qwen's 73.0, a 7.9-point lead; DeepSWE, 65.5 vs 42.5, a 23.0-point lead. Note that on DeepSWE the other two rivals score only 4.4 and 2.7 — that says DeepSWE is brutal for models not specifically tuned for agents, so the gap reflects differences in agent-training investment more than general intelligence. Three caveats must be stated: 1) Everything is self-reported, with zero independent reproduction. The table's footnote — all models evaluated at highest thinking effort with the same hyperparameters and harness — is good practice, but the evaluator is still the lab itself. As of 2026-10-26, no independent party has reproduced the table. The Terminal-Bench 4.0 row is the most telling: Nanbeige leads at 23.8, but the absolute number is low and all four models essentially fail (6.3, 1.6, 0.0). The new benchmark is hard for every ~30B model, so leading it does not mean usable. 2) Some benchmarks are graded by another model. SWE-Atlas-QnA answers are scored by GPT-6 Sol as judge against 1,304 task-specific rubrics, which adds a layer of judge-model bias and uncertainty. Similarly, the YC-bench figure of $1,025,437 is simulated business revenue inside the benchmark, not real money — do not read it as earnings. 3) Highest thinking effort is not your daily usage. The table was run at maximum thinking effort, and the official inference settings confirm the cost: temperature 1.0 and up to 65,536 new tokens for agent tasks, temperature 0.6 and up to 262,144 new tokens for reasoning tasks. Long thinking means long latency and large token bills; running it with default settings on your own server will feel different from the scores. That is true of every thinking model, not just Nanbeige's. In short: the direction of the scores is credible — real agent-tuning work went into this model — but treating self-reported first place as verified first place is premature. Waiting for independent reproduction costs nothing.

3. The real story is not the 33B model, but the 3B it will distill into

The official introduction states the strategy openly: build a high-capacity teacher agent first, then distill advanced agentic capabilities into smaller models. The 33B model is the factory, not the product. Community reaction points the same way: discussion on r/LocalLLaMA focuses less on a new model beating older ones and more on the prospect of it being distilled into 3B models, possibly other sizes too — what people actually want to run is the distilled student. The arithmetic explains why. BF16 weights of about 66GB with an official 4-way tensor-parallel recipe mean a multi-GPU server from the start; even squeezed to about 20GB with 4-bit quantization for a single 24GB card, a 512K context's KV cache will still eat VRAM fast. The community has already moved: GGUF quantizations appeared on release day (Q8_0, Q6_K, Q5_K_M, Q4_K_M, plus AD variants built with a custom importance matrix), proving the demand for consumer-hardware builds. But community quants are community products — verify the precision loss yourself. Flip it around: if your goal is to run a working agent on your own machine, the 33B teacher is over-provisioned. When the distilled Nanbeige4.3-3B arrives at roughly a tenth of the parameters, that is the sweet spot for most people. The 33B model's value today is mostly for teams with servers and evaluation capacity, and as a technical reference for everyone else.

4. How to use it: deployment cost and access (guidance, not a tutorial)

Access first. The official model page lists no hosted API endpoint or online trial — only self-hosted recipes: Transformers, SGLang and vLLM. The Hugging Face endpoints_compatible tag means the model can be deployed to an inference endpoint, not that the lab offers a free API; deployment and billing would be yours. As of 2026-10-26, there is no official free API, per the official page. Deployment cost comes in three tiers: 1) Multi-GPU server (the official recipe): about 66GB of BF16 weights, SGLang with tp-size 4 and vLLM with tensor-parallel-size 4, qwen3 reasoning parser and qwen3_coder tool-call parser. Suits teams that already have GPU servers and want agent coding or cowork workloads. Note the official inference settings: agent tasks at temperature 1.0 and top_p 0.95; reasoning and chat at temperature 0.6, top_p 0.95, up to 262,144 new tokens — budget latency and cost for long-thinking tasks accordingly. 2) A single 24GB card (community quantization path): about 20GB at 4-bit, with community GGUF builds available since release day. But 512K context is a theoretical ceiling; usable context depends on how much VRAM the KV cache leaves. Plan for 32K–128K first and widen only after measuring. 3) Just want to try it without deploying: wait for two things — independent benchmark reproduction, and the distilled Nanbeige4.3-3B. Buying hardware for the 33B model now likely means buying at the peak. Two default behaviors are worth knowing: thinking is on by default and must be explicitly disabled with enable_thinking=False; for multi-turn tool use and office tasks the lab recommends preserve_thinking=True (keep the previous turn's reasoning), while for general chat it recommends False. These switches directly affect token consumption and quality — do not use one configuration for every scenario.

5. Verdict: who should chase it now, and who should wait

Chase it now if you have GPU servers, run agent coding or office cowork workloads, can run your own evaluations to check the official scores, and accept an Apache-2.0 open model whose scores are self-reported. For that group, the 33B model is a candidate worth testing, especially on Terminal-Bench 2.1 and DeepSWE-style agent coding tasks. Wait if you have only consumer-grade GPUs, want a free API to call directly, or expect 512K context to work out of the box. For that group, the right move is to bookmark the model page and wait for independent reproduction and the 3B distilled version — any deployment guide written before those two milestones may go stale fast. Review scores (preliminary, based on public sources, not our own testing): event credibility high (official model page plus API metadata plus multiple media reports cross-check; release date, parameters and license are all verifiable); performance-lead credibility medium (self-reported scores point in a plausible direction, but there is no independent reproduction and three benchmarks go to rivals); value for individual users currently low (high deployment bar, no official free API, and the genuinely accessible 3B version is not out yet).

6. Sources and disclaimer

Sources (verified 2026-10-26): 1) https://huggingface.co/Nanbeige/Nanbeige4.3-33B — official model page: teacher-model positioning, 20-benchmark table, 512K context, thinking on by default, deployment recipes and inference settings, contact address; 2) https://huggingface.co/api/models/Nanbeige/Nanbeige4.3-33B — official API metadata: created 2026-10-24T02:49:18Z, 33,118,977,024 parameters, about 66GB weights, Llama architecture, Apache-2.0, English and Chinese, base model Nanbeige4.3-33B-Base; 3) https://www.marktechpost.com/2026/10/25/nanbeige-releases-nanbeige4-3-33b-open-agentic-coding-model/ — dense architecture, about 66GB BF16, 17/20 lead, Terminal-Bench 2.1 80.9 vs 73.0, SWE-Bench Pro 60.3 vs 61.7; 4) https://aiweekly.co/alerts/nanbeige-opens-33b-model-under-apache-20-with-512k-context — 512K context, thinking on by default, DeepSWE 65.5 vs 42.5, Scicode 52.6 vs 46.7; 5) https://huggingface.co/AtomicChat/Nanbeige4.3-33B-GGUF — community GGUF quantizations (release day, Q8_0/Q6_K/Q5_K_M/Q4_K_M and AD variants); 6) https://kenashe.ai/blog/2026-10-25-nanbeige4-3-33b-is-really-a-small-model-bet/ — teacher-model strategy and community interest in the distilled 3B version. Disclaimer: this site is not affiliated with Nanbeige, BOSS Zhipin or Kanzhun. This article is news reporting and analysis, not purchase, deployment or investment advice. Benchmark scores are lab-reported with no independent reproduction yet. Parameters, dates and license are snapshots from the verification date and may change; the Hugging Face official page is authoritative. Deployment cost figures are estimates from public weight sizes; actual usage must be measured.

Self-reported benchmarks: Nanbeige4.3-33B vs ~30B open models (highest thinking effort, same harness)

Benchmark	Nanbeige4.3-33B	Qwen3.8-27B	Muse-Glimmer-30B	Gemma4-31B
Terminal-Bench 2.1 (agentic coding)	80.9	73.0	61.8	47.2
DeepSWE (agentic coding)	65.5	42.5	4.4	2.7
Terminal-Bench 4.0 (agentic coding)	23.8	6.3	1.6	0.0
SWE-Bench Pro	60.3	61.7	36.9	51.2
GPQA Diamond (reasoning)	89.5	89.2	83.5	91.3
AIME 2026 (reasoning)	95.2	94.5	94.7	89.2
JobBench (cowork)	44.8	34.5	16.6	9.0
Automation Bench (cowork)	54.2	38.3	15.2	9.7

Terminal-Bench 2.1: agentic coding scores (self-reported)

★ Nanbeige4.3-33B80.9

Qwen3.8-27B73.0

Muse-Glimmer-30B61.8

Gemma4-31B47.2

数据来源：Source: Nanbeige official model card benchmark table, verified 2026-10-26. All scores are lab-reported; no independent reproduction yet.

Frequently Asked Questions

Whose model is Nanbeige4.3-33B?

It comes from Nanbeige, the LLM lab of BOSS Zhipin, released on Hugging Face on October 24, 2026, with contact address nanbeige@kanzhun.com. Its default system prompt describes it as a professional large language model independently developed and trained by BOSS Zhipin.

How big is 33B, and can my GPU run it?

33.1 billion parameters, dense architecture, about 66GB of BF16 weights, with the official recipe using 4-way tensor parallelism — a multi-GPU server starting point. At 4-bit quantization it is about 20GB and fits a single 24GB card, but 512K context is a theoretical ceiling limited by KV cache; plan for 32K–128K first.

Is it really better than Qwen3.8-27B?

On the lab's own 20 benchmarks it leads 17, with clear margins on Terminal-Bench 2.1 (80.9 vs 73.0) and DeepSWE (65.5 vs 42.5); it also loses 3, including SWE-Bench Pro and GPQA Diamond. The key point: all scores are self-reported, with no independent reproduction as of 2026-10-26 — treat the direction as credible and the margins as unverified.

Is there a free API to call it directly?

No. The official model page only lists self-hosted recipes (Transformers, SGLang, vLLM) and no hosted API or online trial. The endpoints_compatible tag only means it can be deployed to an inference endpoint, with deployment and cost on you.

What should an ordinary user do now?

Bookmark the model page and wait for two milestones: independent benchmark reproduction (does 17/20 hold up?) and the announced Nanbeige4.3-3B distilled version — at roughly a tenth of the parameters, that is the version most people can actually run. Buying hardware for the 33B model now likely means buying at the peak.