Nanbeige4.3-33B vs Qwen3 235B A22B (2507)
Nanbeige LLM Lab (BOSS Zhipin) · China | Alibaba · China · Updated June 2026
Quick verdict
Pick Nanbeige4.3-33B for 33b dense open-weight model under apache 2.0 with a 524,288-token context or artificial analysis intelligence index of 20, first of the 8 models in its comparison class. Pick Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench).
Nanbeige4.3-33B (Nanbeige LLM Lab (BOSS Zhipin)) and Qwen3 235B A22B (2507) (Alibaba) are two of the models people most often weigh against each other in 2026. Nanbeige4.3-33B is nanbeige LLM Lab's October 25, 2026 Apache 2.0 open model - 33B parameters and 512K context, aimed at agentic coding on modest hardware. Qwen3 235B A22B (2507) is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
- ▸Context window: Nanbeige4.3-33B holds 2× more — 512K tokens (~786 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: Nanbeige4.3-33B is the newer model by about 15 months (released October 25, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Nanbeige4.3-33B | Qwen3 235B A22B (2507) |
|---|---|---|
| Provider | Nanbeige LLM Lab (BOSS Zhipin) (China) | Alibaba (China) |
| Released | October 25, 2026 | July 21, 2025 |
| Context window | 512K tokens (~786 pages) | 256K (~393 pages) |
| Price (in/out) | Open weight (self-host / free) | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
33B dense open-weight model under Apache 2.0 with a 524,288-token context
Nanbeige4.3-33B
Its 512K tokens window holds about 2× more than Qwen3 235B A22B (2507)'s 256K in a single prompt.
Artificial Analysis Intelligence Index of 20, first of the 8 models in its comparison class
Nanbeige4.3-33B
Qwen3 235B A22B (2507) is comparatively weak here — nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead
Lab-reported Terminal-Bench 2.1 of 80.9 and DeepSWE of 65.5
Nanbeige4.3-33B
Qwen3 235B A22B (2507) is comparatively weak here — coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux) among its strengths; Nanbeige4.3-33B does not.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; Nanbeige4.3-33B does not.
Outstanding structured logic — 95.0 on ZebraLogic
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Nanbeige4.3-33B does not.
Largest single-prompt input
Nanbeige4.3-33B
Its 512K tokens window is about 2× larger than Qwen3 235B A22B (2507)'s 256K, fitting roughly 786 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ Nanbeige4.3-33B
Larger 512K tokens window fits more in one prompt.
Anyone whose priority is 33b dense open-weight model under apache 2.0 with a 524,288-token context
→ Nanbeige4.3-33B
It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)
→ Qwen3 235B A22B (2507)
That is its strongest area.
Nanbeige4.3-33B: where it fits
Nanbeige LLM Lab's October 25, 2026 Apache 2.0 open model - 33B parameters and 512K context, aimed at agentic coding on modest hardware. Released October 25, 2026 by Nanbeige LLM Lab (BOSS Zhipin), it is built for 33B dense open-weight model under Apache 2.0 with a 524,288-token context, artificial Analysis Intelligence Index of 20, first of the 8 models in its comparison class, and lab-reported Terminal-Bench 2.1 of 80.9 and DeepSWE of 65.5.
Its trade-offs are real: coding benchmarks are self-reported, with non-standard settings noted on the model card and no independent reproduction, no public first-party API or per-token price; Artificial Analysis lists no publicly available API, and text-only input. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
Qwen3 235B A22B (2507): where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
Nanbeige4.3-33B and Qwen3 235B A22B (2507) overlap enough that the right pick depends on your specific job. Nanbeige4.3-33B holds the larger context; and each leads in its own area — Nanbeige4.3-33B for 33b dense open-weight model under apache 2.0 with a 524,288-token context, Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Nanbeige4.3-33B and Qwen3 235B A22B (2507) without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is Nanbeige4.3-33B or Qwen3 235B A22B (2507) better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Nanbeige4.3-33B leans toward 33b dense open-weight model under apache 2.0 with a 524,288-token context while Qwen3 235B A22B (2507) leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Nanbeige4.3-33B or Qwen3 235B A22B (2507)?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Nanbeige4.3-33B — 512K tokens vs 256K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Nanbeige4.3-33B and Qwen3 235B A22B (2507) together?
Yes — a multi-model platform like LumiChats gives you Nanbeige4.3-33B, Qwen3 235B A22B (2507) and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Nanbeige4.3-33B or Qwen3 235B A22B (2507)?
Nanbeige4.3-33B — released October 25, 2026, about 15 months after Qwen3 235B A22B (2507).
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.