GPT-5.2 vs Nanbeige4.3-33B
OpenAI · US | Nanbeige LLM Lab (BOSS Zhipin) · China · Updated June 2026
Quick verdict
Pick GPT-5.2 for strong all-round reasoning or reliable structured output. 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. Choose Nanbeige4.3-33B if you need self-hosting or data privacy; GPT-5.2 if you want a managed API.
GPT-5.2 (OpenAI, US) and Nanbeige4.3-33B (Nanbeige LLM Lab (BOSS Zhipin), China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GPT-5.2 is a capable GPT-5-generation all-rounder, now succeeded by GPT-5.5. 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. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Cost model: Nanbeige4.3-33B ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-5.2 is API-metered at $1.75/$14 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: Nanbeige4.3-33B holds 1.3× more — 512K tokens (~786 pages) vs 400K (~600 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 11 months (released October 25, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | GPT-5.2 | Nanbeige4.3-33B |
|---|---|---|
| Provider | OpenAI (US) | Nanbeige LLM Lab (BOSS Zhipin) (China) |
| Released | December 11, 2025 | October 25, 2026 |
| Context window | 400K (~600 pages) | 512K tokens (~786 pages) |
| Price (in/out) | $1.75/$14 per 1M tokens | Open weight (self-host / free) |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, image, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Strong all-round reasoning
GPT-5.2
GPT-5.2 lists strong all-round reasoning among its strengths; Nanbeige4.3-33B does not.
Reliable structured output
GPT-5.2
GPT-5.2 lists reliable structured output among its strengths; Nanbeige4.3-33B does not.
Broad ecosystem and tooling
GPT-5.2
GPT-5.2 lists broad ecosystem and tooling among its strengths; Nanbeige4.3-33B does not.
33B dense open-weight model under Apache 2.0 with a 524,288-token context
Nanbeige4.3-33B
Its 512K tokens window holds about 1.3× more than GPT-5.2's 400K in a single prompt.
Artificial Analysis Intelligence Index of 20, first of the 8 models in its comparison class
Nanbeige4.3-33B
Nanbeige LLM Lab's October 25, 2026 Apache 2.0 open model - 33B parameters and 512K context, aimed at agentic coding on modest hardware — and it carries the larger 512K tokens context.
Lab-reported Terminal-Bench 2.1 of 80.9 and DeepSWE of 65.5
Nanbeige4.3-33B
Nanbeige LLM Lab's October 25, 2026 Apache 2.0 open model - 33B parameters and 512K context, aimed at agentic coding on modest hardware — and its weights are open while GPT-5.2 is API-only.
Lowest cost at scale
Nanbeige4.3-33B
Its weights are open, so at volume you pay for your own hardware instead of GPT-5.2's $1.75/$14 per 1M tokens.
Largest single-prompt input
Nanbeige4.3-33B
Its 512K tokens window is about 1.3× larger than GPT-5.2's 400K, fitting roughly 786 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Nanbeige4.3-33B
At Open weight (self-host / free) it undercuts GPT-5.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Nanbeige4.3-33B
Larger 512K tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Nanbeige4.3-33B
Open weights let you run it on your own hardware; GPT-5.2 is API-only.
Anyone whose priority is strong all-round reasoning
→ GPT-5.2
It is specifically built for that.
Anyone whose priority is 33b dense open-weight model under apache 2.0 with a 524,288-token context
→ Nanbeige4.3-33B
That is its strongest area.
An enterprise with regional data-residency rules
→ GPT-5.2 or Nanbeige4.3-33B
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GPT-5.2: where it fits
A capable GPT-5-generation all-rounder, now succeeded by GPT-5.5. Released December 11, 2025 by OpenAI, it is built for strong all-round reasoning, reliable structured output, broad ecosystem and tooling, and professional workflows.
Its trade-offs are real: superseded by GPT-5.5, and smaller context than flagships. At $1.75 in / $14 out per million tokens, it sits in the mid price band.
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: 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.
The bottom line for this matchup
The defining split here is open vs. closed. Nanbeige4.3-33B gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-5.2 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both GPT-5.2 and Nanbeige4.3-33B 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 GPT-5.2 or Nanbeige4.3-33B 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, GPT-5.2 leans toward strong all-round reasoning while Nanbeige4.3-33B leans toward 33b dense open-weight model under apache 2.0 with a 524,288-token context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-5.2 or Nanbeige4.3-33B?
Nanbeige4.3-33B is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-5.2 is API-metered at $1.75/$14 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Nanbeige4.3-33B — 512K tokens vs 400K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-5.2 and Nanbeige4.3-33B together?
Yes — a multi-model platform like LumiChats gives you GPT-5.2, Nanbeige4.3-33B 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, GPT-5.2 or Nanbeige4.3-33B?
Nanbeige4.3-33B — released October 25, 2026, about 11 months after GPT-5.2.
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.