Gemini 3.1 Pro vs Nanbeige4.3-33B
Google · US | Nanbeige LLM Lab (BOSS Zhipin) · China · Updated June 2026
Quick verdict
Pick Gemini 3.1 Pro for full multimodal input — text, image, audio and video in one 1m-token window or long video and document analysis. 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; Gemini 3.1 Pro if you want a managed API.
Gemini 3.1 Pro (Google, 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. Gemini 3.1 Pro is a 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. 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 Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: Gemini 3.1 Pro holds 2× more — 1M (~1,573 pages) vs 512K tokens (~786 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 8 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 | Gemini 3.1 Pro | Nanbeige4.3-33B |
|---|---|---|
| Provider | Google (US) | Nanbeige LLM Lab (BOSS Zhipin) (China) |
| Released | February 19, 2026 | October 25, 2026 |
| Context window | 1M (~1,573 pages) | 512K tokens (~786 pages) |
| Price (in/out) | $2/$12 per 1M tokens | Open weight (self-host / free) |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, image, audio, video, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | 26.3% | Not published |
Who wins what
Full multimodal input — text, image, audio and video in one 1M-token window
Gemini 3.1 Pro
Its 1M window holds about 2× more than Nanbeige4.3-33B's 512K tokens in a single prompt.
Long video and document analysis
Gemini 3.1 Pro
Nanbeige4.3-33B is comparatively weak here — no public first-party API or per-token price; Artificial Analysis lists no publicly available API
Agentic reasoning (high ARC-AGI-2)
Gemini 3.1 Pro
A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window — and it carries the larger 1M context.
33B dense open-weight model under Apache 2.0 with a 524,288-token context
Nanbeige4.3-33B
Open weights make this possible at all — Gemini 3.1 Pro is API-only, so it cannot leave the vendor's servers.
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 its weights are open while Gemini 3.1 Pro is API-only.
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 it is the newer of the two.
Lowest cost at scale
Nanbeige4.3-33B
Its weights are open, so at volume you pay for your own hardware instead of Gemini 3.1 Pro's $2/$12 per 1M tokens.
Largest single-prompt input
Gemini 3.1 Pro
Its 1M window is about 2× larger than Nanbeige4.3-33B's 512K tokens, fitting roughly 1,573 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 Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 3.1 Pro
Larger 1M 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; Gemini 3.1 Pro is API-only.
Anyone whose priority is full multimodal input — text, image, audio and video in one 1m-token window
→ Gemini 3.1 Pro
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
→ Gemini 3.1 Pro 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.
Gemini 3.1 Pro: where it fits
A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for full multimodal input — text, image, audio and video in one 1M-token window, long video and document analysis, agentic reasoning (high ARC-AGI-2), and broad multimodal understanding across formats.
Its trade-offs are real: long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M), premium price per token at $2/$12, and superseded within Google's own line by the newer 3.5/3.6 Flash releases for cost-sensitive work. At $2 in / $12 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. Gemini 3.1 Pro 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 Gemini 3.1 Pro 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 Gemini 3.1 Pro 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, Gemini 3.1 Pro leans toward full multimodal input — text, image, audio and video in one 1m-token window 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, Gemini 3.1 Pro or Nanbeige4.3-33B?
Nanbeige4.3-33B is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $2/$12 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?
Gemini 3.1 Pro — 1M vs 512K tokens, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 3.1 Pro and Nanbeige4.3-33B together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, 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, Gemini 3.1 Pro or Nanbeige4.3-33B?
Nanbeige4.3-33B — released October 25, 2026, about 8 months after Gemini 3.1 Pro.
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.