Amazon Nova Pro vs Nanbeige4.3-33B

Amazon · US  |  Nanbeige LLM Lab (BOSS Zhipin) · China · Updated June 2026

Quick verdict

Pick Amazon Nova Pro for multimodal input across text, image and video at $0.80/$3.20 or deep aws and bedrock integration for enterprise pipelines. 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; Amazon Nova Pro if you want a managed API.

Amazon Nova Pro (Amazon, 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. Amazon Nova Pro is amazon's multimodal Bedrock model at $0.80/$3.20 with a 300K window — an enterprise-integrated generalist, not a frontier model. 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

Side-by-side specs

SpecAmazon Nova ProNanbeige4.3-33B
ProviderAmazon (US) Nanbeige LLM Lab (BOSS Zhipin) (China)
ReleasedDecember 5, 2024 October 25, 2026
Context window300K (~450 pages) 512K tokens (~786 pages)
Price (in/out)$0.8/$3.2 per 1M tokens Open weight (self-host / free)
Open weight?No — API only Yes — self-hostable
Modalitiestext, image, video, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Multimodal input across text, image and video at $0.80/$3.20

Amazon Nova Pro

Nanbeige4.3-33B is comparatively weak here — text-only input

Deep AWS and Bedrock integration for enterprise pipelines

Amazon Nova Pro

Amazon Nova Pro lists deep AWS and Bedrock integration for enterprise pipelines among its strengths; Nanbeige4.3-33B does not.

Balanced cost-to-capability for general business tasks

Amazon Nova Pro

Amazon Nova Pro lists balanced cost-to-capability for general business tasks 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 1.7× more than Amazon Nova Pro's 300K 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

Amazon Nova Pro is comparatively weak here — no published SWE-Bench Verified score

Lowest cost at scale

Nanbeige4.3-33B

Its weights are open, so at volume you pay for your own hardware instead of Amazon Nova Pro's $0.8/$3.2 per 1M tokens.

Largest single-prompt input

Nanbeige4.3-33B

Its 512K tokens window is about 1.7× larger than Amazon Nova Pro's 300K, 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 Amazon Nova Pro, 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; Amazon Nova Pro is API-only.

Anyone whose priority is multimodal input across text, image and video at $0.80/$3.20

→ Amazon Nova 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

→ Amazon Nova 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.

Amazon Nova Pro: where it fits

Amazon's multimodal Bedrock model at $0.80/$3.20 with a 300K window — an enterprise-integrated generalist, not a frontier model. Released December 5, 2024 by Amazon, it is built for multimodal input across text, image and video at $0.80/$3.20, deep AWS and Bedrock integration for enterprise pipelines, balanced cost-to-capability for general business tasks, and a 300K context for long documents and mixed media.

Its trade-offs are real: not a frontier reasoning or coding model against 2026 flagships, no published SWE-Bench Verified score, best value is realised inside the AWS ecosystem, and late-2024 model — older than most of the field here. At $0.8 in / $3.2 out per million tokens, it sits in the budget 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. Amazon Nova 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 Amazon Nova 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 pricing

Frequently asked questions

Is Amazon Nova 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, Amazon Nova Pro leans toward multimodal input across text, image and video at $0.80/$3.20 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, Amazon Nova 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 Amazon Nova Pro is API-metered at $0.8/$3.2 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 300K, about 1.7× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Amazon Nova Pro and Nanbeige4.3-33B together?

Yes — a multi-model platform like LumiChats gives you Amazon Nova 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, Amazon Nova Pro or Nanbeige4.3-33B?

Nanbeige4.3-33B — released October 25, 2026, about 23 months after Amazon Nova 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.