Atria Dawn Preview vs Nanbeige4.3-33B

Shanghai AI Laboratory · China  |  Nanbeige LLM Lab (BOSS Zhipin) · China · Updated June 2026

Quick verdict

Pick Atria Dawn Preview for best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0) or 744b-parameter moe built on a glm-5.2 base, released under a fully open mit license. 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.

Atria Dawn Preview (Shanghai AI Laboratory) and Nanbeige4.3-33B (Nanbeige LLM Lab (BOSS Zhipin)) are two of the models people most often weigh against each other in 2026. Atria Dawn Preview is a free, MIT-licensed 744B open-weight model from Shanghai AI Lab that beat GPT-5.6 Sol on the BrowseComp benchmark — released quietly, weights before paper. 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. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.

Key differences at a glance

Side-by-side specs

SpecAtria Dawn PreviewNanbeige4.3-33B
ProviderShanghai AI Laboratory (China) Nanbeige LLM Lab (BOSS Zhipin) (China)
ReleasedSeptember 11, 2026 October 25, 2026
Context window256K tokens (~393 pages) 512K tokens (~786 pages)
Price (in/out)Open weight (self-host / free) Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Best-in-class among tested models on several of Shanghai AI Lab's own benchmarks, including BrowseComp (92.5, ahead of GPT-5.6 Sol's 92.2) and DeepSearchQA (96.0)

Atria Dawn Preview

Nanbeige4.3-33B is comparatively weak here — coding benchmarks are self-reported, with non-standard settings noted on the model card and no independent reproduction

744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license

Atria Dawn Preview

Atria Dawn Preview lists 744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license among its strengths; Nanbeige4.3-33B does not.

Free to self-host — no API pricing, run entirely on your own hardware

Atria Dawn Preview

Atria Dawn Preview lists free to self-host — no API pricing, run entirely on your own hardware 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 2× more than Atria Dawn Preview's 256K tokens 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

Atria Dawn Preview is comparatively weak here — context window is reported inconsistently across trackers — most list 256K tokens, at least one lists 1M; unconfirmed which is accurate

Largest single-prompt input

Nanbeige4.3-33B

Its 512K tokens window is about 2× larger than Atria Dawn Preview's 256K tokens, 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 best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0)

→ Atria Dawn Preview

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.

Atria Dawn Preview: where it fits

A free, MIT-licensed 744B open-weight model from Shanghai AI Lab that beat GPT-5.6 Sol on the BrowseComp benchmark — released quietly, weights before paper. Released September 11, 2026 by Shanghai AI Laboratory, it is built for best-in-class among tested models on several of Shanghai AI Lab's own benchmarks, including BrowseComp (92.5, ahead of GPT-5.6 Sol's 92.2) and DeepSearchQA (96.0), 744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license, and free to self-host — no API pricing, run entirely on your own hardware.

Its trade-offs are real: context window is reported inconsistently across trackers — most list 256K tokens, at least one lists 1M; unconfirmed which is accurate, released quietly, with weights and an API posted to GitHub before Shanghai AI Lab's own technical report went up on arXiv days later, and a lab research preview rather than a commercial product — support and update cadence are unclear. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

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

Atria Dawn Preview and Nanbeige4.3-33B 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 — Atria Dawn Preview for best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0), Nanbeige4.3-33B for 33b dense open-weight model under apache 2.0 with a 524,288-token context. Rather than crowning one, run the same hard task through both once and let the results decide.

Want both Atria Dawn Preview 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 Atria Dawn Preview 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, Atria Dawn Preview leans toward best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0) 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, Atria Dawn Preview or Nanbeige4.3-33B?

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 tokens, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Atria Dawn Preview and Nanbeige4.3-33B together?

Yes — a multi-model platform like LumiChats gives you Atria Dawn Preview, 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, Atria Dawn Preview or Nanbeige4.3-33B?

Nanbeige4.3-33B — released October 25, 2026, about 44 days after Atria Dawn Preview.

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.