GPT-5.3-Codex vs Nanbeige4.3-33B

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

Quick verdict

Pick GPT-5.3-Codex for dedicated coding agent or cli and ide integration. 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-5.3-Codex if you want a managed API.

GPT-5.3-Codex (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-5.3-Codex is openAI's coding-specialized agent model for autonomous software engineering. 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

SpecGPT-5.3-CodexNanbeige4.3-33B
ProviderOpenAI (US) Nanbeige LLM Lab (BOSS Zhipin) (China)
ReleasedFebruary 5, 2026 October 25, 2026
Context window400K (~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
Modalitiestext, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Dedicated coding agent

GPT-5.3-Codex

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

CLI and IDE integration

GPT-5.3-Codex

GPT-5.3-Codex lists cLI and IDE integration among its strengths; Nanbeige4.3-33B does not.

Autonomous software tasks

GPT-5.3-Codex

GPT-5.3-Codex lists autonomous software 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 1.3× more than GPT-5.3-Codex'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-5.3-Codex 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-5.3-Codex'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-5.3-Codex'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-5.3-Codex, 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-5.3-Codex is API-only.

Anyone whose priority is dedicated coding agent

→ GPT-5.3-Codex

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-5.3-Codex 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-5.3-Codex: where it fits

OpenAI's coding-specialized agent model for autonomous software engineering. Released February 5, 2026 by OpenAI, it is built for dedicated coding agent, cLI and IDE integration, autonomous software tasks, and tool calling.

Its trade-offs are real: coding-specialized, narrower general use, and retired in favor of GPT-5.5 Codex. 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-5.3-Codex 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-5.3-Codex 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 GPT-5.3-Codex 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-5.3-Codex leans toward dedicated coding agent 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-5.3-Codex 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-5.3-Codex 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-5.3-Codex and Nanbeige4.3-33B together?

Yes — a multi-model platform like LumiChats gives you GPT-5.3-Codex, 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-5.3-Codex or Nanbeige4.3-33B?

Nanbeige4.3-33B — released October 25, 2026, about 9 months after GPT-5.3-Codex.

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.