Nanbeige4.3-33B vs Microsoft Phi-4
Nanbeige LLM Lab (BOSS Zhipin) · China | Microsoft · US · Updated June 2026
Quick verdict
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. Pick Microsoft Phi-4 for strong reasoning for a small 14b open-weight model or mit-licensed — fully self-hostable at no per-token cost. On a tight budget at scale, Nanbeige4.3-33B is the value pick.
Nanbeige4.3-33B (Nanbeige LLM Lab (BOSS Zhipin), China) and Microsoft Phi-4 (Microsoft, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Microsoft Phi-4 is microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: Nanbeige4.3-33B holds 32× more — 512K tokens (~786 pages) vs 16K (~25 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 22 months (released October 25, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Nanbeige4.3-33B | Microsoft Phi-4 |
|---|---|---|
| Provider | Nanbeige LLM Lab (BOSS Zhipin) (China) | Microsoft (US) |
| Released | October 25, 2026 | January 10, 2025 |
| Context window | 512K tokens (~786 pages) | 16K (~25 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.07/$0.14 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
33B dense open-weight model under Apache 2.0 with a 524,288-token context
Nanbeige4.3-33B
Its 512K tokens window holds about 32× more than Microsoft Phi-4's 16K in a single prompt.
Artificial Analysis Intelligence Index of 20, first of the 8 models in its comparison class
Nanbeige4.3-33B
Microsoft Phi-4 is comparatively weak here — a tiny 16K context — by far the smallest window in this comparison
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 carries the larger 512K tokens context.
Strong reasoning for a small 14B open-weight model
Microsoft Phi-4
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
MIT-licensed — fully self-hostable at no per-token cost
Microsoft Phi-4
Nanbeige4.3-33B is comparatively weak here — no public first-party API or per-token price; Artificial Analysis lists no publicly available API
Runs on modest or local hardware
Microsoft Phi-4
Microsoft Phi-4 lists runs on modest or local hardware among its strengths; Nanbeige4.3-33B does not.
Lowest cost at scale
Nanbeige4.3-33B
Its weights are open, so at volume you pay for your own hardware instead of Microsoft Phi-4's $0.07/$0.14 per 1M tokens.
Largest single-prompt input
Nanbeige4.3-33B
Its 512K tokens window is about 32× larger than Microsoft Phi-4's 16K, 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 Microsoft Phi-4, 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.
Anyone whose priority is 33b dense open-weight model under apache 2.0 with a 524,288-token context
→ Nanbeige4.3-33B
It is specifically built for that.
Anyone whose priority is strong reasoning for a small 14b open-weight model
→ Microsoft Phi-4
That is its strongest area.
An enterprise with regional data-residency rules
→ Microsoft Phi-4 or Nanbeige4.3-33B
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Microsoft Phi-4: where it fits
Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. Released January 10, 2025 by Microsoft, it is built for strong reasoning for a small 14B open-weight model, mIT-licensed — fully self-hostable at no per-token cost, runs on modest or local hardware, and very cheap hosted inference at about $0.07/$0.14.
Its trade-offs: a tiny 16K context — by far the smallest window in this comparison, text only — no image, audio or video input, an early-2025 small model, outclassed on hard tasks by 2026 flagships, and no first-party per-token API; hosted prices are third-party. At $0.07 in / $0.14 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Nanbeige4.3-33B (China) and Microsoft Phi-4 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Nanbeige4.3-33B is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Nanbeige4.3-33B and Microsoft Phi-4 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 Nanbeige4.3-33B or Microsoft Phi-4 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, Nanbeige4.3-33B leans toward 33b dense open-weight model under apache 2.0 with a 524,288-token context while Microsoft Phi-4 leans toward strong reasoning for a small 14b open-weight model, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Nanbeige4.3-33B or Microsoft Phi-4?
Nanbeige4.3-33B is cheaper — Open weight (self-host / free) vs $0.07/$0.14 per 1M tokens.
Which has the bigger context window?
Nanbeige4.3-33B — 512K tokens vs 16K, about 32× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Nanbeige4.3-33B and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Nanbeige4.3-33B, Microsoft Phi-4 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, Nanbeige4.3-33B or Microsoft Phi-4?
Nanbeige4.3-33B — released October 25, 2026, about 22 months after Microsoft Phi-4.
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.