MiniMax M3 vs Nanbeige4.3-33B
MiniMax · China | Nanbeige LLM Lab (BOSS Zhipin) · China · Updated June 2026
Quick verdict
Pick MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context or native multimodal input — text, image and video. 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. On a tight budget at scale, Nanbeige4.3-33B is the value pick.
MiniMax M3 (MiniMax) and Nanbeige4.3-33B (Nanbeige LLM Lab (BOSS Zhipin)) are two of the models people most often weigh against each other in 2026. MiniMax M3 is miniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. 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 and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: MiniMax M3 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 5 months (released October 25, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | MiniMax M3 | Nanbeige4.3-33B |
|---|---|---|
| Provider | MiniMax (China) | Nanbeige LLM Lab (BOSS Zhipin) (China) |
| Released | May 31, 2026 | October 25, 2026 |
| Context window | 1M (~1,573 pages) | 512K tokens (~786 pages) |
| Price (in/out) | $0.23/$0.96 per 1M tokens | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, image, video, code | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Open-weight 428B MoE (~23B active per token) with a 1M-token context
MiniMax M3
Its 1M window holds about 2× more than Nanbeige4.3-33B's 512K tokens in a single prompt.
Native multimodal input — text, image and video
MiniMax M3
Nanbeige4.3-33B is comparatively weak here — text-only input
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5
MiniMax M3
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
33B dense open-weight model under Apache 2.0 with a 524,288-token context
Nanbeige4.3-33B
MiniMax M3 is comparatively weak here — price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M
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 is the newer of the two.
Lab-reported Terminal-Bench 2.1 of 80.9 and DeepSWE of 65.5
Nanbeige4.3-33B
MiniMax M3 is comparatively weak here — miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified
Lowest cost at scale
Nanbeige4.3-33B
Its weights are open, so at volume you pay for your own hardware instead of MiniMax M3's $0.23/$0.96 per 1M tokens.
Largest single-prompt input
MiniMax M3
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 MiniMax M3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiniMax M3
Larger 1M window fits more in one prompt.
Anyone whose priority is open-weight 428b moe (~23b active per token) with a 1m-token context
→ MiniMax M3
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.
MiniMax M3: where it fits
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. Released May 31, 2026 by MiniMax, it is built for open-weight 428B MoE (~23B active per token) with a 1M-token context, native multimodal input — text, image and video, reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5, and low entry pricing at $0.30/$1.20 per million up to 512K tokens.
Its trade-offs are real: price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M, miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified, sWE-Bench Pro is a different, harder benchmark than SWE-Bench Verified, and newer than M2.7 but with less independent testing so far. At $0.23 in / $0.96 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
MiniMax M3 and Nanbeige4.3-33B overlap enough that the right pick depends on your specific job. Nanbeige4.3-33B costs less per token; MiniMax M3 holds the larger context; and each leads in its own area — MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context, 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 MiniMax M3 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 MiniMax M3 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, MiniMax M3 leans toward open-weight 428b moe (~23b active per token) with a 1m-token context 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, MiniMax M3 or Nanbeige4.3-33B?
Nanbeige4.3-33B is cheaper — $0.23/$0.96 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
MiniMax M3 — 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 MiniMax M3 and Nanbeige4.3-33B together?
Yes — a multi-model platform like LumiChats gives you MiniMax M3, 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, MiniMax M3 or Nanbeige4.3-33B?
Nanbeige4.3-33B — released October 25, 2026, about 5 months after MiniMax M3.
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.