Kimi K3 vs Nanbeige4.3-33B
Moonshot AI · China | Nanbeige LLM Lab (BOSS Zhipin) · China · Updated June 2026
Quick verdict
Pick Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (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.
Kimi K3 (Moonshot AI) and Nanbeige4.3-33B (Nanbeige LLM Lab (BOSS Zhipin)) are two of the models people most often weigh against each other in 2026. Kimi K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. 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: Kimi K3 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 3 months (released October 25, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Kimi K3 | Nanbeige4.3-33B |
|---|---|---|
| Provider | Moonshot AI (China) | Nanbeige LLM Lab (BOSS Zhipin) (China) |
| Released | July 27, 2026 | October 25, 2026 |
| Context window | 1M (~1,573 pages) | 512K tokens (~786 pages) |
| Price (in/out) | $3/$15 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
Largest open-weight model at release — 2.8T sparse MoE, self-hostable
Kimi K3
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
1M-token context with native vision (text, image and video)
Kimi K3
Its 1M window holds about 2× more than Nanbeige4.3-33B's 512K tokens in a single prompt.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness
Kimi K3
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it carries the larger 1M context.
33B dense open-weight model under Apache 2.0 with a 524,288-token context
Nanbeige4.3-33B
Kimi K3 is comparatively weak here — 2.8T params need serious hardware to self-host — weights are free, running is not
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
Kimi K3 is comparatively weak here — coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified
Lowest cost at scale
Nanbeige4.3-33B
Its weights are open, so at volume you pay for your own hardware instead of Kimi K3's $3/$15 per 1M tokens.
Largest single-prompt input
Kimi K3
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 Kimi K3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Kimi K3
Larger 1M window fits more in one prompt.
Anyone whose priority is largest open-weight model at release — 2.8t sparse moe, self-hostable
→ Kimi K3
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.
Kimi K3: where it fits
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.
Its trade-offs are real: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 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
Kimi K3 and Nanbeige4.3-33B overlap enough that the right pick depends on your specific job. Nanbeige4.3-33B costs less per token; Kimi K3 holds the larger context; and each leads in its own area — Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable, 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 Kimi K3 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 Kimi K3 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, Kimi K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable 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, Kimi K3 or Nanbeige4.3-33B?
Nanbeige4.3-33B is cheaper — $3/$15 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Kimi K3 — 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 Kimi K3 and Nanbeige4.3-33B together?
Yes — a multi-model platform like LumiChats gives you Kimi K3, 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, Kimi K3 or Nanbeige4.3-33B?
Nanbeige4.3-33B — released October 25, 2026, about 3 months after Kimi K3.
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.