DeepSeek V3.2 vs Nanbeige4.3-33B
DeepSeek · China | Nanbeige LLM Lab (BOSS Zhipin) · China · Updated June 2026
Quick verdict
Pick DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa) or agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes). 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.
DeepSeek V3.2 (DeepSeek) and Nanbeige4.3-33B (Nanbeige LLM Lab (BOSS Zhipin)) are two of the models people most often weigh against each other in 2026. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. 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: Nanbeige4.3-33B holds 4× more — 512K tokens (~786 pages) vs 131K (~197 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 11 months (released October 25, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | DeepSeek V3.2 | Nanbeige4.3-33B |
|---|---|---|
| Provider | DeepSeek (China) | Nanbeige LLM Lab (BOSS Zhipin) (China) |
| Released | December 1, 2025 | October 25, 2026 |
| Context window | 131K (~197 pages) | 512K tokens (~786 pages) |
| Price (in/out) | $0.28/$0.42 per 1M tokens | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | 73.1% | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA)
DeepSeek V3.2
DeepSeek V3.2 lists long-context efficiency via DeepSeek Sparse Attention (DSA) among its strengths; Nanbeige4.3-33B does not.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
DeepSeek V3.2 lists agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes) among its strengths; Nanbeige4.3-33B does not.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
DeepSeek V3.2 lists elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386) 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 4× more than DeepSeek V3.2's 131K in a single prompt.
Artificial Analysis Intelligence Index of 20, first of the 8 models in its comparison class
Nanbeige4.3-33B
DeepSeek V3.2 is comparatively weak here — superseded - no longer listed on DeepSeek's current pricing page as of August 2026; DeepSeek V4-Flash/V4-Pro are the current models
Lab-reported Terminal-Bench 2.1 of 80.9 and DeepSWE of 65.5
Nanbeige4.3-33B
DeepSeek V3.2 is comparatively weak here — sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2)
Lowest cost at scale
Nanbeige4.3-33B
Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V3.2's $0.28/$0.42 per 1M tokens.
Largest single-prompt input
Nanbeige4.3-33B
Its 512K tokens window is about 4× larger than DeepSeek V3.2's 131K, 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 DeepSeek V3.2, 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 long-context efficiency via deepseek sparse attention (dsa)
→ DeepSeek V3.2
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.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs are real: superseded - no longer listed on DeepSeek's current pricing page as of August 2026; DeepSeek V4-Flash/V4-Pro are the current models, text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 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
DeepSeek V3.2 and Nanbeige4.3-33B overlap enough that the right pick depends on your specific job. Nanbeige4.3-33B costs less per token; Nanbeige4.3-33B holds the larger context; and each leads in its own area — DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa), 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 DeepSeek V3.2 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 DeepSeek V3.2 or Nanbeige4.3-33B better for coding?
Public SWE-Bench figures are not available for Nanbeige4.3-33B, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa) 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, DeepSeek V3.2 or Nanbeige4.3-33B?
Nanbeige4.3-33B is cheaper — $0.28/$0.42 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Nanbeige4.3-33B — 512K tokens vs 131K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and Nanbeige4.3-33B together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, 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, DeepSeek V3.2 or Nanbeige4.3-33B?
Nanbeige4.3-33B — released October 25, 2026, about 11 months after DeepSeek V3.2.
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.