Compare / Nanbeige4.3 33B vs OLMo 3.1 32B Think

Nanbeige4.3 33B vs OLMo 3.1 32B Think

Updated November 2026

Verdict

Nanbeige4.3 33B scores higher on the Small LM Index: 77.8 vs 57.0. On the 2 benchmarks both labs report, Nanbeige4.3 33B wins 2 and OLMo 3.1 32B Think wins 0. Both need about 23 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.

Pick Nanbeige4.3 33B if…

you want the strongest model overall
you need math and reasoning
you work with long documents

Pick OLMo 3.1 32B Think if…

No clear advantage in the data we have.

Specifications

	Nanbeige4.3 33B	OLMo 3.1 32B Think
Small LM Index	77.8 (likely 77–80)	57.0 (likely 53–61)
Lab	Nanbeige (Kanzhun) · China	Ai2 (OLMo) · United States
Released	2026-10-24	2025-12-10
Parameters	33.1B (dense)	32.2B (dense)
Memory at 4-bit	~23 GB	~22 GB
Context window	524K tokens	64K tokens
Input	text	text
Reasoning	Always thinks	Always thinks
License	Apache-2.0	Apache-2.0
API price / 1M tokens	Self-host only	Self-host only
Hugging Face downloads (30 days)	17.8K	6.9K

Benchmarks

Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.

Benchmark	Nanbeige4.3 33B	OLMo 3.1 32B Think
GPQA Diamond Knowledge	89.5	57.5
AIME Math	95.2	78.1
LiveCodeBench Coding	≈ 89	83.3
Terminal-Bench Agentic	80.9	—
IFBench Instruction following	—	68.1
MMLU Knowledge	—	86.4
IFEval Instruction following	—	93.8
AA-LCR Long context	84.0	—

Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.

Questions

Which is better, Nanbeige4.3 33B or OLMo 3.1 32B Think?

Nanbeige4.3 33B scores higher on the Small LM Index: 77.8 vs 57.0. On the 2 benchmarks both labs report, Nanbeige4.3 33B wins 2 and OLMo 3.1 32B Think wins 0.

Which is cheaper to run?

Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 23 GB of memory at 4-bit quantization.

Which has the longer context window?

Nanbeige4.3 33B has the longer context window: 524K tokens against 64K.

Can I use Nanbeige4.3 33B and OLMo 3.1 32B Think commercially?

Both are released under the Apache-2.0 license. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.