Compare / Ling 3.0 Tiny vs Gemma 3n E4B

Ling 3.0 Tiny vs Gemma 3n E4B

Updated November 2026

Verdict

Ling 3.0 Tiny scores higher on the Small LM Index: 51.9 vs 18.2. On the 3 benchmarks both labs report, Ling 3.0 Tiny wins 3 and Gemma 3n E4B wins 0. Both need about 5.7 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.

Pick Ling 3.0 Tiny if…

you want the strongest model overall
you need math and reasoning
you work with long documents
you need a permissive license (MIT)

Pick Gemma 3n E4B if…

you need image input

Specifications

	Ling 3.0 Tiny	Gemma 3n E4B
Small LM Index	51.9 (likely 47–57)	18.2 (likely 16–21)
Lab	InclusionAI (Ling) · China	Google · United States
Released	2026-08-10	2025-06-26
Parameters	7.9B (1.4B active, MoE)	7.8B (dense)
Memory at 4-bit	~5.7 GB	~5.5 GB
Context window	131K tokens	32K tokens
Input	text	text, image, audio, video
Reasoning	Hybrid (thinking on/off)	No thinking
License	MIT	Gemma
API price / 1M tokens	Self-host only	Self-host only
Hugging Face downloads (30 days)	14.3K	12.7K

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	Ling 3.0 Tiny	Gemma 3n E4B
MMLU-Pro Knowledge	65.8^*	50.6
GPQA Diamond Knowledge	73.4	23.7
AIME Math	73.3^*	11.6
LiveCodeBench Coding	≈ 59	25.7
IFBench Instruction following	60.6^*	—
MMLU Knowledge	—	64.9
Multilingual MMLU Multilingual	—	60.3

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, Ling 3.0 Tiny or Gemma 3n E4B?

Ling 3.0 Tiny scores higher on the Small LM Index: 51.9 vs 18.2. On the 3 benchmarks both labs report, Ling 3.0 Tiny wins 3 and Gemma 3n E4B wins 0.

Which is cheaper to run?

Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 5.7 GB of memory at 4-bit quantization.

Which has the longer context window?

Ling 3.0 Tiny has the longer context window: 131K tokens against 32K.

Can I use Ling 3.0 Tiny and Gemma 3n E4B commercially?

Ling 3.0 Tiny is released under MIT and Gemma 3n E4B under Gemma. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.