Compare / DeepSeek-R1-0528-Qwen3-8B vs Gemma 4 E4B

DeepSeek-R1-0528-Qwen3-8B vs Gemma 4 E4B

Updated November 2026

Verdict

DeepSeek-R1-0528-Qwen3-8B scores higher on the Small LM Index: 53.1 vs 38.2. On the 3 benchmarks both labs report, DeepSeek-R1-0528-Qwen3-8B wins 3 and Gemma 4 E4B wins 0. Both need about 6.8 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.

Pick DeepSeek-R1-0528-Qwen3-8B if…

you want the strongest model overall
you mostly write code
you need math and reasoning

Pick Gemma 4 E4B if…

you need image input
you want the more widely used model (more quantized builds and community support)

Specifications

	DeepSeek-R1-0528-Qwen3-8B	Gemma 4 E4B
Small LM Index	53.1 (likely 48–58)	38.2 (likely 34–42)
Lab	DeepSeek · China	Google · United States
Released	2025-05-29	2026-04-02
Parameters	8.2B (dense)	8B (dense)
Memory at 4-bit	~6.8 GB	~5.6 GB
Context window	128K tokens	128K tokens
Input	text	text, image, audio
Reasoning	Always thinks	Hybrid (thinking on/off)
License	MIT	Apache-2.0
API price / 1M tokens	Self-host only	Self-host only
Hugging Face downloads (30 days)	357.7K	4.1M

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	DeepSeek-R1-0528-Qwen3-8B	Gemma 4 E4B
MMLU-Pro Knowledge	≈ 74	69.4
GPQA Diamond Knowledge	61.1	58.6
AIME Math	76.3	42.5
LiveCodeBench Coding	60.5	52.0
τ²-Bench Agentic	—	42.2
MMMU-Pro Vision	—	52.6

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, DeepSeek-R1-0528-Qwen3-8B or Gemma 4 E4B?

DeepSeek-R1-0528-Qwen3-8B scores higher on the Small LM Index: 53.1 vs 38.2. On the 3 benchmarks both labs report, DeepSeek-R1-0528-Qwen3-8B wins 3 and Gemma 4 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 6.8 GB of memory at 4-bit quantization.

Which has the longer context window?

Both have a 128K-token context window.

Can I use DeepSeek-R1-0528-Qwen3-8B and Gemma 4 E4B commercially?

DeepSeek-R1-0528-Qwen3-8B is released under MIT and Gemma 4 E4B under Apache-2.0. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.