Gemma 3 1B vs Qwen 3 0.6B

Google
Gemma 3 1B

Google · 1B params · Quality: 35

Alibaba
Qwen 3 0.6B

Alibaba · 0.6B params · Quality: 50

Architecture Comparison

SpecGemma 3 1BQwen 3 0.6B
TypeDENSEDENSE
Total Parameters1B0.6B
Active Parameters1B0.6B
Layers2628
Hidden Dimension1,5361,024
Attention Heads1616
KV Heads48
Context Length32,768131,072
Precision (default)BF16BF16

Memory Requirements

PrecisionGemma 3 1BQwen 3 0.6B
BF16 Weights2.0 GB1.2 GB
FP8 Weights1.0 GB0.6 GB
INT4 Weights0.5 GB0.3 GB
KV-Cache / Token26624 B57344 B
Activation Estimate0.20 GB0.20 GB

Minimum GPUs Needed (BF16)

H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU

Quality Benchmarks

BenchmarkGemma 3 1BQwen 3 0.6B
Overall3550
MMLU42.0N/A
HumanEval18.0N/A
GSM8K32.0N/A
MT-Bench60.0N/A

Gemma 3 1B

MMLU
42.0
HumanEval
18.0
GSM8K
32.0
MT-Bench
60.0

Qwen 3 0.6B

Capabilities

FeatureGemma 3 1BQwen 3 0.6B
Tool Use✗ No✗ No
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✗ No✗ No
Reasoning✗ No✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes

Recommendation Summary

  • ‣Qwen 3 0.6B scores higher on overall quality (50 vs 35).
  • ‣Qwen 3 0.6B has a smaller memory footprint (1.2 GB vs 2.0 GB BF16), making it easier to deploy on fewer GPUs.
  • ‣Qwen 3 0.6B supports a longer context window (131,072 vs 32,768 tokens).
Calculate ROI for Gemma 3 1B→Calculate ROI for Qwen 3 0.6B→

Compare Other Models

Gemma 3 1B vs DeepSeek R1→Gemma 3 1B vs DeepSeek V3→Gemma 3 1B vs Gemma 3 27B→Gemma 3 1B vs Llama 3.1 405B→Gemma 3 1B vs Llama 3.1 70B→Gemma 3 1B vs Llama 3.1 8B→Qwen 3 0.6B vs DeepSeek R1→Qwen 3 0.6B vs DeepSeek V3→Qwen 3 0.6B vs Gemma 3 27B→Qwen 3 0.6B vs Llama 3.1 405B→