Llama 3.2 1B vs Qwen 2.5 1.5B
Architecture Comparison
SpecLlama 3.2 1BQwen 2.5 1.5B
TypeDENSEDENSE
Total Parameters1.24B1.5B
Active Parameters1.24B1.5B
Layers1628
Hidden Dimension2,0481,536
Attention Heads3212
KV Heads82
Context Length131,07232,768
Precision (default)BF16BF16
Memory Requirements
PrecisionLlama 3.2 1BQwen 2.5 1.5B
BF16 Weights2.5 GB3.0 GB
FP8 Weights1.2 GB1.5 GB
INT4 Weights0.6 GB0.8 GB
KV-Cache / Token32768 B28672 B
Activation Estimate0.30 GB0.30 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Quality Benchmarks
BenchmarkLlama 3.2 1BQwen 2.5 1.5B
Overall3850
MMLU49.3N/A
HumanEval22.0N/A
GSM8K44.4N/A
MT-Bench62.0N/A
Llama 3.2 1B
MMLU
49.3
HumanEval
22.0
GSM8K
44.4
MT-Bench
62.0
Qwen 2.5 1.5B
Capabilities
FeatureLlama 3.2 1BQwen 2.5 1.5B
Tool Use✓ Yes✗ No
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Llama 3.2 1B)
$0.03/M
Input: $0.03/M
Cheapest Output (Qwen 2.5 1.5B)
N/A
| Provider | Llama 3.2 1B In $/M | Out $/M | Qwen 2.5 1.5B In $/M | Out $/M |
|---|---|---|---|---|
| together | $0.03 | $0.03 | — | — |
| fireworks | $0.10 | $0.10 | — | — |
Recommendation Summary
- ‣Qwen 2.5 1.5B scores higher on overall quality (50 vs 38).
- ‣Llama 3.2 1B has a smaller memory footprint (2.5 GB vs 3.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.2 1B supports a longer context window (131,072 vs 32,768 tokens).