Compare/Llama 3.2 Instruct 1B vs Qwen3.5 2B (Reasoning)

Llama 3.2 Instruct 1BvsQwen3.5 2B (Reasoning)

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Meta

Llama 3.2 Instruct 1B

Input
$0.1/M
Output
$0.1/M
Speed
144 tok/s
TTFT
0.52s
Alibaba

Qwen3.5 2B (Reasoning)

Input
$0.02/M
Output
$0.1/M
Speed
357 tok/s
TTFT
0.54s

Winner by Category

Cheaper
Qwen3.5 2B (Reasoning)
Faster (tok/s)
Qwen3.5 2B (Reasoning)
Lower Latency
Llama 3.2 Instruct 1B
Benchmarks (6-6)
Tie

Pricing Comparison

MetricLlama 3.2 Instruct 1BQwen3.5 2B (Reasoning)
Input ($/M tokens)$0.1$0.02
Output ($/M tokens)$0.1$0.1
Cost for 1M input + 100K output tokens:
Llama 3.2 Instruct 1B$0.11
Qwen3.5 2B (Reasoning)$0.03

Speed Comparison

Output Speed (tokens/s) — higher is better
Llama 3.2 Instruct 1B
144 tok/s
Qwen3.5 2B (Reasoning)
357 tok/s
Time to First Token (seconds) — lower is better
Llama 3.2 Instruct 1B
0.52s
Qwen3.5 2B (Reasoning)
0.54s

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
6.316.3
Coding Index
0.63.5
Math Index
0.0
GPQA Diamond
19.6%45.6%
MMLU-Pro
20.0%
LiveCodeBench
1.9%
AIME 2025
0.0%
MATH-500
14.0%
Humanity's Last Exam
5.3%2.1%
SciCode
1.7%2.8%
IFBench
22.8%31.5%
TerminalBench
0.0%3.8%
Llama 3.2 Instruct 1B6 wins
6 winsQwen3.5 2B (Reasoning)

Frequently Asked Questions

Which is cheaper, Llama 3.2 Instruct 1B or Qwen3.5 2B (Reasoning)?

Qwen3.5 2B (Reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.04/M tokens vs $0.10/M for Llama 3.2 Instruct 1B.

Which model performs better on benchmarks?

It's a tie — both models win 6 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Qwen3.5 2B (Reasoning) generates tokens faster at 357 tok/s vs 144 tok/s. Llama 3.2 Instruct 1B also has lower time-to-first-token (0.52s vs 0.54s).

When should I use Llama 3.2 Instruct 1B vs Qwen3.5 2B (Reasoning)?

Choose based on your priorities: Qwen3.5 2B (Reasoning) for lower cost, both perform similarly on benchmarks, and Qwen3.5 2B (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.