Compare/o1-mini vs Llama 3.2 Instruct 90B (Vision)

o1-minivsLlama 3.2 Instruct 90B (Vision)

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

OpenAI

o1-mini

Input
$0/M
Output
$0/M
Speed
TTFT
Meta

Llama 3.2 Instruct 90B (Vision)

Input
$0.72/M
Output
$0.72/M
Speed
42 tok/s
TTFT
0.37s

Winner by Category

Cheaper
o1-mini
Faster (tok/s)
Llama 3.2 Instruct 90B (Vision)
Lower Latency
o1-mini
Benchmarks (6-0)
o1-mini

Pricing Comparison

Metrico1-miniLlama 3.2 Instruct 90B (Vision)
Input ($/M tokens)$0$0.72
Output ($/M tokens)$0$0.72
Cost for 1M input + 100K output tokens:
o1-mini$0.00
Llama 3.2 Instruct 90B (Vision)$0.79

Speed Comparison

Output Speed (tokens/s) — higher is better
o1-mini
Llama 3.2 Instruct 90B (Vision)
42 tok/s
Time to First Token (seconds) — lower is better
o1-mini
Llama 3.2 Instruct 90B (Vision)
0.37s

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
20.411.9
Coding Index
Math Index
GPQA Diamond
60.3%43.2%
MMLU-Pro
74.2%67.1%
LiveCodeBench
57.6%21.4%
AIME 2025
MATH-500
94.4%62.9%
Humanity's Last Exam
4.9%4.9%
SciCode
32.3%24.0%
IFBench
TerminalBench
o1-mini6 wins
0 winsLlama 3.2 Instruct 90B (Vision)

Frequently Asked Questions

Which is cheaper, o1-mini or Llama 3.2 Instruct 90B (Vision)?

o1-mini is cheaper overall. Its blended price (3:1 input/output ratio) is $0.00/M tokens vs $0.72/M for Llama 3.2 Instruct 90B (Vision).

Which model performs better on benchmarks?

o1-mini wins 6 out of 12 benchmarks compared to 0 for Llama 3.2 Instruct 90B (Vision). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Llama 3.2 Instruct 90B (Vision) generates tokens faster at 42 tok/s vs 0 tok/s. o1-mini also has lower time-to-first-token (0.00s vs 0.37s).

When should I use o1-mini vs Llama 3.2 Instruct 90B (Vision)?

Choose based on your priorities: o1-mini for lower cost, o1-mini for stronger benchmark performance, and Llama 3.2 Instruct 90B (Vision) for faster generation. For latency-sensitive apps, check the TTFT comparison above.