Compare/o3-mini (high) vs GLM-5.1 (Reasoning)

o3-mini (high)vsGLM-5.1 (Reasoning)

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

OpenAI

o3-mini (high)

Input
$1.1/M
Output
$4.4/M
Speed
137 tok/s
TTFT
22.56s
Z AI

GLM-5.1 (Reasoning)

Input
$1.4/M
Output
$4.4/M
Speed
48 tok/s
TTFT
0.82s

Winner by Category

Cheaper
o3-mini (high)
Faster (tok/s)
o3-mini (high)
Lower Latency
GLM-5.1 (Reasoning)
Benchmarks (3-7)
GLM-5.1 (Reasoning)

Pricing Comparison

Metrico3-mini (high)GLM-5.1 (Reasoning)
Input ($/M tokens)$1.1$1.4
Output ($/M tokens)$4.4$4.4
Cost for 1M input + 100K output tokens:
o3-mini (high)$1.54
GLM-5.1 (Reasoning)$1.84

Speed Comparison

Output Speed (tokens/s) — higher is better
o3-mini (high)
137 tok/s
GLM-5.1 (Reasoning)
48 tok/s
Time to First Token (seconds) — lower is better
o3-mini (high)
22.56s
GLM-5.1 (Reasoning)
0.82s

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
25.251.4
Coding Index
17.343.4
Math Index
GPQA Diamond
77.3%86.8%
MMLU-Pro
80.2%
LiveCodeBench
73.4%
AIME 2025
MATH-500
98.5%
Humanity's Last Exam
12.3%28.0%
SciCode
39.8%43.8%
IFBench
67.1%76.3%
TerminalBench
6.1%43.2%
o3-mini (high)3 wins
7 winsGLM-5.1 (Reasoning)

Frequently Asked Questions

Which is cheaper, o3-mini (high) or GLM-5.1 (Reasoning)?

o3-mini (high) is cheaper overall. Its blended price (3:1 input/output ratio) is $1.93/M tokens vs $2.15/M for GLM-5.1 (Reasoning).

Which model performs better on benchmarks?

GLM-5.1 (Reasoning) wins 7 out of 12 benchmarks compared to 3 for o3-mini (high). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

o3-mini (high) generates tokens faster at 137 tok/s vs 48 tok/s. However, GLM-5.1 (Reasoning) has lower time-to-first-token (0.82s vs 22.56s).

When should I use o3-mini (high) vs GLM-5.1 (Reasoning)?

Choose based on your priorities: o3-mini (high) for lower cost, GLM-5.1 (Reasoning) for stronger benchmark performance, and o3-mini (high) for faster generation. For latency-sensitive apps, check the TTFT comparison above.