โ† Back to Model Hub/SIDE-BY-SIDE REVIEW
SHARE THIS:

GLM 5.2 vs o1

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for GLM 5.2 and o1.

Zhipu AI

GLM 5.2

GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...

View Full Specs
OpenAI

o1

The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...

View Full Specs

Technical Specifications

SpecificationGLM 5.2o1
ProviderZhipu AIOpenAI
Context Window1,048,576 tokens200,000 tokens
Agent SuitabilityN/A88/100
Time to First Token (TTFT)N/A2500 ms
Deployment Modelmanaged apimanaged api
Production Stabilitybetastable
API AvailableYesYes
Released Date2026-06-162024-12-17

API Pricing Comparison

Input Price per Million Tokens

GLM 5.2

$0.93

o1

$15.00

Output Price per Million Tokens

GLM 5.2

$3.00

o1

$60.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8950.0%vs9180.0%
GLM 5.2
o1
HumanEvalPython coding & logic synthesis
9120.0%vs9450.0%
GLM 5.2
o1
MATHComplex mathematical problem solving
8050.0%vs9480.0%
GLM 5.2
o1
GPQAGraduate-level expert reasoning
5350.0%vs7830.0%
GLM 5.2
o1
HellaSwagCommonsense reasoning and inference
8980.0%vs9200.0%
GLM 5.2
o1
MT-BenchMulti-turn conversation flow quality
930.0%vs940.0%
GLM 5.2
o1

GLM 5.2 Quirks & Gotchas

No developer gotchas reported.

o1 Quirks & Gotchas

  • โ–ธReasoning model โ€” high latency by design, not for real-time use
  • โ–ธBest for complex math/code reasoning where accuracy > speed
  • โ–ธUse o3-mini when you need reasoning with lower latency