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

GLM 4.7 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 4.7 and o1.

Zhipu AI

GLM 4.7

GLM-4.7 is Z.aiโ€™s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing complex agent tasks while...

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 4.7o1
ProviderZhipu AIOpenAI
Context Window202,752 tokens200,000 tokens
Agent SuitabilityN/A88/100
Time to First Token (TTFT)N/A2500 ms
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-12-222024-12-17

API Pricing Comparison

Input Price per Million Tokens

GLM 4.7

$0.40

o1

$15.00

Output Price per Million Tokens

GLM 4.7

$1.75

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
8150.0%vs9180.0%
GLM 4.7
o1
HumanEvalPython coding & logic synthesis
8200.0%vs9450.0%
GLM 4.7
o1
MATHComplex mathematical problem solving
5150.0%vs9480.0%
GLM 4.7
o1
GPQAGraduate-level expert reasoning
3800.0%vs7830.0%
GLM 4.7
o1
HellaSwagCommonsense reasoning and inference
8350.0%vs9200.0%
GLM 4.7
o1
MT-BenchMulti-turn conversation flow quality
865.0%vs940.0%
GLM 4.7
o1

GLM 4.7 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