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GLM 4.7 vs GPT-5.5

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 GPT-5.5.

Zhipu AI

GLM 4.7

GLM-4.7 is Z.ai’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...

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OpenAI

GPT-5.5

GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...

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Technical Specifications

SpecificationGLM 4.7GPT-5.5
ProviderZhipu AIOpenAI
Context Window202,752 tokens1,050,000 tokens
Agent SuitabilityN/A95/100
Time to First Token (TTFT)N/A380 ms
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-12-222026-04-24

API Pricing Comparison

Input Price per Million Tokens

GLM 4.7

$0.40

GPT-5.5

$5.00

Output Price per Million Tokens

GLM 4.7

$1.75

GPT-5.5

$30.00

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%vs9420.0%
GLM 4.7
GPT-5.5
HumanEvalPython coding & logic synthesis
8200.0%vs9680.0%
GLM 4.7
GPT-5.5
MATHComplex mathematical problem solving
5150.0%vs9350.0%
GLM 4.7
GPT-5.5
GPQAGraduate-level expert reasoning
3800.0%vs8420.0%
GLM 4.7
GPT-5.5
HellaSwagCommonsense reasoning and inference
8350.0%vs9900.0%
GLM 4.7
GPT-5.5
MT-BenchMulti-turn conversation flow quality
865.0%vs970.0%
GLM 4.7
GPT-5.5

GLM 4.7 Quirks & Gotchas

No developer gotchas reported.

GPT-5.5 Quirks & Gotchas

  • Best for JSON schema adherence — strict mode available via response_format parameter
  • Requires explicit tool_choice for deterministic function calling