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GPT-5.2-Codex vs GLM 4.7 Flash

Detailed technical comparison between GPT-5.2-Codex (OpenAI) and GLM 4.7 Flash (Zhipu AI). Review live API token pricing, context window capabilities, time-to-first-token latency, and verified benchmark scores side-by-side.

⚡ Executive Summary & Verdict

Comparison Snapshot

GPT-5.2-Codex: 6 WinsvsGLM 4.7 Flash: 0 Wins
Context Leader

GPT-5.2-Codex

400,000 tokens
Agentic Tool-Calling

Tie

Equal Capability
Lowest Latency (TTFT)

Tie

Equal Speed
Input Price Leader

GLM 4.7 Flash

$0.06 / MTok
OpenAIactive

GPT-5.2-Codex

GPT-5.2-Codex is an upgraded version of GPT-5.1-Codex optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks....

View GPT-5.2-Codex Full Specs →
Zhipu AIactive

GLM 4.7 Flash

As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning,...

View GLM 4.7 Flash Full Specs →

Technical Specifications

🏆 = Superior Spec
SpecificationGPT-5.2-CodexGLM 4.7 Flash
ProviderOpenAIZhipu AI
Context Window400,000 tokens🏆202,752 tokens
Agent SuitabilityN/AN/A
Time to First Token (TTFT)N/AN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-01-142026-01-19

API Pricing Comparison

Input Price per Million Tokens

GPT-5.2-Codex

$1.75

GLM 4.7 Flash

$0.06

Output Price per Million Tokens

GPT-5.2-Codex

$14.00

GLM 4.7 Flash

$0.40

💡 Cost Ratio: GLM 4.7 Flash is 28.9x cheaper per input token than GPT-5.2-Codex.

Want to test both models live?

Run side-by-side prompt benchmarks in our dynamic multi-model Sandbox. Compare execution speeds, latency metrics, and compute actual costs in real-time.

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Benchmark Performance Metrics

Standardized Scores (0–100%)

Scores show verified raw accuracy percentages across standardized AI evaluation suites. Higher bars indicate superior performance in that domain.

MMLUGeneral knowledge & multi-task understanding
93.0%vs77.2%+15.8% GPT-5.2-Codex
GPT-5.2-Codex 🏆
GLM 4.7 Flash
HumanEvalPython coding & logic synthesis
99.0%vs78.5%+20.5% GPT-5.2-Codex
GPT-5.2-Codex 🏆
GLM 4.7 Flash
MATHComplex mathematical problem solving
81.4%vs40.0%+41.4% GPT-5.2-Codex
GPT-5.2-Codex 🏆
GLM 4.7 Flash
GPQAGraduate-level expert reasoning
56.4%vs31.0%+25.4% GPT-5.2-Codex
GPT-5.2-Codex 🏆
GLM 4.7 Flash
HellaSwagCommonsense reasoning and inference
88.6%vs80.0%+8.6% GPT-5.2-Codex
GPT-5.2-Codex 🏆
GLM 4.7 Flash
MT-BenchMulti-turn conversation flow quality
9.5%vs8.1%+1.4% GPT-5.2-Codex
GPT-5.2-Codex 🏆
GLM 4.7 Flash

GPT-5.2-Codex Quirks & Gotchas

No developer gotchas reported.

GLM 4.7 Flash Quirks & Gotchas

No developer gotchas reported.

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