GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning): The Ultimate Performance & Pricing Comparison

Deep dive into reasoning, benchmarks, and latency insights.

The Final Verdict in the GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) Showdown

After a comprehensive analysis, GPT-5.2 Codex (xhigh) emerges as the superior choice for users prioritizing raw intelligence and reasoning. However, GLM-4.7-Flash (Reasoning) remains a highly competitive option, especially for scenarios demanding faster response times and cost-efficiency. This detailed GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) comparison will break down every critical metric to help you make the best decision.

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Model Snapshot

Key decision metrics at a glance.

GPT-5.2 Codex (xhigh)

OpenAI

Reasoning: 6
Coding: 4
Multimodal: 4
Long Context: 6
Blended Price / 1M tokens: $0.005
P95 Latency: 1000ms
Tokens per second: 118.561tokens/sec

GLM-4.7-Flash (Reasoning)

Other

Reasoning: 6
Coding: 3
Multimodal: 3
Long Context: 4
Blended Price / 1M tokens: $0.000
P95 Latency: 1000ms
Tokens per second: 89.62tokens/sec

Overall Capabilities

The capability radar provides a holistic view of the GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) matchup. This chart illustrates each model's strengths and weaknesses at a glance, forming a cornerstone of our GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) analysis.

This radar chart visually maps the core capabilities (reasoning, coding, math proxy, multimodal, long context) of `GPT-5.2 Codex (xhigh)` vs `GLM-4.7-Flash (Reasoning)`.

Benchmark Breakdown

For a granular look, this chart directly compares scores across standardized benchmarks. In the critical MMLU Pro test, a key part of the GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) debate, GPT-5.2 Codex (xhigh) scores 60 against GLM-4.7-Flash (Reasoning)'s 60. This data-driven approach is essential for any serious GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) comparison.

This grouped bar chart provides a side-by-side comparison for each benchmark metric.

Speed & Latency

Speed is a crucial factor in the GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) decision for interactive applications. The metrics below highlight the trade-offs you should weigh before shipping to production.

Time to First Token

GPT-5.2 Codex (xhigh)300ms

GLM-4.7-Flash (Reasoning)300ms

Tokens per Second

GPT-5.2 Codex (xhigh)118.561

GLM-4.7-Flash (Reasoning)89.62

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The Economics of GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning)

Power is only one part of the equation. This GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) pricing analysis gives you a true sense of value.

Pricing Breakdown

Compare input and output pricing at a glance.

Real-World Cost Scenario

To process 1M input tokens and generate 250k output tokens, GPT-5.2 Codex (xhigh) would cost $0.005, whereas GLM-4.7-Flash (Reasoning) would cost $0.000. This practical calculation is vital for any developer considering the GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) choice.

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Which Model Wins the GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) Battle for You?

Choose GPT-5.2 Codex (xhigh) if...

Cost is a secondary concern to power in your GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) decision.

You need the most advanced reasoning capabilities available.

Your use case demands cutting-edge AI performance.

Choose GLM-4.7-Flash (Reasoning) if...

You need a highly responsive model for user-facing applications.

Your budget is a primary consideration in the GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) choice.

You are developing at scale where operational costs are critical.

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Your Questions about the GPT-5.2 Codex (xhigh) vs GLM-4.7-Flash (Reasoning) Comparison

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Data source: https://artificialanalysis.ai/