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GLM-5.2 vs GPT-4.1 mini

Detailed pricing comparison and cost analysis.

Updated September 2026

Cost Simulator

GLM-5.2 Cost
$2.28
GPT-4.1 mini Cost
$0.72
GPT-4.1 mini is 68% cheaper
FeatureGLM-5.2GPT-4.1 mini
ProviderZhipu AIOpenAI
Input Price (1M)$1.40$0.40
Output Price (1M)$4.40$1.60
Context Window1,000,0001,000,000

Verdict

GLM-5.2 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens. GPT-4.1 mini costs $0.40 per 1M input tokens and $1.60 per 1M output tokens. GPT-4.1 mini is 71% cheaper on input tokens than GLM-5.2. For output tokens, GPT-4.1 mini is the more affordable option at $1.60/1M vs $4.40.

On context window, GLM-5.2 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.

When to choose GLM-5.2

  • ✓ You are already integrated with Zhipu AI

When to choose GPT-4.1 mini

  • ✓ You need the lowest input token cost ($ 0.40/1M)
  • ✓ Your workload is output-heavy — GPT-4.1 mini generates text cheaper
  • ✓ You are already integrated with OpenAI

Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.

Frequently Asked Questions

Is GLM-5.2 cheaper than GPT-4.1 mini?

GPT-4.1 mini is cheaper on input tokens at $0.40/1M vs $1.40/1M for GLM-5.2 — a 71% saving.

What is the context window of GLM-5.2 vs GPT-4.1 mini?

GLM-5.2 has a 1,000,000-token context window. GPT-4.1 mini has a 1,000,000-token context window. GLM-5.2 supports the larger context, suitable for longer documents and agentic workflows.

Which model is better: GLM-5.2 or GPT-4.1 mini?

The best choice depends on your use case. For cost efficiency on input tokens, GPT-4.1 mini is the cheaper option. For maximum context length, GLM-5.2 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.