Which is cheaper, GLM-4.5-Flash or GPT-5.6 Cyber?
As of August 20, 2026, GLM-4.5-Flash is 100% cheaper on blended LLM API pricing. GLM-4.5-Flash costs $0.00 per million input tokens and $0.00 per million output tokens ($0.00 blended 1M-in + 1M-out). GPT-5.6 Cyber costs $12.50 / $75.00 per million tokens ($87.50 blended). Sources: https://docs.z.ai/guides/overview/pricing and https://developers.openai.com/api/docs/pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does GLM-4.5-Flash vs GPT-5.6 Cyber cost per million tokens?
GLM-4.5-Flash Z AI API pricing is $0.00 input and $0.00 output per million tokens via Z AI. GPT-5.6 Cyber OpenAI API pricing is $12.50 input and $75.00 output per million tokens via OpenAI. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, GLM-4.5-Flash or GPT-5.6 Cyber?
Neither GLM-4.5-Flash nor GPT-5.6 Cyber has a published SWE-bench or LiveCodeBench score on this page yet. Use the comparison table for the evals that do exist.
What is the context window for GLM-4.5-Flash vs GPT-5.6 Cyber?
GLM-4.5-Flash supports a 131K token context window, while GPT-5.6 Cyber supports 400K tokens. GPT-5.6 Cyber offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Which model performs better on benchmarks, GLM-4.5-Flash or GPT-5.6 Cyber?
Shared benchmark scores for GLM-4.5-Flash and GPT-5.6 Cyber are still limited. Check the comparison table for the evals each model has published. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Is GLM-4.5-Flash or GPT-5.6 Cyber better for production use?
Both GLM-4.5-Flash and GPT-5.6 Cyber are production API models. For cost-sensitive production traffic, GLM-4.5-Flash has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Can I switch between GLM-4.5-Flash and GPT-5.6 Cyber in my app?
Yes. Call GLM-4.5-Flash through Z AI and GPT-5.6 Cyber through OpenAI with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
How accurate is this GLM-4.5-Flash vs GPT-5.6 Cyber pricing data?
List rates are the latest published Z AI API pricing and OpenAI API pricing, quoted per million tokens. Official rate sheets: https://docs.z.ai/guides/overview/pricing and https://developers.openai.com/api/docs/pricing. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
What is the output token limit for GLM-4.5-Flash and GPT-5.6 Cyber?
GLM-4.5-Flash supports up to an unspecified number of output tokens per request, while GPT-5.6 Cyber supports up to 128K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Which model has better throughput, GLM-4.5-Flash or GPT-5.6 Cyber?
Neither GLM-4.5-Flash nor GPT-5.6 Cyber has published throughput data on this page yet. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
How do I switch the models in this GLM-4.5-Flash vs GPT-5.6 Cyber comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open GLM-4.5-Flash or GPT-5.6 Cyber from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
