GPT-4 TurbovsGLM-5.3-Flash
GPT-4 Turbo | GLM-5.3-Flash | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 320B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 128k | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $10.00 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $30.00 | — |
| BenchmarksPublished by one model only | ||
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | — | 63.4% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | — | 84.3% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 55.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 42.5% | — |
Agent's Last Exam · pass@1Agentic computer use — A hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | — | 26.3% |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | — | 48.8% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | — | 1773 |
| Overview | ||
| Company | OpenAI | Z.ai |
| Release date | Nov 6 2023 | Aug 26 2026 |
| Access | Proprietary | Open Weight |
Other comparisons
Frequently asked questions
GPT-4 Turbo and GLM-5.3-Flash don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-4 Turbo has a verified first-party API price: $10.00 per million input tokens and $30.00 per million output tokens. No pay-as-you-go API rate is tracked for GLM-5.3-Flash. GPT-4 Turbo shipped 1024 days before GLM-5.3-Flash, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (GPT-4 Turbo) vs 1M (GLM-5.3-Flash). GPT-4 Turbo is proprietary, while GLM-5.3-Flash is open weight.
Direct benchmark comparisons are unavailable — GPT-4 Turbo and GLM-5.3-Flash don't publish scores on any of the same benchmarks.
GPT-4 Turbo was released by OpenAI on Nov 6 2023.
GLM-5.3-Flash was released by Z.ai on Aug 26 2026.
Only GPT-4 Turbo has a verified first-party API price: $10.00 per million input tokens and $30.00 per million output tokens. No pay-as-you-go API rate is tracked for GLM-5.3-Flash. Rates are pay-as-you-go API prices verified on August 18, 2026.
GPT-4 Turbo has a 128k context window; GLM-5.3-Flash has 1M.
GPT-4 Turbo is a proprietary model released by OpenAI. GLM-5.3-Flash is an open weight model released by Z.ai.