GPT-4 Turbo
GPT-4 Turbo is OpenAI's high-capability flagship model featuring a 128K context window, trained on data up to April 2024. It delivers strong reasoning, coding, and instruction-following across complex tasks.
The most capable model OpenAI offers, but the steep output cost means it's only justifiable for genuinely high-stakes, complex tasks.
Demanding professional workflows requiring deep reasoning, nuanced writing, and sophisticated multi-step problem solving where cost is secondary to quality.
You're running high-volume, cost-sensitive, or latency-critical applications where GPT-4o or a flash-tier model would deliver 90% of the quality at 10% of the cost.
Compare every model's knowledge cutoff, max output, and context window.
Output cost of $120/1M tokens is exceptionally high and will compound quickly in agentic or multi-turn workflows. Budget carefully. Context window of 400K is generous but falls short of Gemini 3.1 Pro's 1M+ offering for ultra-long document tasks.
State-of-the-art reasoning and instruction-following surpassing GPT-4o by a significant margin
400K context window enables analysis of full codebases, legal documents, or lengthy research corpora
Exceptional at complex multi-step coding tasks including debugging, architecture design, and code review
Superior nuanced writing with consistent voice, tone control, and factual grounding compared to Claude Sonnet 4.6
At $120/1M output tokens, it is among the most expensive models available — roughly 10x the cost of GPT-4o Mini and far pricier than Gemini 3.1 Pro
No native image generation capability despite multimodal input support
Deliberate inference speed makes it unsuitable for real-time or high-volume interactive applications
What people actually use GPT-5 Pro for.
Auditing and refactoring a 50,000-line legacy codebase for security vulnerabilities and architectural improvements
Drafting a high-stakes legal brief or regulatory submission requiring precise language and comprehensive source synthesis
Conducting deep competitive research by analyzing hundreds of pages of financial filings, patents, and technical documentation in a single context
The nearest models people weigh against it, and what actually separates them.
vs GPT-4 Turbo — Against GPT-4 Turbo (OpenAI), GPT-5 Pro costs about 70% more per token, takes 3.1x the context and answers slower. GPT-4 Turbo is the one to check first if the price difference matters more than the ceiling.
vs GPT-4 Turbo (older v1106) — Against GPT-4 Turbo (older v1106) (OpenAI), GPT-5 Pro costs about 70% more per token, takes 3.1x the context and answers slower. GPT-4 Turbo (older v1106) is the one to check first if the price difference matters more than the ceiling.
vs GPT-4 Turbo Preview — Against GPT-4 Turbo Preview (OpenAI), GPT-5 Pro costs about 70% more per token, takes 3.1x the context and answers slower. GPT-4 Turbo Preview is the one to check first if the price difference matters more than the ceiling.
Price History
→0% since May 30
90 data points · tracked daily since May 30, 2026
Demanding professional workflows requiring deep reasoning, nuanced writing, and sophisticated multi-step problem solving where cost is secondary to quality.. Start free — no card required.
Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
Similar models worth checking before you commit.
GPT-4 Turbo is OpenAI's high-capability flagship model featuring a 128K context window, trained on data up to April 2024. It delivers strong reasoning, coding, and instruction-following across complex tasks.
GPT-4 Turbo (v1106) is an older snapshot of OpenAI's flagship GPT-4 Turbo model released in November 2023, offering a 128K context window with strong general-purpose reasoning and instruction-following capabilities. It predates later GPT-4 Turbo updates and GPT-4o, making it a legacy choice for workflows locked to this specific version.
GPT-4 Turbo Preview is an early access version of GPT-4 Turbo, OpenAI's then-flagship model featuring a 128K context window and knowledge improvements over the original GPT-4. It was designed to deliver GPT-4-class reasoning at reduced cost compared to the original GPT-4.
Pricing moves, ranking shifts, and capability updates.
OpenAI: GPT-5 Pro input pricing changed from $15.00/1M to $7.50/1M (↓ cheaper, 50% cut).
View modelOpenAI: GPT-5 Pro output pricing changed from $120.00/1M to $60.00/1M (↓ cheaper, 50% cut).
View modelOpenAI: GPT-5 Pro (OpenAI) is now indexed. It supersedes GPT-4o. The most capable model OpenAI offers, but the steep output cost means it's only justifiable for genuinely high-stakes, complex tasks.
View modelGPT-5 Pro costs $15 per million input tokens and $120 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $390.00 at list price, before any batch or caching discounts.
GPT-5 Pro has a 400k tokens context window, with up to 272k tokens of output per response. That is the total of prompt plus response the model can hold in one request.
GPT-5 Pro's training data runs through September 30, 2024, and the model was released on October 6, 2025. For anything after that date it needs web search or documents in the prompt.
GPT-5 Pro is best for demanding professional workflows requiring deep reasoning, nuanced writing, and sophisticated multi-step problem solving where cost is secondary to quality.. It is a strong fit when that workflow matters more than the tradeoffs around premium pricing and deliberate speed.
You're running high-volume, cost-sensitive, or latency-critical applications where GPT-4o or a flash-tier model would deliver 90% of the quality at 10% of the cost.
GPT-4 Turbo (OpenAI) at $10.00/1M/1M input against GPT-5 Pro's $15.00/1M/1M — roughly 70% less per token all in. A capable but aging flagship that has been outpaced by cheaper, faster successors in OpenAI's own lineup. Compare it first if GPT-5 Pro's pricing is the thing stopping you.
GPT-4 Turbo (older v1106) — balanced against GPT-5 Pro's deliberate, with 128k tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
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