GPT-4 vs GPT-3.5: Differences, Pricing & 2026 Verdict

GPT-4 is OpenAI’s more capable but more expensive model, while GPT-3.5 is the faster, cheaper model that first powered ChatGPT — and both are now legacy systems that OpenAI is retiring from its API on October 23, 2026. GPT-4 launched on March 14, 2023, with a 8,192-token context window, a December 2023 knowledge cutoff, and image-understanding support in later snapshots. GPT-3.5 launched on November 30, 2022, as the original ChatGPT model, runs on a 16,385-token context window in its Turbo form, and stays text-only. Neither model has been selectable inside the ChatGPT app for years — GPT-3.5 was dropped from the consumer product in 2024 and GPT-4 followed on April 30, 2025 — so this comparison matters mainly to developers still calling these models through the API, teams auditing legacy integrations, and anyone researching how OpenAI’s model lineup evolved. This guide covers the concrete differences in features, benchmark performance, pricing, and what to migrate to before the October shutdown. For a related comparison, see AI Comparison.

Quick Comparison Table

GPT-4 outperforms GPT-3.5 on reasoning, accuracy, and multimodal input, while GPT-3.5 remains cheaper and faster for simple text tasks. The table below lines up the specifications that matter most for a migration or legacy-support decision.

Specification	GPT-4	GPT-3.5 (Turbo)
Developer	OpenAI	OpenAI
Release date	March 14, 2023	November 30, 2022
Context window	8,192 tokens	16,385 tokens
Knowledge cutoff	December 2023	September 2021
Multimodal input	Text and images	Text only
API input price	$30 per 1M tokens	$0.50 per 1M tokens
API output price	$60 per 1M tokens	$1.50 per 1M tokens
Available in ChatGPT app	No (retired April 30, 2025)	No (retired in 2024)
Available via API	Yes, until October 23, 2026	Yes, until October 23, 2026
OpenAI-recommended replacement	GPT-4o or a current GPT-5 model	GPT-4o mini or a GPT-5 mini/nano model

What Is GPT-4?

GPT-4 is OpenAI’s fourth-generation large language model, released March 14, 2023, built for tasks that require deeper reasoning and higher factual accuracy than GPT-3.5. OpenAI reported that GPT-4 is 82% less likely to respond to requests for disallowed content and 40% more likely to produce factual responses than GPT-3.5, based on OpenAI’s internal evaluations at launch. GPT-4 supports an 8,192-token context window in its base API version (with larger-context variants such as GPT-4 Turbo released later), and its vision-enabled snapshots can accept an image alongside text and describe or reason about its contents. GPT-4 originally powered ChatGPT Plus as a premium, subscription-gated model before OpenAI retired it from the ChatGPT app on April 30, 2025 in favor of GPT-4o and later GPT-5 models — see GPT-4o vs GPT-4.1 for how that current lineup compares. Today, GPT-4 exists only as a legacy OpenAI API model, with a knowledge cutoff of December 2023, priced at $30 per 1M input tokens and $60 per 1M output tokens.

What Is GPT-3.5?

GPT-3.5 is the OpenAI model that launched ChatGPT on November 30, 2022, optimized for fast, low-cost conversational responses rather than deep reasoning. Its Turbo variant carries a 16,385-token context window — larger than GPT-4’s base 8,192-token window — but a September 2021 knowledge cutoff, meaning it has no awareness of events, releases, or products after that date. GPT-3.5 is text-only: it cannot process images, audio, or file attachments the way GPT-4’s multimodal snapshots can. OpenAI priced GPT-3.5 Turbo at $0.50 per 1M input tokens and $1.50 per 1M output tokens, a fraction of GPT-4’s rate, which made it the default engine for high-volume chatbots, customer-support automation, and simple text classification. ChatGPT dropped GPT-3.5 as a selectable model in 2024 after GPT-4o’s release, and OpenAI now recommends GPT-4o mini or a GPT-5 mini/nano model as its direct successor for cost-sensitive workloads.

Feature Comparison

GPT-4 and GPT-3.5 diverge on four core features: context window, multimodal input, reasoning depth, and coding reliability. Each affects a different class of use case, from long-document analysis to real-time chat support.

Context Window and Memory

GPT-3.5 Turbo’s 16,385-token context window is actually larger than GPT-4’s base 8,192-token window, which surprises developers who assume the newer model always wins on every spec. A larger context window lets a model track more conversation history or reference material in a single request — useful for tasks such as summarizing long support transcripts or multi-turn troubleshooting sessions. OpenAI later shipped GPT-4 Turbo with a 128,000-token window specifically to close this gap, but the original GPT-4 API model that most legacy integrations still call keeps the smaller 8,192-token limit.

Multimodal Capabilities

GPT-4’s vision-enabled snapshots accept images as input, while GPT-3.5 processes text only. GPT-4 can describe a photo, read text embedded in a screenshot, or reason about a chart when an image is attached to a request — capabilities GPT-3.5 has no equivalent for at any price point. Teams building tools that need document scanning, receipt parsing, or visual quality checks, examples include invoice-processing bots and accessibility alt-text generators, need GPT-4 or a newer multimodal model like GPT-4o rather than GPT-3.5.

Reasoning and Accuracy

GPT-4 handles multi-step reasoning and nuanced instructions with materially higher accuracy than GPT-3.5. OpenAI’s own launch benchmarks showed GPT-4 producing more factual responses and refusing more disallowed requests than GPT-3.5 under the same evaluation criteria. In practice, this shows up on tasks that require holding several constraints in mind at once, examples include multi-clause legal summaries, layered math word problems, and instructions with conditional logic (“do X unless Y, then do Z”).

Coding Ability

GPT-4 generates and debugs code more reliably than GPT-3.5 across common languages such as Python, JavaScript, and SQL. GPT-3.5 can produce working boilerplate and answer straightforward syntax questions, but it more frequently hallucinates library methods or drops edge-case handling on multi-function tasks. GPT-4’s stronger grasp of context and logic carries over directly to code review, refactoring suggestions, and explaining unfamiliar codebases.

Performance Comparison

GPT-4 outperforms GPT-3.5 on accuracy and complex-task benchmarks, while GPT-3.5 responds faster and at lower latency. The trade-off is consistent across nearly every published evaluation: better answers cost more time and money.

Benchmark Performance

GPT-4 scores higher than GPT-3.5 on the standardized exams and reasoning benchmarks OpenAI published at launch, including bar-exam-style and academic test simulations where GPT-4 placed in a materially higher percentile than GPT-3.5. The gap is widest on tasks requiring implicit reasoning — problems that don’t spell out every step — where GPT-3.5 more often produces a plausible-sounding but incorrect chain of logic.

Speed and Latency

GPT-3.5 returns responses faster than GPT-4 because its smaller, less complex architecture requires less computation per token. For latency-sensitive products, examples include live chat widgets and voice-assistant backends, that speed advantage often outweighs GPT-4’s accuracy gains, particularly for short, low-ambiguity queries where GPT-3.5’s answer quality is already sufficient.

Pricing

GPT-4 costs 60 times more than GPT-3.5 Turbo on input tokens and 40 times more on output tokens through the OpenAI API. Both models bill per token, split between input tokens (the prompt sent) and output tokens (the response generated).

Model	Input (per 1M tokens)	Output (per 1M tokens)	Context window
GPT-4	$30.00	$60.00	8,192 tokens
GPT-3.5 Turbo	$0.50	$1.50	16,385 tokens

Neither model is billed inside a consumer ChatGPT subscription anymore, since both were removed from the ChatGPT app’s model picker; the pricing above applies to direct API usage only. A high-volume, simple-classification workload, examples include support-ticket tagging and spam filtering, stays economical on GPT-3.5 Turbo, while a low-volume workload that depends on getting a complex answer right the first time, examples include contract-clause extraction and multi-step financial analysis, justifies GPT-4’s higher per-token cost.

Pros and Cons

GPT-4 trades speed and cost for accuracy, while GPT-3.5 trades accuracy for speed and cost — the two models sit at opposite ends of the same trade-off.

GPT-4 Pros and Cons

7 factors define GPT-4’s trade-off profile:

• Pro: Higher accuracy on complex, multi-step reasoning tasks.
• Pro: Multimodal input support for images in vision-enabled snapshots.
• Pro: Lower hallucination rate than GPT-3.5 on factual questions.
• Pro: Stronger, more reliable code generation and debugging.
• Con: Costs 60 times more per input token than GPT-3.5 Turbo.
• Con: Slower response times due to higher computational overhead.
• Con: Smaller 8,192-token context window than GPT-3.5 Turbo’s 16,385 tokens.

GPT-3.5 Pros and Cons

6 factors define GPT-3.5’s trade-off profile:

• Pro: Input and output pricing a fraction of GPT-4’s per-token rate.
• Pro: Faster response generation, well suited to real-time chat.
• Pro: Larger 16,385-token context window in its Turbo form.
• Con: September 2021 knowledge cutoff with no image or file input.
• Con: More frequent hallucinations on nuanced or multi-step prompts.
• Con: No longer selectable inside the ChatGPT consumer app.

User Reviews

Developers who compare the two models consistently describe GPT-4 as the better choice for correctness and GPT-3.5 as the better choice for cost and speed. Community discussion on developer forums and comparison write-ups converges on a similar pattern: GPT-3.5 remains adequate for simple questions, casual conversation, and quick drafts, while GPT-4 is the go-to choice when a task needs the best available reasoning, nuanced context understanding, or reliability under ambiguity. The most common complaint about GPT-3.5 in this feedback is inconsistent handling of multi-part instructions, where it answers one clause of a request and silently drops another. The most common complaint about GPT-4, even before its retirement from ChatGPT, was response latency under sustained, high-volume use — a direct consequence of the additional computation its architecture requires per response.

Use Cases

GPT-4 fits accuracy-critical, low-volume tasks, while GPT-3.5 fits high-volume, latency-sensitive tasks. Matching the model to the workload is the single biggest lever on both cost and output quality.

When to Choose GPT-4

Choose GPT-4 for:

• Legal, financial, or medical-adjacent text that requires high factual accuracy.
• Multi-step coding tasks, examples include refactoring a module or debugging a race condition.
• Documents that combine text and images, examples include scanned forms or annotated diagrams.
• Long-form writing that tracks nuanced instructions across a full draft.

When to Choose GPT-3.5

Choose GPT-3.5 for:

• High-volume support-ticket triage or FAQ-style chatbot responses.
• Simple text classification, examples include spam detection and sentiment tagging.
• Draft generation where a human reviews and edits the output afterward.
• Latency-sensitive applications, examples include live chat widgets and voice interfaces.

Final Recommendation

Choose GPT-4 for accuracy-critical or multimodal work, and choose GPT-3.5 Turbo for high-volume, cost-sensitive text tasks — while planning a migration off both before October 23, 2026.

Choose GPT-4 if:
– The task involves multi-step reasoning, legal or financial accuracy, or image understanding.
– Response cost matters less than getting the answer right on the first attempt.
– The workload volume is low enough that a 60x per-token price premium stays affordable.

Choose GPT-3.5 if:
– The task is simple classification, casual conversation, or a quick draft.
– Response latency and per-token cost are the primary constraints.
– The workload runs at high volume, where GPT-4’s pricing would be difficult to justify.

Both paths lead to the same next step: since OpenAI retires gpt-4 and gpt-3.5-turbo from the API on October 23, 2026, any production system still calling either model needs a migration plan to a current model — GPT-5 vs GPT-5 Mini covers how OpenAI’s current entry-tier lineup compares, for teams deciding where to land before that date.

Alternatives

GPT-4o, the GPT-5 model family, Claude, and Gemini are the main alternatives to GPT-4 and GPT-3.5 today. GPT-4o replaced both older models inside ChatGPT and combines multimodal input with lower latency than original GPT-4, and OpenAI’s GPT-5 family extends that lineup further. Outside OpenAI, Claude vs ChatGPT and Gemini vs ChatGPT compare the two other frontier providers most commonly evaluated alongside OpenAI’s models. For a side-by-side view across every major provider rather than just OpenAI’s own history, see our Best AI Chatbot and Best AI Models guides.

FAQ

Is GPT-4 Still Available in ChatGPT?

No, GPT-4 is not available in the ChatGPT app — OpenAI retired it from the consumer product on April 30, 2025. It remains accessible only through the OpenAI API, and only until its scheduled shutdown on October 23, 2026.

Is GPT-3.5 Free to Use?

GPT-3.5 is not free through the OpenAI API, where it bills at $0.50 per 1M input tokens and $1.50 per 1M output tokens. It was the free, default model in ChatGPT at launch in November 2022, but ChatGPT has since replaced it with GPT-4o and GPT-5-family models for both free and paid tiers.

How Much Better Is GPT-4 Than GPT-3.5?

GPT-4 outperforms GPT-3.5 on nearly every OpenAI benchmark from launch, most notably on tasks requiring implicit, multi-step reasoning. OpenAI reported GPT-4 as 82% less likely to produce disallowed content and 40% more likely to produce factual answers than GPT-3.5, though GPT-3.5 remains faster and far cheaper per token.

When Do GPT-4 and GPT-3.5 Shut Down in the API?

OpenAI retires gpt-4, gpt-4-0613, gpt-3.5-turbo, and their fine-tuned variants from the API on October 23, 2026. The gpt-3.5-turbo-instruct variant shuts down slightly earlier, on September 28, 2026. Any integration still calling these model IDs must migrate before those dates.

What Should I Use Instead of GPT-3.5?

OpenAI recommends GPT-4o mini or a GPT-5 mini/nano-tier model as GPT-3.5’s direct replacement, since both cost less per token than GPT-3.5 Turbo while adding vision support and a substantially larger context window. The OpenAI Models Guide breaks down which current model fits which budget.

Can I Still Access GPT-4 Through the API?

Yes, GPT-4 remains available through the OpenAI API as of this writing, though it is scheduled for shutdown on October 23, 2026. OpenAI recommends migrating to GPT-4o or a current GPT-5-family model, both of which outperform original GPT-4 at a lower price per token.

Final Verdict

GPT-4 wins on accuracy, reasoning, and multimodal capability; GPT-3.5 wins on speed and cost — but both are legacy models on a fixed retirement date. Neither is selectable inside ChatGPT anymore, and OpenAI’s October 23, 2026 API shutdown means the practical choice for new projects isn’t GPT-4 versus GPT-3.5 at all — it’s which current-generation replacement, such as GPT-4o, GPT-4o mini, or a GPT-5-family model, best fits the workload these two originally defined. Teams maintaining legacy integrations can treat this comparison as a migration reference: match GPT-4’s use cases to a current reasoning-capable model, and match GPT-3.5’s use cases to a current low-cost model, before October 2026.