GPT-4o vs GPT-4o Mini: Pricing, Specs, and Verdict

GPT-4o suits workloads that need OpenAI’s strongest reasoning and native multimodal handling in a single API call, while GPT-4o mini suits workloads that need the lowest possible cost and the highest throughput for simpler tasks. Both models are now API-only decisions rather than a choice inside the ChatGPT app: OpenAI retired GPT-4o and GPT-4o mini from the ChatGPT consumer interface on February 13, 2026, and fully retired GPT-4o from Business, Enterprise, and Edu Custom GPTs on April 3, 2026. Developers building on the OpenAI API can still call both models unchanged, at the same pricing and specifications they shipped with in 2024. For a related comparison, see AI Comparison.

This guide compares GPT-4o and GPT-4o mini across 8 dimensions: specs, features, benchmarks, pricing, reviews, and use cases. Every price, context-window figure, and benchmark score below comes from OpenAI’s own API documentation and launch announcements, not third-party estimates. This comparison identifies which model fits your cost budget, latency requirement, and task complexity.

GPT-4o vs GPT-4o Mini at a Glance

GPT-4o and GPT-4o mini share the same 128,000-token context window and October 2023 knowledge cutoff, but GPT-4o mini costs roughly 16.7 times less per token. The table below summarizes the core differences before the detailed breakdown that follows. Readers comparing OpenAI’s full lineup against every other major provider can see the complete field in the Best AI Models guide.

Category	GPT-4o	GPT-4o Mini
Developer	OpenAI	OpenAI
Released	May 13, 2024	July 18, 2024
Context window	128,000 tokens	128,000 tokens
Max output tokens	16,384 tokens	16,384 tokens
Knowledge cutoff	October 1, 2023	October 1, 2023
Input modalities	Text, image (audio via Realtime API)	Text, image
Input price	$2.50 per 1M tokens	$0.15 per 1M tokens
Output price	$10.00 per 1M tokens	$0.60 per 1M tokens
Tier 1 throughput	500 RPM / 30,000 TPM	500 RPM / 200,000 TPM
ChatGPT app access	Retired (April 3, 2026)	Retired (February 13, 2026)
Best for	Complex reasoning, vision-heavy tasks	High-volume, cost-sensitive workloads

What Is GPT-4o?

GPT-4o is OpenAI’s natively multimodal flagship model, trained end-to-end across text, image, and audio and released on May 13, 2024. The “o” stands for “omni,” reflecting a single neural network that processes multiple input types rather than routing between separate specialized models. GPT-4o originally powered ChatGPT’s default experience and its Advanced Voice Mode before OpenAI moved ChatGPT’s default to the GPT-5 model family in 2025.

OpenAI’s launch benchmarks put GPT-4o at 88.7% on the MMLU knowledge test and above 90% on the HumanEval coding benchmark, both near the top of the field at release. Through the API, GPT-4o accepts text and image input and returns text output, with native audio handled separately through OpenAI’s Realtime API rather than the standard Chat Completions or Responses endpoints. OpenAI briefly deprecated GPT-4o during the GPT-5 rollout in 2025, then restored it after Plus and Pro subscribers said they needed more time to transition and preferred its conversational tone, before retiring it from ChatGPT entirely in early 2026.

What Is GPT-4o Mini?

GPT-4o mini is OpenAI’s compact, cost-efficient model, released on July 18, 2024, to replace GPT-3.5 Turbo as the default low-cost option. It shares GPT-4o’s 128,000-token context window and October 2023 knowledge cutoff but trades some reasoning depth for a dramatically lower price and faster inference. OpenAI’s own launch materials describe it as more than 60% cheaper than GPT-3.5 Turbo while outperforming it on most benchmarks.

At launch, OpenAI reported GPT-4o mini scoring 82% on MMLU, 87.2% on HumanEval, 87.0% on the Multilingual Grade School Math benchmark, and 59.4% on the MMMU multimodal reasoning eval. The model accepts text and image input through the API and returns text output only, with no audio input or output support. Like GPT-4o, GPT-4o mini was retired from the ChatGPT consumer interface on February 13, 2026, and remains available exclusively through the API today.

Feature Comparison: Multimodality, Context Window, and Throughput

GPT-4o and GPT-4o mini match on context window and max output tokens but diverge sharply on throughput and native audio access. Both models handle the same 128,000-token input window, yet 3 practical differences shape which one fits a given integration.

Multimodal Input and Output

GPT-4o processes text and image input through its standard API endpoints and adds native audio input and output through OpenAI’s separate Realtime API, the same audio pipeline that powers ChatGPT’s Advanced Voice Mode. GPT-4o mini processes text and image input only, with no audio modality in either direction. Applications that need voice conversations, live audio transcription, or spoken responses require GPT-4o’s Realtime endpoint; GPT-4o mini cannot serve that use case today.

Context Window and Max Output Tokens

Both models cap input context at 128,000 tokens and maximum output at 16,384 tokens per request, so neither model has a context-window advantage over the other. A workload that needs to summarize a 100-page document or hold a long multi-turn conversation fits within either model’s window equally well. The deciding factor for that kind of task becomes cost and reasoning quality rather than window size.

Rate Limits and Throughput

GPT-4o mini allows dramatically higher token throughput than GPT-4o at every usage tier: OpenAI’s published Tier 1 limits list GPT-4o at 500 requests per minute and 30,000 tokens per minute, against GPT-4o mini’s 500 requests per minute and 200,000 tokens per minute. At Tier 2, GPT-4o climbs to 5,000 RPM and 450,000 TPM, while GPT-4o mini reaches 5,000 RPM and 2,000,000 TPM. Applications that chain or parallelize many model calls, such as batch classification or high-volume customer support, hit GPT-4o’s token ceiling far sooner than GPT-4o mini’s.

Performance Comparison: Benchmarks and Reasoning Quality

GPT-4o outperforms GPT-4o mini on every published benchmark, though the gap is narrower than the 16.7x price difference suggests. The table below compares the 2 benchmarks OpenAI reported for both models at launch.

Benchmark	GPT-4o	GPT-4o Mini
MMLU (general knowledge)	88.7%	82.0%
HumanEval (coding)	~90%+	87.2%

GPT-4o’s advantage widens on tasks that require multi-step reasoning, nuanced language understanding, or creative long-form generation, according to developer reports comparing the 2 models in production. GPT-4o mini still performs competitively on structured tasks like math and code, scoring 87.0% on the MGSM math benchmark and 87.2% on HumanEval, close enough to GPT-4o’s coding performance that many code-formatting and simple-function tasks show little practical difference. Latency compounds the practical performance gap: GPT-4o mini generates responses noticeably faster than GPT-4o, an advantage that matters more than raw accuracy for real-time, high-volume applications.

GPT-4o vs GPT-4o Mini Pricing

GPT-4o mini costs about 16.7 times less than GPT-4o on both input and output tokens. OpenAI’s API pricing page lists the following rates as of August 2026.

Pricing tier	GPT-4o	GPT-4o Mini
Input tokens	$2.50 per 1M	$0.15 per 1M
Cached input tokens	$1.25 per 1M	$0.075 per 1M
Output tokens	$10.00 per 1M	$0.60 per 1M

The cost gap compounds quickly at scale. A workload processing 10 million input tokens and 2 million output tokens in a month costs $45.00 on GPT-4o (10 × $2.50 plus 2 × $10.00) versus $2.70 on GPT-4o mini (10 × $0.15 plus 2 × $0.60), a difference that grows linearly with volume. Neither model carries a separate ChatGPT subscription price anymore, since both are retired from the consumer app; every dollar spent on either model now runs through direct API billing.

Pros and Cons

GPT-4o’s strengths center on reasoning depth and native audio, while GPT-4o mini’s strengths center on cost and throughput. Neither model wins on every dimension, which is why the right choice depends on the specific workload.

GPT-4o Pros and Cons

GPT-4o’s 4 clearest advantages are its stronger performance on complex reasoning and creative tasks, native audio input and output through the Realtime API, higher accuracy on nuanced multimodal analysis, and a proven track record as OpenAI’s flagship model since 2024. Its main drawbacks are pricing that runs 16.7 times higher than GPT-4o mini, a Tier 1 throughput cap of just 30,000 tokens per minute, and full retirement from the ChatGPT consumer app as of 2026.

GPT-4o Mini Pros and Cons

GPT-4o mini’s 4 clearest advantages are its dramatically lower per-token cost, a Tier 1 throughput ceiling nearly 7 times higher than GPT-4o’s, fast response latency suited to real-time applications, and benchmark scores that still beat GPT-3.5 Turbo across most categories. Its main drawbacks are no audio input or output support, measurably lower accuracy on complex reasoning and nuanced writing tasks, and the same 2026 retirement from ChatGPT’s consumer interface as GPT-4o.

User Reviews: What Developers Say

Developers who have used both models describe GPT-4o mini as the default budget workhorse for high-volume tasks and GPT-4o as the model reserved for work that genuinely needs deeper reasoning. Community discussion since GPT-4o mini’s 2024 launch has stayed consistent on this split even as both models moved from ChatGPT into API-only status.

Developers favoring GPT-4o mini cite its fit for chaining or parallelizing multiple model calls, passing large volumes of context through a pipeline, and powering fast, real-time customer-facing text responses without ballooning API costs. Developers favoring GPT-4o cite its stronger performance on ambiguous or multi-step reasoning, image-heavy analysis, and tasks where an incorrect answer is costly enough to justify the higher per-token price. A recurring theme in the ChatGPT retirement discussion was consumer nostalgia for GPT-4o’s conversational tone, with OpenAI itself citing user feedback that some Plus and Pro subscribers preferred its “warmth” over newer models before ultimately retiring it from the app in 2026.

Use Cases: Which Workloads Fit Each Model

GPT-4o mini fits high-volume, well-defined tasks, while GPT-4o fits lower-volume tasks that need deeper judgment. Real-world fit depends more on task complexity and call volume than on any single feature difference.

GPT-4o mini suits 4 common workload types well: content classification and moderation pipelines, data extraction from structured or semi-structured text, high-volume customer support chatbots, and summarization run across large document batches. GPT-4o suits workloads such as multi-step agentic tool use, long-form creative or technical writing, and detailed image or document analysis where accuracy matters more than per-call cost. Teams building voice-based products, such as a live customer support line, need GPT-4o’s Realtime API specifically, since GPT-4o mini has no audio modality at all.

Teams comparing conversational AI products more broadly can see how leading options stack up in the Best AI Chatbot guide.

Final Recommendation: Choose GPT-4o or GPT-4o Mini?

The right pick depends on whether a workload needs maximum reasoning accuracy or maximum cost efficiency at scale. Choose GPT-4o if these 4 factors matter most:

• Complex, multi-step reasoning or ambiguous tasks make accuracy the priority
• Native audio input or output through the Realtime API is required
• Image-heavy or document-heavy analysis needs the highest available accuracy
• Call volume is low enough that the 16.7x price premium stays manageable

Choose GPT-4o mini if these 4 factors matter most:

• Per-token cost is the primary constraint on the project’s budget
• The workload involves chaining or parallelizing a high volume of model calls
• Tasks are well-defined, such as classification, extraction, or summarization
• Higher Tier 1 throughput (200,000 TPM versus 30,000 TPM) is needed to avoid rate-limit errors

Alternatives to GPT-4o and GPT-4o Mini

GPT-4.1, GPT-4.1 mini, and GPT-5 mini are the 3 most relevant successors now that GPT-4o and GPT-4o mini are API-only legacy models. Readers deciding between OpenAI’s flagship and its efficient mid-2024 tier can compare the next model generation directly in the GPT-4o vs GPT-4.1 guide, which covers the model OpenAI positioned as GPT-4o’s direct upgrade.

For a comparison of OpenAI’s current small-model tier rather than its 2024 lineup, the GPT-5 vs GPT-5 Mini guide breaks down the same cost-versus-capability trade-off using OpenAI’s newest models. Readers who want the full lineage from GPT-3.5 through today can start with the GPT-4 vs GPT-3.5 Comparison guide, and the OpenAI Models Guide maps every current and retired OpenAI model in one place.

Frequently Asked Questions

Is GPT-4o still available in ChatGPT?

No, OpenAI retired GPT-4o and GPT-4o mini from the ChatGPT consumer interface on February 13, 2026, and fully removed GPT-4o from Business, Enterprise, and Edu Custom GPTs on April 3, 2026. Both models remain fully available and unchanged through the OpenAI API for developers.

How much cheaper is GPT-4o mini than GPT-4o?

GPT-4o mini costs about 16.7 times less than GPT-4o on both input and output tokens: $0.15 versus $2.50 per 1 million input tokens, and $0.60 versus $10.00 per 1 million output tokens. A workload of 10 million input and 2 million output tokens costs $2.70 on GPT-4o mini compared to $45.00 on GPT-4o.

Does GPT-4o mini have the same context window as GPT-4o?

Yes, both models share a 128,000-token context window and a 16,384-token maximum output limit. Context window size is not a differentiator between the 2 models; cost, throughput, and reasoning accuracy are.

Which model should I use for a high-volume chatbot?

GPT-4o mini fits high-volume chatbots better because its Tier 1 rate limit allows 200,000 tokens per minute, compared to GPT-4o’s 30,000 tokens per minute, alongside a per-token cost roughly 16.7 times lower. GPT-4o remains the better choice only if the chatbot’s queries regularly require complex reasoning that GPT-4o mini handles less reliably.

Can GPT-4o mini process images?

Yes, GPT-4o mini accepts text and image input through the API, though it returns text output only. GPT-4o mini has no audio input or output support at all, unlike GPT-4o, which adds native audio through OpenAI’s separate Realtime API.

What replaced GPT-4o and GPT-4o mini in ChatGPT?

OpenAI moved ChatGPT’s default models to the GPT-5 family starting in 2025, and by 2026 the consumer app runs on GPT-5.5 Instant for free users and GPT-5.5 Thinking for Plus and higher tiers. GPT-4o and GPT-4o mini remain accessible only through direct API calls, not inside the ChatGPT app itself.

Final Verdict

GPT-4o and GPT-4o mini both remain fully functional, well-documented API models even after their 2026 retirement from the ChatGPT consumer app. GPT-4o wins for workloads that need OpenAI’s strongest available reasoning, native audio through the Realtime API, or the highest accuracy on image and document analysis, and its price premium is easiest to justify at lower call volumes. GPT-4o mini wins for cost-sensitive, high-throughput workloads, such as classification pipelines and customer support bots, where its 16.7x lower price and nearly 7 times higher Tier 1 throughput outweigh its more modest reasoning depth.

The practical starting point for an undecided team is measuring actual task accuracy on GPT-4o mini before defaulting to GPT-4o’s higher cost, since OpenAI’s own benchmarks show the mini model still clears 82% on MMLU and above 87% on coding and math evals. The right answer depends on whether a project’s next API call needs maximum reasoning accuracy or maximum cost efficiency at scale, not on which model carries the more recognizable name.