GPT-5 vs GPT-4o: Which OpenAI Model Wins?
GPT-5 outperforms GPT-4o on complex reasoning, coding, and math benchmarks, while GPT-4o remains faster and more consistently well-liked for everyday conversation. OpenAI released GPT-5 on August 7, 2025, replacing GPT-4o as ChatGPT’s default model and pulling GPT-4o from the app’s model picker without warning. The move triggered a backlash large enough that OpenAI restored GPT-4o access for paying subscribers within days, before fully retiring it from the ChatGPT consumer app on February 13, 2026. For a related comparison, see AI Comparison.
GPT-4o remains fully available through the OpenAI API today, unchanged since its 2024 launch, alongside GPT-5 and the newer GPT-5.5 and GPT-5.6 model families that now power ChatGPT‘s default experience. This guide compares GPT-5 and GPT-4o across specs, benchmarks, pricing, and real user sentiment to identify which model fits a given task in 2026.
GPT-5 vs GPT-4o at a Glance
GPT-5 and GPT-4o differ most sharply on context window, reasoning depth, and per-token pricing, while both share the same $10.00 output-token price. The table below summarizes the core specs before the detailed breakdown that follows. For a related comparison, see GPT-4o vs GPT-4.
| Category | GPT-5 | GPT-4o |
|---|---|---|
| Developer | OpenAI | OpenAI |
| Released | August 7, 2025 | May 13, 2024 |
| Context window | 400,000 tokens | 128,000 tokens |
| Max output tokens | 128,000 tokens | 16,384 tokens |
| Knowledge cutoff | September 30, 2024 | October 1, 2023 |
| Input modalities | Text, image | Text, image (audio via Realtime API) |
| Output modalities | Text only | Text (audio via Realtime API) |
| Input price | $1.25 per 1M tokens | $2.50 per 1M tokens |
| Output price | $10.00 per 1M tokens | $10.00 per 1M tokens |
| SWE-bench Verified | 74.9% | ~30.8% |
| ChatGPT availability | Superseded by GPT-5.5 / GPT-5.6 | Retired (February 13, 2026) |
| Best for | Complex reasoning, coding, agentic tasks | Fast conversational replies, legacy workloads |
What Is GPT-5?
GPT-5 is OpenAI’s natively multimodal flagship model, trained jointly on text and image data and released on August 7, 2025. It unified OpenAI’s previous separate fast-response and deep-reasoning lines into one system that routes each query to the right internal variant: gpt-5-main and gpt-5-main-mini for fast replies, gpt-5-thinking and gpt-5-thinking-mini for deeper multi-step reasoning. Through the API, developers can call gpt-5-thinking-nano directly and set an explicit reasoning effort of low, medium, high, or minimal, while ChatGPT Pro subscribers get gpt-5-thinking-pro, which uses parallel test-time compute for its most demanding answers.
OpenAI’s own system card puts GPT-5 at 74.9% on SWE-bench Verified and 94.6% on the AIME 2025 math competition without external tools, both state-of-the-art scores at launch. GPT-5 accepts text and image input through the API and returns text-only output, with a 400,000-token context window and a 128,000-token maximum output. GPT-5 replaced GPT-4o as ChatGPT’s default model at launch, then was itself superseded inside the ChatGPT app by later GPT-5.5 and GPT-5.6 releases, though the original GPT-5 endpoint remains available through the OpenAI API today.
What Is GPT-4o?
GPT-4o is OpenAI’s natively multimodal “omni” model, trained end-to-end across text, image, and audio and released on May 13, 2024. It powered ChatGPT’s default experience and Advanced Voice Mode for more than a year before GPT-5 replaced it in August 2025. OpenAI’s launch benchmarks put GPT-4o at 88.7% on MMLU and above 90% on HumanEval, both near the top of the field at release.
GPT-4o accepts text and image input through its standard API endpoints and returns text output, with native audio handled separately through OpenAI’s Realtime API. The model carries a 128,000-token context window, a 16,384-token maximum output, and an October 1, 2023 knowledge cutoff, all unchanged since 2024. OpenAI briefly restored GPT-4o access in ChatGPT after the GPT-5 launch backlash, then retired it from the consumer app entirely on February 13, 2026, leaving the API as its only home today.
Feature Comparison: Reasoning Modes, Multimodality, and Context Window
GPT-5 adds adjustable reasoning effort and a context window more than 3 times larger than GPT-4o’s, while GPT-4o keeps its edge on native audio. Three practical differences shape which model fits a given integration.
Reasoning Effort and Model Variants
GPT-5 lets developers choose a reasoning effort of low, medium, high, or minimal through the API, trading response depth for speed on a per-request basis. GPT-4o offers no equivalent control; it always responds using the same fixed inference path regardless of task complexity. This makes GPT-5 the more flexible choice for pipelines that mix simple lookups with complex multi-step problems in the same application.
Multimodal Input and Output
GPT-4o processes text and image input through its standard endpoints and adds native audio input and output through OpenAI’s separate Realtime API, the same pipeline that powers ChatGPT’s Advanced Voice Mode. GPT-5 processes text and image input but returns text-only output through its primary API, with no equivalent native audio endpoint of its own. Applications that need live voice conversations still route through GPT-4o’s Realtime API rather than GPT-5’s text-only response format.
Context Window and Max Output Tokens
GPT-5’s 400,000-token context window holds more than 3 times as much input as GPT-4o’s 128,000-token window, and its 128,000-token maximum output is nearly 8 times larger than GPT-4o’s 16,384-token cap. A workload that needs to process a 500-page document or maintain a very long multi-turn conversation fits inside GPT-5’s window while overflowing GPT-4o’s. Tasks that stay under roughly 100,000 tokens of context see no practical window advantage from either model.
Performance Comparison: Benchmarks and Real-World Testing
GPT-5 beats GPT-4o on every published reasoning and coding benchmark, though GPT-4o still generates responses noticeably faster. The table below compares the benchmarks both OpenAI and independent testers have published for the 2 models. For a related comparison, see OpenAI Models Guide.
| Benchmark | GPT-5 | GPT-4o |
|---|---|---|
| SWE-bench Verified (coding) | 74.9% | ~30.8% |
| AIME 2025, no tools (math) | 94.6% | Not reported |
| MMLU (general knowledge) | Not reported by OpenAI | 88.7% |
| Output speed (Artificial Analysis) | ~99 tokens/sec | ~140 tokens/sec |
Independent benchmarking firm Artificial Analysis scores GPT-5 at 35 on its Intelligence Index against GPT-4o’s 11, a gap driven mainly by GPT-5’s stronger multi-step reasoning and its extended “thinking” mode. That same reasoning process makes GPT-5 slower to produce a first response: Artificial Analysis measured GPT-5’s average time to first token at roughly 91 seconds in high-reasoning-effort testing, compared with under 1 second for GPT-4o. The practical trade-off is consistent across sources: GPT-5 answers harder questions more reliably, while GPT-4o answers simpler questions faster.
GPT-5 vs GPT-4o Pricing
GPT-5 costs less than GPT-4o on input tokens and the same on output tokens, at OpenAI’s standard API rates. OpenAI’s developer pricing page lists the following rates as of August 2026.
| Pricing tier | GPT-5 | GPT-4o |
|---|---|---|
| Input tokens | $1.25 per 1M | $2.50 per 1M |
| Cached input tokens | $0.125 per 1M | $1.25 per 1M |
| Output tokens | $10.00 per 1M | $10.00 per 1M |
A workload processing 10 million input tokens and 2 million output tokens costs $32.50 on GPT-5 (10 × $1.25 plus 2 × $10.00) versus $45.00 on GPT-4o (10 × $2.50 plus 2 × $10.00), a savings of roughly 28% that comes with GPT-5’s added reasoning latency. GPT-5’s cached-input rate of $0.125 per 1 million tokens is 10 times cheaper than GPT-4o’s $1.25, a meaningful difference for applications that repeatedly send the same system prompt or reference document.
Neither price includes a ChatGPT consumer subscription tier, since GPT-4o now runs exclusively through the API while ChatGPT itself defaults to newer GPT-5.5 and GPT-5.6 models. For a related comparison, see GPT-4o vs GPT-4.1.
Pros and Cons
GPT-5’s strengths center on reasoning depth and coding accuracy, while GPT-4o’s strengths center on speed and conversational warmth. Neither model wins on every dimension, which is why the right choice depends on the specific task. For a related comparison, see GPT-4 vs GPT-3.5 Comparison.
GPT-5 Pros and Cons
GPT-5’s 4 clearest advantages are its stronger performance on complex reasoning and coding tasks, a context window more than 3 times larger than GPT-4o’s, adjustable reasoning effort for balancing speed against depth, and lower per-token input pricing. Its main drawbacks are dramatically higher response latency in high-reasoning-effort mode, no native audio input or output, and a more formal tone that drove significant user backlash at launch.
GPT-4o Pros and Cons
GPT-4o’s 4 clearest advantages are its faster response speed at every request size, native audio input and output through the Realtime API, a warmer conversational tone that many users preferred, and a proven track record as ChatGPT’s default model for more than a year. Its main drawbacks are measurably lower accuracy on complex reasoning and coding benchmarks, a smaller 128,000-token context window, and full retirement from the ChatGPT consumer app as of 2026.
User Reviews: What ChatGPT Users Say
ChatGPT users reacted to GPT-5’s launch with widespread criticism of its tone, even as many acknowledged its stronger reasoning. OpenAI’s decision to pull every previous model, including GPT-4o, from the ChatGPT picker without warning drove much of the initial backlash. Reddit threads in the days after launch filled with users asking how to bring GPT-4o back, with some calling GPT-5 a “disaster” compared to the model it replaced.
The core complaint centered on personality rather than capability: users described GPT-5’s replies as more brief and emotionless next to GPT-4o’s warmer, more lighthearted tone. OpenAI acknowledged the reaction publicly and restored GPT-4o access for Plus subscribers within days of the backlash, before later shipping personality updates aimed at making GPT-5 feel warmer. Independent side-by-side testing by TechRadar found GPT-4o winning 3 of 5 head-to-head categories tested, including emotional support and summarizing, with the reviewer describing GPT-4o as feeling “like your friend” against GPT-5’s “high-school teacher” tone.
Use Cases: Which Workloads Fit Each Model
GPT-5 fits workloads that need deep reasoning or reliable code generation, while GPT-4o fits workloads that need fast, natural conversation or native voice. Real-world fit depends more on task complexity and latency tolerance than any single benchmark score. For a related comparison, see Best AI Voice Generator.
GPT-5 suits 4 common workload types well: multi-step agentic tool use, production code generation and debugging, complex math or financial analysis, and long-document analysis that needs its larger context window. GPT-4o suits real-time customer support chat, voice-based products built on the Realtime API, quick brainstorming and everyday writing, and any application where GPT-5’s added reasoning latency would hurt the user experience. Teams building a live support line specifically need GPT-4o’s Realtime API, since GPT-5 has no equivalent native audio endpoint.
Teams comparing GPT-5 against a smaller, cheaper OpenAI model can see that trade-off directly in the GPT-5 vs GPT-5 Mini guide, while those weighing GPT-4o against its direct 2025 successor can check the GPT-4o vs GPT-4.1 comparison.
Final Recommendation: Choose GPT-5 or GPT-4o?
The right pick depends on whether a task needs maximum reasoning accuracy or maximum response speed. Choose GPT-5 if these 4 factors matter most:
- Complex, multi-step reasoning or coding tasks make accuracy the priority
- A context window larger than 128,000 tokens is needed for long documents
- Adjustable reasoning effort is useful for balancing speed against depth
- Lower per-token input cost matters more than response latency
Choose GPT-4o if these 4 factors matter most:
- Response speed is critical, such as in a live chat interface
- Native audio input or output through the Realtime API is required
- A warmer, more conversational tone matters for the product’s audience
- The workload is simple enough that GPT-5’s added latency brings no benefit
Alternatives to GPT-5 and GPT-4o
GPT-5 Mini, GPT-4.1, and GPT-5.5 are the most relevant alternatives now that GPT-5 and GPT-4o sit at different points in OpenAI’s current lineup. Readers who want a cheaper, faster OpenAI model than GPT-5 can compare the trade-off directly in the GPT-5 vs GPT-5 Mini guide, which breaks down the same cost-versus-capability decision covered here for the mini tier.
Readers deciding between OpenAI’s 2024 flagship and its direct 2025 successor can see how GPT-4o compares to the model OpenAI positioned as its upgrade in the GPT-4o vs GPT-4.1 guide. Teams comparing GPT-5 against rival flagship models can check the GPT-5 vs Claude Opus 4.1 and GPT-5 vs Grok 4 comparisons, 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 from the ChatGPT consumer app on February 13, 2026, and from Business, Enterprise, and Edu Custom GPTs on April 3, 2026. GPT-4o remains fully available and unchanged through the OpenAI API for developers.
Why did OpenAI replace GPT-4o with GPT-5?
OpenAI replaced GPT-4o with GPT-5 on August 7, 2025, to make its stronger reasoning, coding, and math performance the default ChatGPT experience for all users. The switch removed every previous model from the picker at once, including GPT-4o, which triggered enough user backlash that OpenAI restored GPT-4o access for Plus subscribers within days.
Is GPT-5 better than GPT-4o for coding?
Yes, GPT-5 scores 74.9% on SWE-bench Verified compared with roughly 30.8% for GPT-4o, a wide margin on OpenAI’s primary coding benchmark. GPT-5’s advantage grows further on multi-step debugging tasks, where its adjustable reasoning effort lets it work through a problem more thoroughly than GPT-4o’s fixed inference path.
How much does GPT-5 cost compared to GPT-4o?
GPT-5 costs $1.25 per 1 million input tokens versus GPT-4o’s $2.50, while both models charge $10.00 per 1 million output tokens. A workload of 10 million input and 2 million output tokens costs $32.50 on GPT-5 compared to $45.00 on GPT-4o.
Can I still access GPT-4o through the API?
Yes, GPT-4o remains available through the OpenAI API unchanged since its 2024 launch, even though it was retired from the ChatGPT consumer app in 2026. Developers can call GPT-4o directly at $2.50 per 1 million input tokens and $10.00 per 1 million output tokens.
What replaced GPT-5 in ChatGPT?
OpenAI moved ChatGPT’s default models to the GPT-5.5 and GPT-5.6 families after GPT-5’s initial 2025 launch, with GPT-5.5 Instant serving free users and GPT-5.5 Thinking serving Plus and higher tiers by 2026. The original GPT-5 model remains accessible through the OpenAI API at its original pricing and specifications.
Final Verdict
GPT-5 and GPT-4o now serve different purposes inside OpenAI’s lineup: GPT-5 as the higher-accuracy reasoning engine, GPT-4o as the fast, conversational legacy option. GPT-5 wins for coding, math, and multi-step reasoning tasks, backed by a 74.9% SWE-bench Verified score against GPT-4o’s roughly 30.8%, plus a context window more than 3 times larger. GPT-4o wins for latency-sensitive applications and native voice, generating responses in under a second where GPT-5’s reasoning mode can take over a minute.
The practical starting point for an undecided team is testing both models against the actual task at hand, since GPT-5’s reasoning advantage matters far less on simple, well-defined requests than its added latency costs. Teams building on the API today get to choose deliberately between the two, unlike ChatGPT’s consumer app, where GPT-4o is gone and GPT-5 itself has already been superseded by the newer GPT-5.5 and GPT-5.6 families. The right answer depends on whether a project’s next API call needs maximum reasoning depth or maximum response speed, not on which model launched more recently.
