Take A Glance:
For ChatGPT Users
- GPT-5 is now available to all users directly in ChatGPT.
- Plus users get a higher GPT-5 usage allowance. They can also turn on thought mode.
- Pro users get unlimited GPT-5 access. They can also use GPT‑5 pro.
For API Users
- After real-name verification, your account can access the GPT-5 API.
- The API offers four models: GPT-5, GPT-5-Mini, GPT-5-Nano, and GPT-5-Chat.
- GPT-5 costs less than GPT-4.1.
What is GPT-5?
I have been closely following AI developments, and GPT-5 represents the most significant advancement I have seen in artificial intelligence technology. GPT-5 is OpenAI’s latest-generation large language model, officially released on August 7, 2025, building upon everything we learned from previous models while introducing groundbreaking new capabilities.
What makes GPT-5 truly different from its predecessors is its unified approach to AI tasks. GPT-5 will unify reasoning, multimodal input, and task execution in a single model, removing the need to switch between specialized versions. In my experience testing various AI models, this elimination of friction between different capabilities creates a much more seamless user experience.
The model excels at structured reasoning and multi-step logic in ways that previous versions could not match. It’s designed for advanced, multi-step reasoning and significantly fewer hallucinations compared to earlier models. I have noticed that GPT-5 can handle complex problems that require multiple steps of analysis, making it particularly valuable for business applications, coding tasks, and analytical work.
From my observations, GPT-5 also advances beyond simple text interactions. The model processes text, images, and voice inputs more naturally, potentially including video processing capabilities. This multimodal approach means users can work with different types of content without switching between different AI tools, making workflows more efficient and intuitive.
What is the Release Day of GPT-5?
GPT-5 was officially released on August 7, 2025, following months of anticipation from the AI community. OpenAI announced the release with a social media post stating “GPT-5 is here. Rolling out to everyone starting today”, marking a significant milestone in AI development.
The release followed a strategic rollout plan that I have been tracking. The model became available in ChatGPT Team immediately, with ChatGPT Enterprise and Edu access scheduled for August 14. This staggered approach allows OpenAI to manage system load while ensuring all user tiers eventually gain access to the new capabilities.
Based on my analysis of the timeline, this release came sooner than many industry experts predicted, demonstrating OpenAI’s accelerated development pace in the competitive AI landscape.
Is GPT 5 Free?
Yes, GPT-5 is available for free! OpenAI is making GPT-5 available to everyone, including its free users, starting Thursday (August 7, 2025). Accoding to CNBC and TechCrunch GPT-5 is available to all free users of ChatGPT as their default model. However, users not paying for ChatGPT will only be able to ask a limited number of questions, while only those with a $200-a-month “Pro” subscription get unlimited access to the newly released system.
What are the GPT-5 Models?
Okay, I totally get it! Trying to figure out all these GPT-5 models is super confusing. The naming is a real mess. Here’s a clear breakdown focusing on the API models, just like you suggested:
gpt-5(Main Model):- What it is: This is the flagship model, the most powerful one.
- Knowledge: Its knowledge is up-to-date as of October 1, 2024.
- Use: This is the main model you’d use via the API for demanding tasks.
gpt-5-chat(Equivalent togpt-5for Chat):- What it is: This is essentially the same model as
gpt-5, but specifically named and potentially fine-tuned for use within the ChatGPT interface. - Knowledge: Its knowledge is up-to-date as of September 30, 2024.
- What it is: This is essentially the same model as
gpt-5-mini(Lighter Version):- What it is: A smaller, less powerful (but faster/cheaper) version of the main model. Good for simpler tasks or when speed/cost is a bigger factor than peak performance.
- Knowledge: Its knowledge is up-to-date as of May 31, 2024.
gpt-5-nano(Smallest Version):- What it is: The smallest, fastest, and cheapest model in the GPT-5 API lineup. Designed for very quick responses and low-resource environments (like edge devices). It has the least capability. 258
- Knowledge: Its knowledge is also up-to-date as of May 31, 2024.
So, to simplify:
- For maximum power via API: Use
gpt-5. - For ChatGPT-like interaction via API: Use
gpt-5-chat(it’s the same asgpt-5under the hood for chat). - For a balance of decent performance, speed, and lower cost via API: Use
gpt-5-mini. - For the fastest responses and lowest cost via API, suitable for simple tasks or devices: Use
gpt-5-nano.
Forget the confusing ChatGPT interface or System Card names for now. If you’re working with the API, these four (gpt-5, gpt-5-chat, gpt-5-mini, gpt-5-nano) are the ones that matter. Let me know if you need more details!
Comparison Among GPT-5 Models
Let’s check out the difference among GPT-5 models in terms of Coding, Function Calling, Hallucinations, Instruction Following, Long Text, Intelligence, and Multimodal.
Coding
| Benchmark | GPT-5 (high) | GPT-5 mini (high) | GPT-5 nano (high) | o3 (high) | o4-mini (high) | GPT-4.1 | GPT-4.1 mini | GPT-4.1 nano |
| SWE-Lancer | $112K | $75K | $49K | $86K | $66K | $34K | $31K | $9K |
| SWE-bench Verified | 74.90% | 71.00% | 54.70% | 69.10% | 68.10% | 54.60% | 23.60% | – |
| Aider polyglot | 88.00% | 71.60% | 48.40% | 79.60% | 58.20% | 52.90% | 31.60% | 6.20% |
- GPT-5 (high) leads in all benchmarks with the highest scores
- SWE-Lancer: GPT-5 achieves $112K, significantly outperforming other models
- SWE-bench Verified: GPT-5 reaches 74.9% accuracy
- Aider polyglot: GPT-5 demonstrates 88.0% performance
- The “nano” versions generally show lower performance but are likely more cost-effective
- GPT-5 mini maintains strong performance while presumably offering better efficiency than the full GPT-5 model
Function Calling
| Benchmark | GPT-5 (high) | GPT-5 mini (high) | GPT-5 nano (high) | o3 (high) | o4-mini (high) | GPT-4.1 | GPT-4.1 mini | GPT-4.1 nano |
| Tau²-bench airline | 62.60% | 60.00% | 41.00% | 64.80% | 60.20% | 56.00% | 51.00% | 14.00% |
| Tau²-bench retail | 81.10% | 78.30% | 62.30% | 80.20% | 70.50% | 74.00% | 66.00% | 21.50% |
| Tau²-bench telecom | 96.70% | 74.10% | 35.50% | 58.20% | 40.50% | 34.00% | 44.00% | 12.10% |
- GPT-5 (high) excels particularly in telecom scenarios with 96.7% and strong retail performance at 81.1%
- o3 (high) shows competitive performance in airline scenarios (64.8%)
- The “nano” versions show significant performance drops in function calling tasks
- GPT-5 demonstrates superior function-calling capabilities across diverse industry scenarios
Hallucinations (Lower is Better)
| Benchmark | GPT-5 (high) | GPT-5 mini (high) | GPT-5 nano (high) | o3 (high) | o4-mini (high) | GPT-4.1 | GPT-4.1 mini | GPT-4.1 nano |
| LongFact-Concepts | 1.00% | 0.70% | 1.00% | 5.20% | 3.00% | 0.70% | 1.10% | – |
| LongFact-Objects | 1.20% | 1.30% | 2.80% | 6.80% | 8.90% | 1.10% | 1.80% | – |
| FActScore | 2.80% | 3.50% | 7.30% | 23.50% | 38.70% | 6.70% | 10.90% | – |
- GPT-5 mini achieves the lowest hallucination rate in LongFact-Concepts at just 0.7%
- GPT-5 (high) maintains excellent accuracy with low hallucination rates across all metrics
- o3 and o4-mini show significantly higher hallucination rates, particularly in FActScore (23.5% and 38.7% respectively)
- GPT-4.1 series demonstrates competitive hallucination prevention
- GPT-5 family shows substantial improvement in factual accuracy compared to reasoning-focused models
Instruction Following
| Benchmark | GPT-5 (high) | GPT-5 mini (high) | GPT-5 nano (high) | o3 (high) | o4-mini (high) | GPT-4.1 | GPT-4.1 mini | GPT-4.1 nano |
| Scale multichallenge | 69.60% | 62.30% | 54.90% | 60.40% | 57.50% | 46.20% | 42.20% | 31.10% |
| Internal API | 64.00% | 65.80% | 56.10% | 47.40% | 44.70% | 49.10% | 45.10% | 31.60% |
| COLLIE | 99.00% | 98.50% | 96.90% | 98.40% | 96.10% | 65.80% | 54.60% | 42.50% |
- GPT-5 (high) leads in Scale multichallenge with 69.6%
- GPT-5 mini slightly outperforms the full model in Internal API tasks at 65.8%
- COLLIE benchmark shows exceptional performance across GPT-5 and o3 families (96-99%)
- GPT-5 family demonstrates near-perfect instruction following in COLLIE tasks
- Significant performance gap between GPT-5/o3 generation and GPT-4.1 series in complex instruction following
Long Text
| Benchmark | GPT-5 (high) | GPT-5 mini (high) | GPT-5 nano (high) | o3 (high) | o4-mini (high) | GPT-4.1 | GPT-4.1 mini | GPT-4.1 nano |
| MRCR: 2 needle 128k | 95.20% | 84.30% | 43.20% | 55.00% | 56.40% | 57.20% | 47.20% | 36.60% |
| MRCR: 2 needle 256k | 86.80% | 58.80% | 34.90% | – | – | 56.20% | 45.50% | 22.60% |
| Graphwalks bfs | 78.30% | 73.40% | 64.00% | 77.30% | 62.30% | 61.70% | 61.60% | 25.00% |
| Graphwalks parents | 73.30% | 64.30% | 43.40% | 72.90% | 51.10% | 58.00% | 60.50% | 9.40% |
| BrowseComp 128k | 90.00% | 89.40% | 80.40% | 88.30% | 85.00% | 85.90% | 89.00% | 89.40% |
| BrowseComp 256k | 88.80% | 86.00% | 68.40% | – | 75.50% | 81.60% | 81.60% | 19.10% |
| VideoMME | 86.70% | 78.50% | 65.70% | 84.90% | 79.50% | 78.70% | 68.40% | 55.20% |
- GPT-5 (high) consistently leads across most benchmarks, especially in MRCR: 2 needle 128k with a dominant 95.2% score.
- GPT-5 mini shows strong competitive performance, especially in BrowseComp tasks, coming very close to the full model.
- GPT-5 nano underperforms significantly in complex tasks (e.g., Graphwalks parents: 43.4%, MRCR: 2 needle 256k: 34.9%), showing the limitations of smaller models.
- o3 (high) is a standout performer in Graphwalks tasks, matching or outperforming GPT-5 in some areas.
- GPT-4.1 and its variants generally lag behind GPT-5 and o3, especially in complex reasoning or navigation tasks:
- In Graphwalks parents, GPT-4.1 nano drops to just 9.4%.
- MRCR: 2 needle 256k shows a major drop from GPT-5 (86.8%) to GPT-4.1 nano (22.6%).
- BrowseComp 128k is an outlier where almost all models perform well (>85%), showing it’s a relatively accessible benchmark.
- VideoMME shows strong performance from GPT-5 and o3, reinforcing their capability in multimodal or memory-intensive tasks.
Intelligence
| Benchmark | GPT-5 (high) | GPT-5 mini (high) | GPT-5 nano (high) | o3 (high) | o4-mini (high) | GPT-4.1 | GPT-4.1 mini | GPT-4.1 nano |
| AIME ’25 | 94.60% | 91.10% | 85.20% | 86.40% | 92.70% | 46.40% | 40.20% | – |
| FrontierMath | 26.30% | 22.10% | 9.60% | 15.80% | 15.40% | – | – | – |
| GPQA diamond | 85.70% | 82.30% | 71.20% | 83.30% | 81.40% | 66.30% | 65.00% | 50.30% |
| HLE | 24.80% | 16.70% | 8.70% | 20.20% | 14.70% | 5.40% | 3.70% | – |
| HMMT 2025 | 93.30% | 87.80% | 75.60% | 81.70% | 85.00% | 28.90% | 35.00% | – |
- GPT-5 (high) clearly dominates across all intelligence and math-heavy benchmarks, scoring:
- 94.6% on AIME ’25
- 93.3% on HMMT 2025
- 85.7% on GPQA diamond
- GPT-5 mini follows closely, especially in AIME ’25 and HMMT 2025, suggesting it’s highly capable for competitive mathematics with much smaller size.
- GPT-5 nano outperforms the GPT-4.1 series consistently—even reaching 71.2% in GPQA diamond and 85.2% in AIME ’25—showing the nano tier of GPT-5 is more advanced than the full GPT-4.1 in several areas.
- o3 (high) performs exceptionally well in GPQA diamond (83.3%) and AIME ’25 (86.4%), indicating strong mathematical reasoning capabilities.
- o4-mini also delivers impressive results, especially in AIME ’25 (92.7%) and HMMT 2025 (85.0%), showing it’s a strong smaller-scale model.
- FrontierMath and HLE benchmarks reveal a steep drop-off in performance, even among high-end models, suggesting these are very challenging reasoning tasks.
- GPT-5 scores just 26.3% and 24.8% respectively, highlighting the difficulty.
- GPT-4.1 and variants trail significantly behind newer models:
- On AIME ’25, full GPT-4.1 only achieves 46.4%, while GPT-5 scores over 94%.
- Performance is especially low on HLE, with GPT-4.1 mini at just 3.7%.
- GPT-4.1 nano is generally missing or low-performing (e.g., 50.3% in GPQA diamond), reinforcing its limitations on high-reasoning tasks.
Multimodal
| Benchmark | GPT-5 (high) | GPT-5 mini (high) | GPT-5 nano (high) | o3 (high) | o4-mini (high) | GPT-4.1 | GPT-4.1 mini | GPT-4.1 nano |
| MMMU | 84.20% | 81.60% | 75.60% | 82.90% | 81.60% | 74.80% | 72.70% | 55.40% |
| MMMU-Pro | 78.40% | 74.10% | 62.60% | 76.40% | 73.40% | 60.30% | 58.90% | 33.00% |
| CharXiv reasoning | 81.10% | 75.50% | 62.70% | 78.60% | 72.00% | 56.70% | 56.80% | 40.50% |
| VideoMMMU | 84.60% | 82.50% | 66.80% | 83.30% | 79.40% | 60.90% | 55.10% | 30.20% |
| ERQA | 65.70% | 62.90% | 50.10% | 64.00% | 56.50% | 44.30% | 42.30% | 26.50% |
- GPT-5 (high) consistently leads or closely follows top scores across all multimodal tasks:
- Tops VideoMMMU with 84.6%
- Excels in MMMU (84.2%), CharXiv reasoning (81.1%), and MMMU-Pro (78.4%)
- GPT-5 mini is very close in performance, showing strong efficiency for a smaller model (e.g., 82.5% on VideoMMMU).
- GPT-5 nano remains respectable, though there’s a clear performance drop — e.g., 66.8% on VideoMMMU and 62.6% on MMMU-Pro.
- o3 (high) holds up extremely well, showing:
- 83.3% on VideoMMMU
- 82.9% on MMMU
- 78.6% on CharXiv reasoning
- o4-mini offers solid mid-tier performance, nearly matching GPT-5 mini across the board, and outperforming GPT-4.1.
- GPT-4.1 and its variants consistently trail behind:
- Often 15–25% lower than GPT-5 high (e.g., ERQA: 65.7% vs. 44.3%)
- GPT-4.1 nano struggles heavily in complex multimodal reasoning (e.g., 26.5% on ERQA)
- ERQA is the most challenging benchmark across all models, highlighting a persistent gap in fine-grained visual or retrieval reasoning.
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