GPT-4 changed the standard for AI assistants, while newer models focus on reasoning, longer context, and completing complex tasks.
AI agents can now use tools, write code, and handle multi-step workflows with less human intervention.
Lower-cost and open-weight models have widened access, but reliability, safety, and human oversight remain critical.
GPT-4 could answer questions, write text and look at information. Newer models can think through hard problems, read much more at once and use outside tools. They can also finish tasks that take many steps. Since GPT-4 arrived in 2023, language models have gone beyond chat. They now work with documents, code, data, and apps, and they need less help from people.
OpenAI built GPT-4 to accept text and image inputs and return text. Image input began as a research preview. Most people used it as a smart assistant for drafting, summaries, and questions. It was powerful yet expensive. The table later in this piece compares its limits with those of current frontier models.
OpenAI released O1 in September 2024. The model was trained to think before it replied. It spent extra computing time on a problem, then gave its answer. Other labs soon added similar modes.
DeepSeek released R1 in January 2025 as an open-weight reasoning model. The lesson was clear. Reasoning models show that better answers can come from more thinking time, not only from larger training runs.
The context window is the amount of text a model can read at once. Google showed one million tokens with Gemini 1.5 Pro in early 2024. OpenAI's GPT-5.6 launch reported long-context tests that run up to the same length. A model can now read a long report or a large codebase in one pass. GPT-4o, released in May 2024, handled text, audio, and images in a single model.
The biggest change is agency. Models now plan steps, use tools, write and test code, and complete long tasks with little supervision. Anthropic launched Claude Fable 5 in June 2026. It sits in a tier above Opus. Anthropic says Fable 5 can work autonomously for longer than any earlier Claude model.
OpenAI released GPT-6 Astra in September 2026. It targets computer use, software engineering, and long-horizon agent work. Success for AI agents is now measured by finished work rather than chat quality.
| Area | GPT-4 (2023) | Frontier Models (2026) |
|---|---|---|
| Context window | 8,000 to 32,000 tokens | Hundreds of thousands, up to about 1 million |
| Modalities | Text, with image input in preview | Text, images, and audio |
| Reasoning | Single-pass answers | Adjustable reasoning effort |
| Typical use | Chat and drafting | Coding and multi-step tasks |
| Flagship price per million tokens | USD 30 input, USD 60 output | USD 10 input, USD 50 output |
Flagship prices fell only modestly. Anthropic priced Fable 5 the same as GPT-6 Astra. Both sit below the original GPT-4 launch price. Output costs, however, dropped little. The sharper fall came one tier down.
OpenAI says GPT-6.1 Sol nearly matches Astra on coding and computer use at one-fifth of the price, with USD 2 for input and USD 10 for output. Model choice is now a routing decision. Teams can send hard problems to a flagship and routine work to a cheaper tier.
Early benchmarks looked like exams. Newer tests look like jobs. OpenAI's GPT-5.6 launch cited Agents' Last Exam, an outside test of long-running professional workflows across 55 fields. A high score on a short quiz says little about how a model handles a week of messy, real work.
Open-weight models changed who can build with AI. DeepSeek, Qwen, Llama, Mistral and OpenAI's GPT-OSS give developers options beyond hosted APIs. Licenses and hardware needs differ across them. Firms with privacy, cost, or deployment needs can run a model under their own control.
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Hallucinations remain. Models still state wrong facts with confidence. Benchmark scores do not guarantee reliability. Safety limits have also become part of every launch. Anthropic released Fable 5 with classifiers that send some cybersecurity and biology requests to Opus 4.8.
OpenAI first gave GPT-5.6 to selected partners after the U.S. government asked it to hold back a wider launch. News reports say OpenAI later cancelled a planned GPT-6.1 Astra release after safety tests.
LLM advancements since GPT-4 point in a clear direction. The gap between tiers will keep narrowing. Cheaper models will take over tasks that once needed a flagship. Flagships will move toward longer and riskier work. Open-weight models will keep pressure on prices. Labs will compete on how much unsupervised work a model can handle.
Buyers will compare vendors on shipped results, such as completed projects, fewer errors, and lower costs per task. That test will matter more than any parameter count or public leaderboard rank.
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Smart AI is easy to find now. Trustworthy AI is harder. An agent can work for hours, but that means little if you can't count on its results. Treat it like a new hire. Give it limited access at first. Set clear rules.
Keep a record of what it does. Have a person check the work before anyone uses it. As the agent proves itself, give it more to do. The teams that gain the most from AI will be the ones who can show their work is reliable.
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1. How is GPT-4 different from today's frontier models?
GPT-4 gave single-pass answers in chat, with a context window of 8,000 to 32,000 tokens. Current models reason before replying, read up to about one million tokens, handle text, images and audio, and complete multi-step tasks like coding.
2. What are reasoning models, and why do they matter?
Reasoning models spend extra computing time thinking before they answer. OpenAI's O1 and DeepSeek's R1 showed that better results can come from more thinking time, not only from bigger training runs.
3. Have AI model prices really dropped since GPT-4?
Only partly. Flagship prices fell modestly, from USD 30 input and USD 60 output to USD 10 input and USD 50 output per million tokens. The sharper drop came in cheaper tiers, such as GPT-6.1 Sol at USD 2 input and USD 10 output.
4. What does 'agency' mean in AI models?
It means a model can plan steps, use tools, write and test code, and finish long tasks with little supervision. Success is now judged by completed work, not by how well the model chats.
5. Do today's models still make mistakes?
Yes. Hallucinations remain, and models can state wrong facts with confidence. High benchmark scores do not guarantee reliability, so teams should set limits, keep records and have a person review agent work.