GPT-5.4
OpenAI’s flagship multimodal model for long-context reasoning, coding, and computer-use workflows.
GPT-5.4 is the premium choice for technical teams who need to run long-context reasoning, advanced coding, and computer-use workflows. It stands out when accuracy and workflow depth matter more than raw speed, especially in agentic research and software delivery pipelines.
Why we love it
- Strong long-context positioning with up to 1M tokens in public launch discussion
- Useful fit for coding, research, and browser-task automation in one model
- Aligned with OpenAI ecosystem workflows such as ChatGPT and Codex
Things to know
- Official public pricing for GPT-5.4 itself is still not fully stabilized across materials
- Likely too expensive for lightweight everyday inference
- Best value depends on whether your stack truly needs computer-use depth
About
GPT-5.4 is OpenAI’s top-tier large language model for teams and builders who need deep reasoning, long-context analysis, coding help, and browser-style task execution. It fits research, software, and agent workflows where a 1M-token context window and stronger tool use matter.
GPT-5.4 sits at the top of OpenAI’s current model stack and is positioned for advanced AI agent workflows, code generation, and multimodal problem solving. Public launch coverage and community discussion highlight three notable signals: rollout of GPT-5.4, GPT-5.4 Thinking, and GPT-5.4 Pro; support for up to a 1M-token context window; and strong computer-use performance, including a reported 75.0% result on OSWorld Verified versus 72.4% for humans in circulated launch materials. For engineering teams, that combination makes it relevant for long document analysis, repository-scale coding, and stepwise browser automation. GPT-5.4 uses OpenAI’s API ecosystem and naturally fits stacks already built around ChatGPT, Codex, GitHub, and internal copilots. Pricing is still evolving in public materials, so buyers should verify current API rates directly before production rollout. Compared with smaller fast models, GPT-5.4 is better suited to high-stakes reasoning and extended workflows, but it will likely be more expensive and heavier than lightweight inference options.
Key Features
- ✓Analyze up to 1M-token contexts for research and repositories
- ✓Automate browser-style tasks with stronger computer use
- ✓Generate and refactor code across complex workflows
- ✓Handle multimodal reasoning in one model layer
- ✓Fit OpenAI-native stacks built around ChatGPT and Codex
Product Comparison
| Dimension | GPT-5.4 | Claude Sonnet 4.6 | Gemini 3.1 Pro |
|---|---|---|---|
| Core pain-point scenario | Best default choice for teams that need one model to cover coding, reasoning, tool use, and long-document work without constant model switching | Best fit for agentic coding and developer workflows that involve repo-scale edits, debugging, and multi-step execution | Best fit for very large document or codebase ingestion when teams prioritize context depth and lower analysis cost |
| Killer advantage | 1.05M context ceiling plus strong general-purpose quality makes it the safer flagship when the workload is mixed and hard to predict | Strong coding reliability with a 1M context tier makes it attractive for engineering-heavy pipelines that need sustained code understanding | Aggressive price-to-context ratio makes it compelling for research, bulk analysis, and long-context retrieval workloads |
| Performance and limits | At under 272K input tokens, pricing stays relatively manageable, but long sessions above that threshold become materially more expensive | Works well for long-horizon coding tasks, but once prompts exceed 200K tokens, the cost doubles on input pricing, so careless context packing hurts ROI | Handles long-context analysis efficiently, but it is usually a better pick for document-heavy and Google-centric workflows than for premium computer-use style automation |
| Ecosystem and onboarding | Strong option for teams already standardizing on OpenAI API, Responses API, and Codex-style workflows; onboarding is straightforward for most developers | Best when teams already use Claude, Claude Code, Anthropic API, or Bedrock and want coding-first model behavior with low workflow friction | Most natural for organizations already using Google AI Studio, Gemini API, or Vertex AI, especially when they want tighter Google ecosystem alignment |
| Cost versus ROI | $2.50 per 1M input and $15 per 1M output below the long-context pricing threshold; above 272K, it rises to $5 input and $22.50 output, so ROI is strongest when one premium model replaces several tools | $3 per 1M input and $15 per 1M output up to 200K, then $6 input and $22.50 output above that; worth it when coding accuracy and agent consistency matter more than raw token thrift | $2 per 1M input and $12 per 1M output up to 200K, then $4 input and $18 output beyond that; usually the best ROI for cost-sensitive long-context analysis |
| Best buying signal | Choose it when you want the most balanced premium default for a mixed workload across engineering, knowledge work, and tool-based execution | Choose it when your top priority is shipping code faster with an AI that behaves like a strong engineering copilot across larger repositories | Choose it when you care most about long-context throughput, cost discipline, and large-scale reading tasks |
Frequently Asked Questions
Yes for depth-focused use cases. While GPT-5.2 is cheaper on OpenAI’s public API pricing page, GPT-5.4 is positioned for longer-context reasoning, stronger tool use, and more ambitious computer-use style workflows.
The core advantage is workflow depth. GPT-5.4 combines long-context processing, coding support, and computer-use style task execution, which makes it more useful for repository-scale analysis and multi-step automation than lighter chat models.
Public launch coverage says yes, up to 1M tokens. That level matters for enterprise document review, large codebases, and agent systems that need to carry a lot of state across long task chains.
Yes, especially for teams already using Codex, GitHub, or internal copilots. Its appeal comes from handling long code context, stepwise reasoning, and broader tool-oriented tasks rather than just answering short prompts.
Not fully yet. OpenAI’s official pricing page is the safest source, but early public discussion around GPT-5.4 pricing is still mixed enough that finance and platform teams should validate live rates before launch.
Teams with simple chat, summarization, or low-cost automation needs may not need it. If your workflow does not require long context, advanced coding, or computer use, a smaller model will usually deliver better price-performance.