OpenAI GPT-5.4 完全ガイド: ベンチマーク、ユースケース、価格、API、比較

OpenAI GPT-5.4 Complete Guide: Benchmarks, Use Cases, Pricing, API, and GPT-5.4 Pro Comparison - DEV Community Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Report Abuse OpenAI released GPT-5.4 on March 5, 2026 , and this is the first GPT release in a while that feels less like a narrow benchmark bump and more like a model-line reset. The reason is simple: GPT-5.4 is the first mainline OpenAI reasoning model that combines frontier professional-work quality, frontier coding from GPT-5.3-Codex, native computer use, and 1.05M-context API support in the same default model. That matters a lot if your real workload is not "one perfect answer in one shot," but messy multi-step work spread across documents, spreadsheets, web apps, codebases, and tool chains. The short answer: GPT-5.4 is now OpenAI's best all-around model for serious professional work. If you need one model that can research, write, analyze, code, use tools, drive browsers, and survive large contexts, this is the new default. If you need the highest ceiling and can tolerate much higher latency and price, GPT-5.4 Pro is the step-up. TL;DR GPT-5.4 launched on March 5, 2026 as OpenAI's new mainline reasoning model for professional work. OpenAI says it is the first mainline reasoning model to absorb the frontier coding capabilities of GPT-5.3-Codex . On GDPval , GPT-5.4 reaches 83.0% , up from 70.9% for GPT-5.2. On OpenAI's internal investment banking modeling tasks , GPT-5.4 scores 87.3% versus 68.4% for GPT-5.2. On SWE-Bench Pro , GPT-5.4 posts 57.7% , slightly ahead of GPT-5.3-Codex at 56.8% . On OSWorld-Verified , GPT-5.4 hits 75.0% , above GPT-5.2 at 47.3% and even above the human baseline OpenAI cites at 72.4% . The API model supports a 1,050,000 token context window and 128,000 max output tokens , but benchmark results show quality still drops sharply at the far end of that window. GPT-5.4 costs more per token than GPT-5.2 : $2.50 input, $0.25 cached input, and $15.00 output per 1M tokens. GPT-5.4 Pro costs much more at $30 input and $180 output per 1M tokens, and is for the hardest tasks only. In ChatGPT, GPT-5.4 Thinking replaces GPT-5.2 Thinking for Plus, Team, and Pro users. GPT-5.2 Thinking retires on June 5, 2026 . What GPT-5.4 Actually Is OpenAI's own positioning is unusually clear here. GPT-5.4 is: the new default frontier model for complex professional work the first mainline reasoning model that inherits GPT-5.3-Codex-level coding ambition OpenAI's first general-purpose model with native computer use a model with 1.05M context in the API and experimental 1M-context support in Codex a model that supports the full modern agent stack: web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, MCP, and tool search That last point is the real story. Previous OpenAI model choices were easier to split into buckets: use the reasoning model for analysis use the coding model for coding use special tools for browser or desktop automation GPT-5.4 makes those boundaries much blurrier. Naming note OpenAI says GPT-5.4 is the first mainline reasoning model that incorporates the frontier coding capabilities of GPT-5.3-Codex. That is why this release is named GPT-5.4 instead of staying on the GPT-5.2 line with another minor update. 1. Professional Work Is the Real Headline Most model launches still center on coding, math, or abstract reasoning. GPT-5.4 is different. OpenAI's release materials repeatedly frame it around real office work : spreadsheets, presentations, documents, legal analysis, and research-heavy deliverables. That is not marketing fluff. The public numbers back it up. This is where GPT-5.4 becomes more than a "better chatbot." It is now credible for: board update outlines and narrative memos spreadsheet modeling and sanity-checking presentation draft generation with stronger visual variety long document comparison and synthesis contract-heavy diligence work finance, strategy, and operations research that needs both writing and structured reasoning OpenAI also says human raters preferred GPT-5.4-generated presentations 68.0% of the time over GPT-5.2 due to stronger aesthetics, more visual variety, and better use of image generation. That matters because a lot of "knowledge work" is not just about factual recall. It is about producing work products that look usable . 2. GPT-5.4 Turns Coding Into a First-Class Default Capability The coding section is where this launch gets more subtle. OpenAI says GPT-5.4 combines the coding strengths of GPT-5.3-Codex with leading knowledge-work and computer-use capabilities, especially for longer-running tasks where the model can use tools, iterate, and keep pushing with less manual intervention. The official comparison table supports that claim, but with nuance. Here is the practical read: GPT-5.4 is now the best default if your coding work is mixed with analysis, docs, browser steps, and tool orchestration. GPT-5.3-Codex remains very relevant if your workload is mostly pure coding inside a Codex-style environment. GPT-5.2 is now mostly a legacy comparison target. That second point is my inference from OpenAI's own tables. GPT-5.4 edges GPT-5.3-Codex on SWE-Bench Pro, but GPT-5.3-Codex still leads on Terminal-Bench 2.0. So the cleaner way to think about this is: GPT-5.4 = strongest all-around engineering model GPT-5.3-Codex = still a very sharp specialist for terminal-heavy coding loops Inference from official evals If your task is not just "write code," but "understand the repo, search docs, inspect a browser, edit files, and finish the workflow," GPT-5.4 is the better strategic default. If the task lives almost entirely inside a coding agent loop, GPT-5.3-Codex may still be the tighter fit in some environments. 3. Native Computer Use Is One of the Biggest Practical Upgrades This is the part many people will underrate at first. OpenAI calls GPT-5.4 its first general-purpose model with native computer-use capabilities . That is a big shift because it means the mainline reasoning model can now operate on screenshots, return UI actions, and participate directly in browser or desktop workflows. The benchmark jump is not small. OpenAI's docs describe three practical ways to use this capability: a built-in computer tool loop for screenshot-based UI actions a custom browser or VM harness with Playwright, Selenium, VNC, or MCP a code-execution harness where the model writes and runs scripts for UI work That opens up a long list of real product use cases: browser QA and acceptance testing reproducing UI bugs from screenshots or step lists support workflows across admin panels and dashboards CRM or ERP task automation that still needs human supervision accessibility and regression walkthroughs research agents that move between tabs, forms, downloads, and screenshots The built-in loop is also straightforward. OpenAI's computer-use docs describe it as: send a task with the computer tool enabled inspect the returned computer_call execute the returned actions in order send back an updated screenshot as computer_call_output repeat until the model stops asking for computer actions Minimal computer-use example import OpenAI from ' openai ' ; const client = new OpenAI (); const response = await client . responses . create ({ model : ' gpt-5.4 ' , tools : [{ type : ' computer ' }], input : ' Check whether the Filters panel is open. If it is not open, click Show filters. Then type penguin in the search box. Use the computer tool for UI interaction. ' }); console . log ( response . output ); Enter fullscreen mode Exit fullscreen mode Computer-use safety OpenAI's computer-use guide explicitly says confirmation policy should be part of product design, especially for actions like posting, sending data, deleting information, confirming financial actions, or following suspicious on-screen instructions. Treat computer use like a privileged workflow, not a novelty demo. 4. Tool Use and MCP Workloads Are Where GPT-5.4 Starts Feeling Like an Agent Model GPT-5.4 is not just stronger at single-model reasoning. It is stronger at deciding what tools to call and when . OpenAI's official evals show: 82.7% on BrowseComp for GPT-5.4 89.3% on BrowseComp for GPT-5.4 Pro 67.2% on MCP Atlas for GPT-5.4 54.6% on Toolathlon for GPT-5.4 98.9% on Tau2-bench Telecom for GPT-5.4 That matters for teams building agents across big internal tool surfaces. The most interesting supporting feature here is tool search . According to OpenAI's tool-search docs, tool search lets the model dynamically search for and load tools into the context only when needed. The point is not just convenience. It can reduce token usage, preserve the model cache better, and avoid dumping a huge tool catalog into the prompt up front. That is especially useful when you have: large internal tool catalogs namespaced function sets tenant-specific tool inventories MCP servers with many functions agent systems where most tools are irrelevant on most turns Minimal tool-search pattern const response = await client . responses . create ({ model : ' gpt-5.4 ' , input : ' List open orders for customer CUST-12345. ' , tools : [ crmNamespace , { type : ' tool_search ' }], parallel_tool_calls : false }); Enter fullscreen mode Exit fullscreen mode In OpenAI's docs, the deferred tools live inside a namespace or MCP server and are loaded only when the model decides it needs them. That is a major design improvement for enterprise agents because it moves you away from the old pattern of shoving 50 JSON schemas into every request. 5. The 1M Context Window Is Real, but It Is Not Magic This is one of the most important practical caveats in the whole release. Yes, GPT-5.4 supports a 1,050,000 token context window in the API, with 128,000 max output tokens . OpenAI also says GPT-5.4 in Codex has experimen