OpenAI Reasoning & coding
GPT-5.4
Carry a complex brief from evidence to an implementation plan.
GPT-5.4 is OpenAI's general-purpose route for reasoning, coding, visual understanding and agent tasks. Give it the source material, the decision to make and a measurable acceptance check. It is a useful choice when a workflow needs one model to connect long context, tool calls and reliable structured output.
- Reasoning
- Text + image input
- Long context
The model can propose and structure work, but execution still depends on the tools, permissions and validation loop in your application. Confirm the effective route limits before sending a very large request.
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gpt-5.4Start a conversation with this model and ask follow-up questions.
USD /1M tokens
Pricing
| Group | Tier | Input | Output | Cache Read |
|---|---|---|---|---|
| default | input<272K | $2.5 | $15 | $0.25 |
| default | input>=272K | $5 | $22.5 | $0.5 |
| openai-0.2 | input<272K | $0.5 | $3 | $0.05 |
| openai-0.2 | input>=272K | $1 | $4.5 | $0.1 |
Public list prices are shown in USD. Final charges may vary by account group and usage tier.
Overview
What is GPT-5.4?
GPT-5.4 is listed in the TokenHot catalog with text and image input, text output and a 1.05M-token context value. Treat that catalog value as the route's current display data, while the provider snapshot and gateway configuration remain separate version checks.
The strongest use cases are tasks where several facts must stay aligned: a repository and its issue, a visual reference and a UI requirement, or a migration plan and its compatibility constraints. Ask for uncertainty and evidence instead of rewarding a confident guess.
Make a patch reviewable
Include the reproduction, affected modules and project conventions. Request a minimal change, a regression check and a clear list of commands that still need to run.
Compare versions by outcome
Give the current integration, the target behavior and representative requests. Separate API compatibility from changes in quality, latency or cost.
Bound every action
Define tool schemas, permissions, stop conditions and the artifact that proves completion. Keep consequential actions in your application control plane.
From brief to deliverable
Put GPT-5.4 to work
Platform and engineering teams
Plan a version migration
Use a real integration diff to identify incompatible parameters, changed assumptions and the smallest safe rollout. The model should explain which evidence supports each migration step.
- Tools you need
- Repository access and a test runner for verification; pasted files are enough for analysis.
- Keep in mind
- A proposed migration is not a compatibility test. Run the same fixtures against both routes before changing production traffic.
View a task brief
Review the current integration and the target GPT-5.4 route. Identify compatibility risks, propose the smallest maintainable patch, and produce a rollout checklist with a rollback trigger. Cite the supplied files for every factual change. Mark unexecuted tests clearly and do not claim the migration is safe without fixture results.
Frontend and product teams
Reconcile a visual reference with code
Pair a screenshot with the component and acceptance criteria. Ask for concrete layout observations, likely causes and a short implementation sequence that a frontend engineer can verify.
- Tools you need
- Image input plus a browser and accessibility tools for interaction verification.
- Keep in mind
- A screenshot cannot prove keyboard behavior, responsive layout or form submission.
View a task brief
Review the supplied screenshot, requirements and component code. List the most consequential visible discrepancies first, then give implementation suggestions and a browser verification checklist. Distinguish visual evidence from hypotheses and do not state that an interaction passed without test output.
Operations and agent builders
Turn a long request into a bounded run
Use the model to decompose a multi-step operation before any tool is called. Require a machine-readable plan, explicit dependencies and a stop state for missing evidence.
- Tools you need
- Your orchestration layer, schema validation and logging; the model does not supply a hosted workspace.
- Keep in mind
- The model cannot grant itself access or confirm an external side effect.
View a task brief
Create a bounded plan for the supplied operation. Return structured tool arguments, dependencies, stop conditions and a final report shape. Never invent a tool result; mark unavailable information and wait for the application to supply it.
Capabilities in context
Key features
| Feature | What you get | Why it matters |
|---|---|---|
| Context capacity | 1.05M tokens shown in the catalog | Keep the effective context and output budget visible in your own request builder, especially for multi-file work. |
| Input and output | Text and image input; text output | Use visual evidence as context. Choose a separate generation route for image, audio or video output. |
| Reasoning and tools | Reasoning, function calling and tool use are catalog capabilities | Your application still owns tool execution, permissions, retries and result validation. |
| Structured responses | Structured output is listed by the catalog | Validate the returned schema and handle refusal, truncation or malformed output as normal states. |
| Version identity | TokenHot model ID: gpt-5.4 | Pin the route name in your integration and record the catalog snapshot when reproducibility matters. |
The model ID, modalities and route capabilities come from TokenHot's current catalog. Confirm endpoint-specific parameters and provider snapshot behavior before integration.
Audience & fit
Where GPT-5.4 fits
Teams maintaining a production integration
Keep a stable request set and compare accepted results, latency, retries and total cost before switching from another GPT route.
Builders of tool-based workflows
Use explicit schemas and bounded permissions. Put irreversible actions behind application checks and record the returned artifacts.
Analysts working from mixed evidence
Name every source and ask for section-level support. Require a separate list of unknowns so missing context is visible.
Cost context
Plan the cost of a useful result
Count the complete request
Budget input, output, cache and retries together. A short answer can still require a large context or several validation turns.
Check the long-input tier
GPT-5.4 uses conditional tiers in the catalog. Review the full pricing table and the exact boundary before submitting a large context.
Measure accepted work
Compare the cost of a result that passes your checks, not just the visible token count or first response.
Use the current pricing table for available rates and account groups. Model IDs and prices come from TokenHot’s catalog; provider pricing and subscriptions are separate.
API
Code examples
curl --location --request POST 'https://api.tokenhot.ai/v1/chat/completions' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data-raw '{
"model": "gpt-5.4",
"messages": [
{
"role": "user",
"content": "What model are you"
}
]
}'FAQ
Frequently asked questions
What is GPT-5.4 best suited for?
GPT-5.4 is suited to the input, output, and capability types listed on this page. Test production workloads before choosing it for a critical system.
How is GPT-5.4 priced?
Pricing is shown in the pricing section above. Actual cost depends on usage volume, account group, and the request payload.
How can I call GPT-5.4?
Use the model ID shown above with one of the supported API protocols. Code examples are provided when usage data is available.
Can GPT-5.4 browse or run code by itself?
No hosted browser, shell or workspace is implied by the model route. Your application must provide supported tools, permissions and an execution environment.
Does image input mean the model generates images?
No. Image input lets the model analyze visual material. Use an image-generation model when the output itself must be an image.
How should I compare GPT-5.4 with another model?
Use the same source material, tool contract and acceptance checks. Record corrections, latency, retries and total cost before making a routing decision.
Why can the effective context differ from the provider maximum?
The page reflects TokenHot catalog data for this route. Gateway limits, output budgets and request construction can reduce the usable amount; verify them at integration time.
Get started
Build with GPT-5.4
API