GPT-5.5 vs GPT-5.4: Specs, Price, Benchmarks, and Choice

GPT-5.5 is the stronger model than GPT-5.4 on the official launch evaluations, but it also costs exactly twice as much per text token at standard API rates. Both support a 1.05-million-token context window, 128,000 output tokens, image input, structured outputs, computer use, web search, code execution, and MCP.

That makes the decision less obvious than “newer wins.” GPT-5.5 is best suited to difficult coding, research, professional work, and long agent tasks where higher completion quality can offset higher token cost. GPT-5.4 remains compelling when a workflow is stable, cost-sensitive, or benefits from reasoning disabled by default.

There is also a current-model caveat. OpenAI released GPT-5.4 on March 5, 2026 and GPT-5.5 on April 23. As of August 2026, OpenAI recommends GPT-5.6 for most new reasoning workloads. This comparison is most useful for teams maintaining existing GPT-5.4 systems, evaluating a staged move to GPT-5.5, or seeking a lower-cost baseline beside newer models.

Quick Comparison

The context and output limits are identical. The meaningful differences are intelligence, efficiency on difficult tasks, knowledge cutoff, default reasoning behavior, and price.

The Most Important Difference: Default Reasoning

GPT-5.4 defaults to none reasoning effort. GPT-5.5 defaults to medium.

That one configuration difference can change a migration test substantially. If you replace the model ID and accept defaults, GPT-5.5 may use more reasoning, take longer, and generate a higher bill even before model pricing is considered. At the same time, it may complete more difficult tasks with fewer retries.

For a fair comparison, decide what you want to measure:

• Compare defaults to evaluate the normal out-of-box experience.
• Compare the same effort level to isolate model capability.
• Compare equal budgets to evaluate business value.
• Compare equal latency targets to evaluate interactive performance.

OpenAI describes low effort as a good option for efficient planning, tool use, search, data analysis, and execution-oriented coding. Medium is the balanced choice for agentic coding, research, documents, spreadsheets, and long-horizon delegation. High and xhigh target more difficult, slower work.

Do not assume GPT-5.5 medium and GPT-5.4 none are equivalent operating points. Record the exact effort value in every evaluation.

Coding Performance

GPT-5.4 was already a strong coding release. OpenAI described it as incorporating the coding advances of GPT-5.3-Codex while extending them into a general professional model with native computer use and broad tool support.

GPT-5.5 improved the behaviors that matter around code generation: understanding a large system, diagnosing ambiguous problems, predicting surrounding changes, coordinating tools, and staying with a task until testing and validation were complete.

At launch, OpenAI reported:

OpenAI also reported 58.6% for GPT-5.5 on SWE-Bench Pro and said it outperformed GPT-5.4 while using fewer tokens across the coding evaluations shown in the launch material.

Those results favor GPT-5.5 for repository-scale implementation, difficult debugging, multi-file refactors, and long command-line workflows. GPT-5.4 can still be more economical for routine changes with clear instructions, especially when the task does not benefit from medium reasoning.

The best coding evaluation is your own issue set. Include familiar tasks, ambiguous bugs, changes that require tests, and scenarios where a tool fails. Measure completion, regressions, human edits, and total cost—not just whether code was produced.

Knowledge Work and Computer Use

GPT-5.4 was introduced as a professional-work model capable of operating across spreadsheets, presentations, documents, software, and tools. It was also OpenAI’s first general-purpose release described as having native state-of-the-art computer use.

GPT-5.5 pushed further on intent understanding and end-to-end execution. The model was designed to locate information, decide what matters, manipulate artifacts, inspect the result, and continue with less back-and-forth.

Official launch comparisons included:

The gap on GDPval is modest, while the telecom workflow gain is much larger. This illustrates why a single average does not answer a deployment question. A model can be only slightly better on broad professional tasks and materially better on a policy-constrained multi-step workflow.

For office automation, test formulas, references, layout, citations, and final file integrity. A more polished explanation does not matter if the spreadsheet contains a broken formula or the presentation omits a required slide.

Tool Use and Research

Both models support a broad Responses API tool set, including:

• Function calling.
• Web search.
• File search.
• Tool search.
• Image generation.
• Code interpreter.
• Hosted shell.
• Patch application.
• Skills.
• Computer use.
• MCP.

GPT-5.5’s official evaluations showed stronger tool-related performance:

The raw tool list is not the differentiator; both models can access similar capabilities. The difference is how effectively the model selects tools, interprets their output, and keeps the workflow moving.

Tool design still matters more than many teams expect. An unrestricted command tool can turn one mistake into a large side effect. Prefer narrow actions, explicit parameters, observable return values, and least-privilege access regardless of model choice.

Reasoning and Scientific Work

GPT-5.5 also improved on several hard reasoning benchmarks announced by OpenAI:

The Tier 4 mathematics gap is notable, but teams should not extrapolate a benchmark score directly into domain reliability. Scientific, financial, legal, and security work requires expert review, grounded sources, reproducible calculations, and clear handling of uncertainty.

GPT-5.5 is the better candidate when a task contains several dependent reasoning stages and a mistake is expensive. GPT-5.4 may be sufficient for summarization, extraction, routine transformation, or workflows where deterministic validation catches errors cheaply.

Context Window: A Tie with Different Economics

Both models accept up to 1,050,000 context tokens and can produce up to 128,000 output tokens. Both also apply a long-context pricing multiplier when the prompt exceeds 272,000 input tokens: the full session is billed at twice the normal input price and 1.5 times the normal output price for standard, Batch, and Flex processing.

Because GPT-5.5’s base token prices are already twice GPT-5.4’s, very long sessions can become expensive quickly.

Avoid filling the context window because it exists. Use retrieval, file search, summarization, and structured task state. Keep stable instructions separate from temporary tool logs. Re-read authoritative resources before consequential edits and discard intermediate context that no longer affects the outcome.

The December 2025 knowledge cutoff gives GPT-5.5 three more months of training-era knowledge than GPT-5.4’s August 2025 cutoff. Neither cutoff is current for August 2026, so both need web search or connected data for recent facts.

API Pricing: GPT-5.5 Costs 2× per Token

At current documented standard rates per one million text tokens:

Regional processing carries a 10% uplift for both model families. Tool-specific fees can also apply.

Token price is not the same as task price. Suppose GPT-5.4 needs three attempts and five minutes of human repair, while GPT-5.5 succeeds once. The newer model may still be cheaper. Conversely, if a high-volume extraction pipeline already reaches 99% accuracy on GPT-5.4, doubling token cost may deliver little value.

Calculate:

• Tokens per attempt.
• Attempts per completed task.
• Tool calls and external fees.
• Human review minutes.
• Failure and escalation cost.
• Latency and throughput requirements.

Choose based on cost per accepted result.

Latency and Efficiency

OpenAI said GPT-5.5 matched GPT-5.4 per-token latency in real-world serving while achieving higher intelligence, and that it used fewer tokens on the compared Codex tasks. That is favorable but does not mean every response is equally fast.

GPT-5.5 defaults to medium reasoning while GPT-5.4 defaults to none. An application using defaults may therefore observe more time before completion. Tool calls, long inputs, reasoning level, service tier, and output length all influence latency.

Benchmark time to accepted result rather than time to first token alone. For interactive support, first-token latency may dominate. For a background code migration, completion quality and fewer retries may matter more.

Responses API and Workflow State

Both models support Responses and Chat Completions, but OpenAI recommends Responses for reasoning-heavy and tool-driven workflows.

For GPT-5.4 and GPT-5.5 long-running flows, preserve the assistant message phase field. Intermediate updates use phase: "commentary"; the completed response uses phase: "final_answer". If an application replays conversation items and drops these values, the model may treat an intermediate preamble as the final response.

Using previous_response_id is often simpler because prior state is preserved. If you manage state yourself, retain tool calls, outputs, reasoning items where supported, and exact phase information.

This migration detail can matter more than a benchmark point. A state bug can make either model stop early.

Which Model Should You Choose?

Choose GPT-5.5 when

• The workload involves difficult coding or debugging.
• The agent must stay on task through many tool calls.
• Errors are expensive and higher first-pass quality has clear value.
• Research, documents, spreadsheets, or computer use require judgment.
• The team has evaluations that show fewer retries or human interventions.
• A more recent knowledge cutoff materially helps the baseline.

Choose GPT-5.4 when

• Cost per token is a major constraint.
• The workflow already performs reliably in production.
• Tasks are routine, well-scoped, and easily validated.
• Low latency with reasoning disabled is important.
• The expected quality gain does not justify a 2× token rate.
• You need a stable baseline during a staged migration.

Evaluate GPT-5.6 when

• You are starting a new reasoning workload in August 2026.
• You want to follow OpenAI’s current model-selection guidance.
• The project can compare Sol, Terra, or Luna variants based on intelligence, cost, and latency.

Do not build new architecture around the assumption that GPT-5.5 is the final destination. Stable tool contracts and evaluations make model upgrades easier.

Migration Checklist

Moving from GPT-5.4 to GPT-5.5 should be an evaluation project, not a one-line production change.

1. Pin the current GPT-5.4 snapshot as the baseline.
2. Build a representative evaluation set from real tasks and failures.
3. Compare the same reasoning effort first.
4. Then compare each model’s recommended operating point.
5. Track completion, correctness, tool errors, latency, tokens, and human edits.
6. Audit prompts for assumptions tied to GPT-5.4 behavior.
7. Confirm phase and conversation-state handling in the Responses API.
8. Test long-context pricing with real prompt sizes.
9. Run a shadow or limited-percentage rollout.
10. Keep an immediate rollback path.

Use model snapshots when you need repeatable behavior. Aliases can move; snapshots are intended to lock performance and behavior more precisely.

Creative Workflow Example

A creative team might use the language model to research an audience, write a concept, create a shot list, define prompts, and evaluate outputs. The media generation layer can remain stable while the planning model changes.

DeepFake’s AI model library can serve as that visual layer. A team can use text-to-video for an open scene or image-to-video when an approved keyframe must anchor identity and composition.

Evaluate GPT-5.4 and GPT-5.5 on the planning artifacts: completeness of the shot list, consistency of character instructions, adherence to brand constraints, and usefulness of the review rubric. Do not judge the language model only from how elegant the prose sounds.

Final Verdict

GPT-5.5 beats GPT-5.4 on OpenAI’s announced coding, computer-use, professional-work, tool-use, mathematics, and cybersecurity comparisons. It also offers a later knowledge cutoff and defaults to medium reasoning, making it the stronger choice for difficult long-horizon work.

GPT-5.4 matches the same context and output limits, supports a very similar tool set, and costs half as much per token. It remains a rational choice for high-volume, well-tuned, easily verified workloads where GPT-5.5’s quality gain does not offset the price.

The deciding metric is not benchmark rank or token price. It is cost per correctly completed task. Run both models on the work your users actually delegate, control the reasoning setting, include human review cost, and preserve a stable surrounding system. For greenfield projects, include GPT-5.6 in the same evaluation because it is now OpenAI’s recommended starting point.

Official references

• Introducing GPT-5.4
• GPT-5.4 API model page
• Introducing GPT-5.5
• GPT-5.5 API model page
• OpenAI reasoning guide