GPT-5.4 mini brings the core capabilities of GPT-5.4 to a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs with strong performance across reasoning, coding,...
GPT‑5.4 Mini is a compact language model from OpenAI that balances capability with lower computational overhead. It supports a context window of 400,000 tokens and a maximum output of 128,000 tokens,…
GPT‑5.4 Mini excels at complex, multi‑step reasoning tasks that can be decomposed into a chain of thought. Benchmarks indicate strong performance on GPQA Diamond (87.5) and similar scientific reasoning datasets. It handles mathematical problem‑solving, code generation and debugging, and logical puzzles effectively. Its large context window further enables it to maintain context over long documents—ideal for summarising lengthy reports, extracting key points from legal contracts, or answering questions about a full research paper. The model also performs well on tool‑use scenarios where it must decide when to call external functions based on conversation history. For simpler tasks like short‑answer Q&A or classification, a cheaper model may be more cost‑effective.
If your use case involves short prompts (under 10K tokens), simple classification, or straightforward generation that does not require deep reasoning, a cheaper model like GPT‑4o-mini or GPT‑3.5‑Turbo (available via OrcaRouter) may provide sufficient quality at a fraction of the cost. GPT‑5.4 Mini's price of $0.75/$4.50 per 1M tokens is higher than many smaller models. Additionally, if you do not need multimodal input (file or image) or the 400K context window, those features add no value. Evaluate the average token consumption per request and the required output length. For high‑volume production systems, even small per‑token savings can significantly reduce monthly expenditure. OrcaRouter allows you to switch models easily by changing the model ID in the API call.
Yes, GPT‑5.4 Mini supports the function calling interface compatible with OpenAI's API. When using OrcaRouter's OpenAI‑compatible endpoint, you can define functions (tools) in the request and let the model decide to invoke them. The large context window is particularly useful for agents that need to maintain a history of function calls and their results. This capability enables building complex AI workflows: natural language queries trigger function calls to databases, calculators, or APIs, and the model processes the returned data to generate final answers. For best results, provide clear function descriptions and examples. Note that the model may sometimes return invalid function arguments; implement validation layers in production.
GPT‑5.4 Mini supports JSON mode when you set the response_format parameter to {"type": "json_object"} in the API request. This instructs the model to output valid JSON. Combined with the system prompt, you can enforce a specific schema. The 128K‑token output limit allows generating very long structured documents, such as full SQL schemas or nested configuration files. However, the model does not guarantee structural correctness beyond JSON validity—you may need to post‑process or validate against a schema. For production, use the tool calling approach to enforce more reliable structured outputs. The OrcaRouter API passes the response_format parameter exactly as provided to OpenAI, without modification.
GPQA Diamond is a multiple‑choice benchmark that tests graduate‑level scientific reasoning across physics, chemistry, biology, and other domains. A score of 87.5 indicates that GPT‑5.4 Mini correctly answered 87.5% of the questions, placing it at a high reasoning capability level for its model size. This score is a headline benchmark reported by OpenAI. It suggests the model can handle complex scientific queries that require deep understanding and step‑by‑step reasoning. However, benchmarks do not capture all real‑world scenarios; evaluate the model on your own representative tasks. Compared to larger models like GPT‑5.4 Full (which typically scores higher), the Mini variant offers a balance of performance and cost.
Latency primarily depends on the model’s underlying infrastructure (OpenAI) and the length of input and output. Because OrcaRouter adds no additional processing overhead beyond routing the request to OpenAI, the response time is similar to calling OpenAI directly. For a prompt of 10K input tokens and a 500‑token output, latency is typically under five seconds. For longer outputs (up to 128K tokens), response times can extend to several minutes. OrcaRouter supports streaming via server‑sent events, which reduces perceived latency by delivering tokens as they are generated. Use the stream parameter to enable real‑time output. Note that multimodal inputs (images/files) may add extra preprocessing time.
Despite strong reasoning benchmarks, GPT‑5.4 Mini can still produce factual errors or hallucinate information, especially on niche or rapidly evolving topics. Its knowledge cutoff is not specified; assume it reflects OpenAI's latest training data. The model may also struggle with tasks that require exact arithmetic or precise recall of obscure facts. Additionally, the 128K‑token output limit, while generous, may not be sufficient for generating very long books or entire codebases in one pass. For such tasks, consider chunking the output or using a model with sequential generation. The model’s performance on languages other than English may be less robust. Always test with diverse inputs from your target domain.
Larger models in the GPT‑5.4 family, such as GPT‑5.4 Full, typically achieve higher scores on reasoning benchmarks (e.g., GPQA Diamond >90) and have larger context windows (e.g., 1M tokens). However, they are more expensive per token and have higher latency. GPT‑5.4 Mini sacrifices some raw performance for lower cost and faster inference. For tasks that do not require the highest accuracy, the Mini variant often provides a favourable cost‑performance trade‑off. If your application demands maximum accuracy on difficult reasoning tasks, choose the Full model. OrcaRouter offers both options with simple model ID changes. Benchmark scores are only one factor; evaluate on your own dataset.
OrcaRouter bills at the exact provider rate with zero markup. For GPT‑5.4 Mini, the price is $0.75 per 1 million input tokens and $4.50 per 1 million output tokens. Input tokens include the system prompt, user messages, and any multimodal tokens (file or image). Output tokens count only the generated text. There are no additional fees for streaming or non‑streaming calls. Cached input tokens are not discounted because OrcaRouter passes through the provider's pricing without modification. To estimate cost, multiply your average per‑request token counts by these rates. For high‑volume usage, consider using a model with lower per‑token pricing for simple tasks.
While GPT‑5.4 Mini is cheaper than the full GPT‑5.4 model, it is still more expensive than many smaller models like GPT‑4o-mini or GPT‑3.5‑Turbo. Use it only when the larger context window, multimodal support, or higher reasoning ability is essential. For example, a customer‑support chatbot with short queries may overspend by using this model. Batch processing of long documents can quickly rack up token costs. Calculate the total tokens per document and multiply by the rates to see if a cheaper alternative with similar capability exists. OrcaRouter allows you to route requests to multiple models based on prompt length or topic, optimising cost automatically.
No. OrcaRouter does not modify or cache model responses. Every request is forwarded to OpenAI in real time, and you are billed exactly the provider rate per token. There are no volume discounts or prepaid plans; pricing is pay‑as‑you‑go based on token usage. This transparency means your costs directly reflect your OpenAI usage. If OpenAI introduces caching or tiered pricing in the future, OrcaRouter will pass those changes through without markup. For predictable high‑volume usage, consider a direct enterprise agreement with OpenAI, but for flexible access with minimal overhead, OrcaRouter is a straightforward option.
To use GPT‑5.4 Mini, set the OpenAI‑compatible client's base URL to https://api.orcarouter.ai/v1 and the model ID to "openai/gpt-5.4-mini". Provide your OrcaRouter API key as the authentication token. All standard OpenAI chat completion parameters are supported: `messages`, `temperature`, `top_p`, `max_tokens`, `stream`, `response_format`, `tools`, etc. Example (Python): ```python from openai import OpenAI client = OpenAI(api_key="your-orcarouter-key", base_url="https://api.orcarouter.ai/v1") response = client.chat.completions.create( model="openai/gpt-5.4-mini", messages=[{"role": "user", "content": "Explain quantum entanglement"}], max_tokens=1000 ) ``` The response includes the completions, usage statistics, and model identifier.
OrcaRouter's API is designed to be fully OpenAI‑compatible. There are no OrcaRouter‑specific parameters; all parameters are passed directly to the underlying provider (OpenAI). However, OrcaRouter adds a small latency overhead for routing and authentication, typically under 50 milliseconds. You can pass standard parameters like `user`, `stop`, `frequency_penalty`, `presence_penalty`, and `logit_bias`. The API returns the model field as "openai/gpt-5.4-mini" regardless of provider. If you need to track usage per request, use the `user` parameter or parse the returned usage metrics. For advanced routing (e.g., fallback models), contact OrcaRouter support.
Yes. Migration requires only two changes in your code: replace the OpenAI base URL with https://api.orcarouter.ai/v1 and use your OrcaRouter API key. The model ID must be prefixed with the provider (e.g., "openai/gpt-5.4-mini" instead of "gpt-5.4-mini"). All other parameters remain unchanged. This allows you to use OrcaRouter as a unified gateway for multiple providers without modifying your existing OpenAI integration. Test on a subset of traffic before switching completely. OrcaRouter provides usage logs and billing information that you can compare to your previous direct usage to verify cost transparency.
GPT‑5.4 Mini is a newer model from OpenAI with a larger context window (400K vs. 128K for GPT‑4o) and higher max output (128K vs. 4K‑16K typical). It also supports image and file inputs, while GPT‑4o primarily handles text and images. Pricing for GPT‑4o is typically lower ($2.50/$10 per 1M tokens for standard version) but depends on the variant. On reasoning benchmarks like GPQA Diamond, GPT‑5.4 Mini (87.5) surpasses GPT‑4o's reported scores (around 70‑80). However, GPT‑4o has been widely tested and may have better support for certain tools. Choose GPT‑5.4 Mini when long context and high reasoning are priorities; use GPT‑4o for shorter tasks where cost is paramount.
GPT‑5.4 Full offers a larger context window (1M tokens) and higher absolute reasoning scores (GPQA Diamond >90), but at significantly higher per‑token pricing. The Mini variant sacrifices some benchmark performance for cost efficiency. For many practical applications, the difference in output quality is marginal, especially on tasks that do not push the limits of reasoning. If your use case requires processing extremely long documents (over 400K tokens) or maximising accuracy on difficult graduate‑level questions, GPT‑5.4 Full is justified. Otherwise, GPT‑5.4 Mini often provides similar results at roughly half the cost. OrcaRouter lets you easily switch between the two by changing the model ID in your API request.
Claude 3.5 Sonnet (by Anthropic) offers a 200K context window, lower than GPT‑5.4 Mini's 400K. Pricing for Claude 3.5 Sonnet is $3.00 per 1M input and $15.00 per 1M output (Anthropic rates), making it more expensive per token. Benchmark scores on similar reasoning tests are comparable, though direct comparisons on GPQA Diamond are not publicly available. Claude 3.5 Sonnet is known for strong instruction following and safety guardrails. GPT‑5.4 Mini may be preferred for tasks that need very long context or higher output token limits. Evaluate both on your specific prompts, as subjective quality differences can affect user satisfaction. OrcaRouter provides access to both models for easy A/B testing.
Open‑source models like Llama 3.1 70B or Mixtral 8x22B can be run on your own hardware for predictable costs, especially at high volume. However, they often have smaller context windows (128K or less) and may require significant infrastructure to achieve low latency. GPT‑5.4 Mini offers a 400K context window, multimodal input, and expert‑tuned reasoning without infrastructure overhead. If you value ease of use, token‑based pricing, and the ability to scale instantly, GPT‑5.4 Mini via OrcaRouter is more convenient. If you need complete control over data residency and have low latency requirements, and your task fits within a smaller context, an open‑source alternative may be cheaper in the long run. Test both in your environment.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-5.4-mini",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstreamstructured_outputstool_choicetools| Input / 1M tokens | $0.750 |
| Output / 1M tokens | $4.50 |
| Cache read / 1M | $0.075 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/openai/gpt-5.4-miniOpen @misc{orcarouter_gpt_5_4_mini,
title = {GPT-5.4 Mini API},
author = {OpenAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.4-mini}
}OpenAI. (2026). GPT-5.4 Mini API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.4-mini