GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency...
OpenAI GPT-5.4 Nano is a language model developed by OpenAI, accessed through OrcaRouter's OpenAI-compatible API. It supports file, image, and text input modalities and offers a context window of…
The 400,000-token context window allows GPT-5.4 Nano to process entire novels, long research papers, or extended conversation histories in a single API call. This eliminates the need for chunking or summarization when working with large documents. For example, you could feed in a full legal contract (often 30,000–50,000 words) and ask for clause-by-clause analysis. The model can also maintain coherent reasoning over very long prompts, making it suitable for complex tasks like multi-step code review or narrative generation. Keep in mind that larger contexts increase latency and cost, so you should only use the full window when necessary.
If your task requires only short inputs (a few thousand tokens) and does not need image or file support, a smaller model like GPT-4o mini or similar will be more cost-effective and faster. GPT-5.4 Nano's larger context and multimodal capabilities come at a higher price per token. For simple chatbots, classification, or lightweight summarization, a cheaper model can deliver comparable quality without paying for unused capacity. Additionally, if your application demands very low latency or high throughput, smaller models generally have faster inference times. Use GPT-5.4 Nano only when its unique features—long context, large output, or multimodal input—are essential.
GPT-5.4 Nano can generate up to 128,000 tokens in a single response. This is useful for tasks that require producing very long content, such as drafting entire reports, writing full-length stories, or generating comprehensive codebases. Combined with the large context window, you can input a lengthy prompt and receive an equally long answer without multiple round-trips. However, generating such long outputs can be expensive and slow. For most applications, shorter outputs (e.g., a few thousand tokens) are sufficient. The 128K limit is a ceiling, not a target; you should set appropriate max_tokens in your API calls to control costs and latency.
GPQA (Graduate-Level Physics Question Answering) Diamond is a benchmark that tests a model's ability to answer multiple-choice questions on graduate-level physics concepts. A score of 81.7 means GPT-5.4 Nano correctly answered 81.7% of the questions, indicating strong reasoning skills in a specialized domain. This is a challenging dataset, so achieving a high score suggests the model can handle complex scientific reasoning. However, benchmarks are not the full picture; real-world performance on your specific task may vary. Compare this score to other models available on OrcaRouter to gauge relative capability in reasoning tasks.
Latency depends on input and output token counts, model load, and OrcaRouter infrastructure. For short prompts (e.g., 1,000 tokens input, 100 tokens output), response times are typically a few seconds. For very large contexts (e.g., 400K tokens input), latency can be significantly longer due to the additional processing needed. Output generation speed scales with the number of tokens produced. OrcaRouter does not provide specific latency figures, but you can estimate using the model's time-to-first-token and token-per-second rates from general OpenAI performance, noting that larger contexts increase both. For lowest latency, use smaller contexts and outputs.
Strengths: High score on GPQA Diamond (81.7) demonstrates advanced science reasoning. Its large context window and multimodal input allow it to outperform smaller models on tasks that require integrating information from many pages or images. Limitations: Benchmarks do not cover every domain. The model may still make errors on niche topics or highly ambiguous queries. It is not specifically optimized for coding or creative writing, though it likely performs well on those tasks. Also, because it is a large model, it is more expensive and slower than alternatives. For most benchmarks, you should evaluate the model on your own data to confirm suitability.
Pricing is $0.20 per 1 million input tokens and $1.25 per 1 million output tokens. OrcaRouter bills at the provider rate with zero markup, so you pay exactly the OpenAI direct cost. Input tokens include the prompt, image tokens (counted as a multiple), and file content after extraction. Output tokens are generated responses. There are no additional fees for API access or usage tiers. This transparent pricing makes it easy to estimate costs: for example, a 10,000-token input and 1,000-token output costs $0.002 + $0.00125 = $0.00325 per call.
The high cost per token relative to smaller models means you should right-size your usage. If your task uses only 10,000–20,000 tokens per request, a cheaper model like GPT-4o mini (if available) would be significantly more economical. However, if you truly need the 400K context or 128K output, GPT-5.4 Nano may be the only practical choice. Caching can reduce costs: OrcaRouter does not currently mention prompt caching, but you can structure your prompts to reuse large static prefixes to minimize repeated input tokens. Also, remember that image inputs incur token costs proportional to image resolution; use lower-res images when possible.
OrcaRouter passes through provider rates without markup, so discounts from the provider (e.g., bulk discounts or committed use) would apply if OpenAI offers them. However, no specific caching features are documented for GPT-5.4 Nano on OrcaRouter. To manage costs, you can implement client-side caching of prompts or use patterns like system messages that remain constant across requests. If you anticipate high volume, contact OrcaRouter for possible negotiated rates. For now, the standard pay-per-token pricing applies.
You access GPT-5.4 Nano through OrcaRouter's OpenAI-compatible API at base_url https://api.orcarouter.ai/v1. Use the model ID "openai/gpt-5.4-nano" in your request. The API follows the same format as OpenAI's Chat Completions endpoint, so you can use existing OpenAI SDKs by changing the base URL and model name. Example using Python openai library: ```python from openai import OpenAI client = OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="your_key") response = client.chat.completions.create( model="openai/gpt-5.4-nano", messages=[{"role": "user", "content": "Hello"}] ) ``` All standard parameters like temperature, max_tokens, top_p, etc., are supported.
For most use cases, set temperature to a reasonable value like 0.7 for balance, or lower (0.2–0.4) for factual tasks. max_tokens defaults to the model's maximum (128K), but you should set it explicitly to limit costs. A typical setting might be 4096 tokens for standard responses. For image inputs, include the image in the content array using the data URL format or a URL. For file inputs, upload the file to OrcaRouter and reference its URL; OrcaRouter's API supports file attachments. You can also use system messages to set behavior. Top_p can be left at 1, and frequency/penalty parameters work as usual.
Migration is simple because OrcaRouter's API is fully OpenAI-compatible. Change the base URL from https://api.openai.com/v1 to https://api.orcarouter.ai/v1, and replace the model name from "gpt-5.4-nano" to "openai/gpt-5.4-nano". Your existing code, SDKs, and authentication patterns work with only these two changes. OrcaRouter uses its own API keys, so obtain an API key from your OrcaRouter account. No changes are needed for messages, tools, streaming, or other features. Test with a small request to confirm connectivity before scaling.
Compared to smaller OpenAI models like GPT-4o or GPT-4o mini, GPT-5.4 Nano offers a larger context window (400K vs. typically 128K) and higher output limit (128K vs. 16K), plus multimodal input support. However, it costs more per token: $0.20/$1.25 per M vs. lower rates for smaller models. Its GPQA Diamond score of 81.7 may be higher than older models but not directly comparable to future ones. For tasks that fit within smaller contexts, a cheaper model is preferable. GPT-5.4 Nano is positioned as a high-end option for demanding applications.
Without specific benchmark comparisons, we can only compare on specifications. GPT-5.4 Nano's 400K context is similar to Anthropic Claude's 200K context, but larger. Its multimodal input support matches Gemini's capabilities. Pricing: GPT-5.4 Nano at $0.20/$1.25 is competitive with Claude Opus and Gemini Ultra, but exact rates vary. The GPQA Diamond score of 81.7 is one data point; other models may score differently. For long-context tasks, GPT-5.4 Nano is a strong contender, but the best model depends on your specific domain. Test on your data to determine which yields better results.
Choose GPT-5.4 Nano if your use case requires both a very large context window and multimodal input (text + image + file). For example, analyzing a 300-page PDF with embedded images and charts. If you only need long text without images, other models like Claude 3.5 Sonnet (200K context) or Gemini 1.5 Pro (1M context) might be more cost-effective or offer different strengths. Consider the pricing: GPT-5.4 Nano's rate is transparently zero-markup on OrcaRouter, so compare per-token costs. Also, if you already rely on OpenAI ecosystem (tools, SDKs, fine-tuning), staying with GPT-5.4 Nano simplifies integration.
Potential limitations: No cited advantages in coding or creative tasks. Its 400K context, while large, is smaller than some competitors like Gemini 1.5 Pro (1M tokens). Its benchmark score (81.7 on GPQA Diamond) may not indicate superior performance on all reasoning tasks. The model is not optimized for low latency; smaller models respond faster. Additionally, because it is a large model, running on OpenAI infrastructure, you are subject to their availability and rate limits. OrcaRouter may have its own queues. For highly specialized domains like medicine or law, a fine-tuned model might be better. Evaluate trade-offs carefully.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-5.4-nano",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstreamstructured_outputstool_choicetools| Input / 1M tokens | $0.200 |
| Output / 1M tokens | $1.25 |
| Cache read / 1M | $0.020 |
| 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-nanoOpen @misc{orcarouter_gpt_5_4_nano,
title = {GPT-5.4 Nano API},
author = {OpenAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.4-nano}
}OpenAI. (2026). GPT-5.4 Nano API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.4-nano