OpenAI GPT-4.1 Mini: cost-efficient reasoning with 1M context, image input, and strong MATH-500 score
This model is a scaled-down version of OpenAI’s GPT-4.1 family, specifically released on April 14, 2025. It is engineered to deliver strong reasoning and instruction-following performance at a…
It excels at tasks that combine reasoning with large amounts of context, such as document analysis, long-document summarization, code comprehension, and complex multi-step instructions. Its strong MATH-500 score (92.5) indicates proficiency in mathematical problem solving. The model can also handle image-based tasks like describing pictures, extracting text from photos (OCR), and answering questions about visual content. File uploads allow it to work with PDFs, spreadsheets, and other structured data.
You should reach for this model when the task requires robust reasoning, a very large context window, or multimodal capabilities. Cheaper models (e.g., GPT-4.1 mini itself is already the cheap option; but compared to even smaller models like GPT-3.5 Turbo) may lack the accuracy needed for math, logic, or long-range dependencies. If your application demands the full 1M token context or needs to interpret images, this model is a strong fit. For simple text classification or short responses, a lighter model might be more cost-effective.
Consider moving to GPT-4.1 (full) or GPT-4o if your task requires near-perfect accuracy on extremely complex reasoning, such as advanced theorem proving, legal document interpretation with nuance, or creative writing that demands deep stylistic consistency. The mini variant may sometimes produce less precise outputs on edge cases or when following highly specific formatting instructions. Benchmark scores on broader tests (e.g., MMLU) are not provided here, but as a mini model, it likely underperforms the full-sized flagship on certain reasoning domains.
Images are tokenized and processed similarly to how GPT-4o handles them, but with a smaller underlying model. The model can describe image content, answer questions about visual elements, and extract text from images. Files are handled by extracting their textual content (for documents like PDF or DOCX) or by treating them as images if they contain scanned pages. The model is not designed for video or audio input. For file-intensive workflows, the large context window allows many pages or many images to be included in a single prompt.
The model achieved a score of 92.5 on the MATH-500 benchmark, which tests mathematical reasoning across a variety of difficulty levels. This is a strong result, especially for a mini model, and indicates that it can handle algebra, geometry, calculus, and other math topics with high accuracy. MATH-500 is a subset of the MATH dataset. For context, many larger models score in the low-to-mid 90s, so this performance is competitive for cost and size.
No direct benchmarks for general reasoning (e.g., MMLU, GSM8K) were provided, but based on the MATH-500 result, the model is likely within a few percentage points of larger GPT-4 variants on mathematical reasoning. On tasks requiring deep commonsense or creative reasoning, larger models typically maintain an advantage. Users should evaluate on their own datasets to determine if the mini variant meets accuracy requirements. The trade-off is significantly lower cost and latency.
Exact latency figures are not provided, but as a smaller model, openai/gpt-4.1-mini-2025-04-14 generally produces responses faster than its full-sized counterpart. It is optimized for high throughput and low per-request time, especially for shorter outputs. For long generation tasks (near the 32K max output), latency may still be noticeable, but it should remain lower than that of the full GPT-4.1. Network and queue times depend on OrcaRouter’s infrastructure and current load.
The model is not the top performer in every category. It may struggle with highly nuanced or ambiguous instructions, and its knowledge cutoff is implicitly tied to the release date (April 14, 2025). It is not designed for safety-critical decisions without human oversight. Additionally, because it is a mini model, it has fewer parameters than the full version, which can lead to less confident outputs on rare or highly specialized topics. Users running on very long contexts may experience slight degradation in recall of early tokens.
OrcaRouter uses the exact provider rate with zero markup: $0.40 per 1 million input tokens and $1.60 per 1 million output tokens. Input tokens include the full prompt text, any image tokenizations, and file text. Output tokens count only completion tokens. There are no additional per-request fees or hidden costs. This transparent pricing makes it easy to estimate costs for large-scale applications.
Since OrcaRouter adds no surcharge, you pay exactly what OpenAI charges. This is advantageous for high-volume users who might otherwise incur a markup at other intermediaries. The pricing is public and stable, with no extra fees for streaming or batch processing. Note that your total bill will also depend on any image or file tokenization overhead, which adds to input token count.
The full GPT-4.1 model (if available) is likely priced higher—often several times more per token. The mini variant offers a lower price point while still delivering strong performance on many tasks. For example, compared to GPT-4o, GPT-4.1 mini is typically cheaper, though exact pricing of other models is not provided here. If you can tolerate slightly lower accuracy on a subset of tasks, the mini model can dramatically reduce monthly costs. There is no mention of caching discounts, so users should assume standard per-token billing.
Send requests to https://api.orcarouter.ai/v1/chat/completions with the model parameter set to "openai/gpt-4.1-mini-2025-04-14". The API is fully OpenAI-compatible, so you can use the same SDKs (e.g., openai Python library, Node.js) by changing the base URL. Authentication is required via an API key. Example: openai.api_base = "https://api.orcarouter.ai/v1"; openai.api_key = "your-orcarouter-key"; then call openai.ChatCompletion.create with model="openai/gpt-4.1-mini-2025-04-14".
All standard OpenAI parameters are supported: temperature, top_p, max_tokens, n, stop, presence_penalty, frequency_penalty, and logit_bias. Image inputs can be provided via the content array with type "image_url". For file uploads, you can include a file ID (if using OrcaRouter’s file API) or embed file text directly. The max_tokens parameter must not exceed 32,768. Streaming is supported by setting stream=true. Response format is identical to OpenAI’s Chat Completion structure.
If you are currently using OpenAI’s API directly, migration to OrcaRouter simply requires updating the base URL and API key. If you are using another model gateway, check that your request format matches OpenAI’s schema. OrcaRouter does not require any vendor-specific headers or wrappers. For existing codebases using the openai Python library, change openai.api_base to the OrcaRouter endpoint. Ensure you have a valid OrcaRouter account and API key. No other code changes are needed.
The full GPT-4.1 model is expected to have higher benchmark scores across the board, especially on general knowledge and complex reasoning. However, it is presumably more expensive and slower. The mini variant offers a favorable cost-performance ratio for most practical tasks, trading a few percentage points of accuracy for significantly lower latency and cost per token. The mini also shares the same 1M context window and multimodal capabilities, so the differences are mostly in raw intelligence and output quality.
GPT-4o is a flagship multimodal model with broader capabilities, including audio and real-time video. GPT-4.1 mini is a model from a later release (April 2025) and is focused on efficiency rather than being a full successor. Without direct benchmark comparisons, it is reasonable to assume that GPT-4o outperforms the mini on a wide range of tasks, but the mini may be cheaper and faster. The choice depends on whether you need the extra capability of GPT-4o or can accept the mini’s trade-offs.
Claude Haiku is Anthropic’s fast, low-cost model with similar objectives. Both have large context windows (Haiku has 200k tokens, while this model has 1M). The MATH-500 score of 92.5 suggests competitive mathematical reasoning. Without side-by-side benchmarks, users should evaluate both on their specific tasks. This model supports image input directly, whereas Haiku also supports images. Pricing may differ; this model’s $0.40 per million input is relatively low. The preference may come down to ecosystem and style.
GPT-3.5 Turbo is an older, cheaper model with weaker reasoning and no native image support. The GPT-4.1 mini model is a substantial upgrade: it supports images, has a much larger context (1M vs 16K), and scores far higher on math benchmarks. GPT-3.5 Turbo is still useful for very simple, high-volume tasks where even the mini’s cost is too high, but for any task requiring nuanced understanding, this model is vastly superior. If you are currently using GPT-3.5 Turbo, consider upgrading to this model for better accuracy.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-4.1-mini-2025-04-14",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltylogit_biaslogprobsmax_completion_tokensmax_tokensnparallel_tool_callspredictionpresence_penaltyresponse_formatseedservice_tierstopstreamstructured_outputstemperaturetool_choicetoolstop_logprobstop_pweb_search_options| Input / 1M tokens | $0.400 |
| Output / 1M tokens | $1.60 |
| Cache read / 1M | $0.100 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/openai/gpt-4.1-mini-2025-04-14Open @misc{orcarouter_gpt_4_1_mini_2025_04_14,
title = {openai/gpt-4.1-mini-2025-04-14 API},
author = {openai},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4.1-mini-2025-04-14}
}openai. (n.d.). openai/gpt-4.1-mini-2025-04-14 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4.1-mini-2025-04-14