Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...
Google Gemini 2.5 Pro is a large language model developed by Google, designed for complex reasoning and multimodal understanding. It processes text, image, file, audio, and video inputs, and comes…
Gemini 2.5 Pro can process and understand text, images, files (such as PDFs and spreadsheets), audio, and video. It can generate text outputs up to 65,536 tokens. Its large context window of 1,048,576 tokens allows it to consider entire books, long codebases, or extended chat histories in a single prompt. The model excels at reasoning-heavy tasks such as mathematical problem solving, code generation, and complex document analysis. It can also perform multimodal tasks like describing images, transcribing audio, or answering questions about video content. The model is accessed via OrcaRouter's OpenAI-compatible API, making it easy to integrate into existing workflows.
Choose Gemini 2.5 Pro when your task requires the full 1M token context window, such as analyzing a long legal document, summarizing a multi-hour meeting transcript, or working with an entire code repository. Its strong math reasoning (96.7 on MATH-500) makes it a good choice for complex problem solving or tutoring. Multimodal use cases that mix text, images, audio, or video also benefit from its unified processing. However, for short, simple tasks like basic Q&A or one-turn translation, a smaller model such as Gemini 2.0 Flash or GPT-4o mini will be faster and cheaper. Consider the token count: if input is under 32K tokens, many other models can handle it at lower cost.
The best use cases include tasks that require deep understanding over long contexts: legal document review, academic paper analysis, codebase assistance, and multi-turn conversational agents that need to retain extensive history. Its powerful math reasoning makes it suitable for tutoring, scientific computation, and math competition problems. The multimodal support enables applications like image captioning, video summarization, and audio transcription followed by analysis. It is also used for data extraction from complex documents (PDFs with tables, charts) and for generating long-form structured outputs where consistency over many pages is important.
A cheaper model is advisable when your context fits within a smaller window (e.g., under 128K tokens) or when you do not need multimodal input. If your task is simple classification, short summarization, or format conversion, smaller models like Gemini 1.5 Flash or GPT-4o mini will respond faster and cost significantly less. Also, if latency is critical, smaller models generally have lower inference time. Budget-conscious projects with high volume should evaluate whether the 1M context and math reasoning are actually required. For many everyday tasks, the incremental cost of Gemini 2.5 Pro may not justify the benefits.
Gemini 2.5 Pro achieved a score of 96.7 on the MATH-500 benchmark. MATH-500 is a subset of the MATH dataset consisting of 500 challenging math problems covering topics like algebra, geometry, number theory, and probability. A score of 96.7 indicates that the model solved nearly all problems correctly, demonstrating strong mathematical reasoning and step-by-step solution capability. This places it among the top-performing models for math tasks. Users working on math-heavy applications, such as educational tools, scientific computing, or finance, can rely on this benchmark as evidence of the model's proficiency.
With 1,048,576 tokens of context, Gemini 2.5 Pro offers one of the largest context windows available among production models. For comparison, GPT-4o has a 128,000 token context, Claude 3.5 Sonnet has 200K tokens, and Gemini 1.5 Pro also had 1M tokens in its experimental version. This large context allows the model to process very long documents or entire codebases in one go without needing chunking or external retrieval. However, larger context may increase latency and cost due to the attention mechanism. Users should consider whether their use case truly requires such a large context before selecting this model.
Strengths include top-tier math reasoning (96.7 on MATH-500), strong multimodal understanding, and the ability to handle very long contexts. The model also supports multiple input types (text, image, file, audio, video) which is broader than many alternatives. Limitations include higher cost compared to smaller models, and no specific benchmark scores provided for other domains like coding (e.g., HumanEval), language understanding (e.g., MMLU), or multilingual tasks. Without those figures, users should test on their own data. Additionally, the model's latency may be higher than compact models due to its size and context length, though no specific numbers are available.
Pricing is $2.50 per 1 million input tokens and $15.00 per 1 million output tokens. These are the provider rates from Google, and OrcaRouter applies zero markup. You are billed exactly what Google charges. Input tokens include the prompt text and any multimodal content (images, audio, video) that are tokenized. Output tokens are the generated response. Token counts are reported in the API response. Because there is no markup, the cost is transparent and predictable. For example, a prompt with 100,000 input tokens generating 10,000 output tokens would cost approximately $0.40. Users should monitor token usage to control costs.
Gemini 2.5 Pro is priced at a premium compared to smaller models. For instance, Gemini 1.5 Flash costs $0.15 per 1M input tokens and $0.60 per 1M output tokens, making it more cost-effective for short-context or simple tasks. Similarly, GPT-4o mini is lower cost. However, for tasks requiring the large context or strong math reasoning, Gemini 2.5 Pro may be more efficient than chaining multiple calls to a smaller model. The trade-off is higher per-call cost but potentially better accuracy and fewer retries. Users should estimate total token volumes and evaluate whether the performance gain justifies the expense.
No information is provided about caching or discounted tiers for Gemini 2.5 Pro on OrcaRouter. The pricing is explicitly stated as provider rate with zero markup. Some providers offer input token caching or reduced pricing for lower priority; however, there is no such detail available for this model via OrcaRouter. Users are advised to check OrcaRouter's documentation for any updated pricing or caching options. As of now, the cost is based solely on the token count of each API call at the current per-million rates. For high-volume usage, contact OrcaRouter for potential enterprise agreements.
Cost can be estimated by knowing the number of input and output tokens. The API response includes token usage fields. Input tokens: count all tokens in the prompt (including system message, user messages, and any multimodal tokens). Output tokens: count the generated response. Then calculate: (input_tokens / 1,000,000) * $2.50 + (output_tokens / 1,000,000) * $15.00. For example, a prompt of 500,000 input tokens generating 20,000 output tokens costs (0.5 * $2.50) + (0.02 * $15.00) = $1.25 + $0.30 = $1.55. There are no additional fees. Use the tokenizer provided by Google or estimate using known ratios (e.g., 1 image ~ 258 tokens for Gemini images, but exact tokenization varies).
Make requests to the OrcaRouter API endpoint at https://api.orcarouter.ai/v1, using the model ID "google/gemini-2.5-pro". The API is fully OpenAI-compatible, so you can use the OpenAI Python client or any SDK that supports OpenAI's chat completions format. Set the base URL to the OrcaRouter endpoint, and provide your OrcaRouter API key. Example using Python: client = OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="your_key"). Then client.chat.completions.create(model="google/gemini-2.5-pro", messages=[...]). The response follows the standard structure with choices, usage, etc. No special parameters are required beyond standard chat completions.
The API supports the standard OpenAI chat completion parameters: model, messages, max_tokens, temperature, top_p, stop, frequency_penalty, presence_penalty, and stream. For Gemini 2.5 Pro, max_tokens can be up to 65536. Messages should follow the standard structure with roles like system, user, assistant. To include multimodal content (images, audio, video), use the content array format with type "image_url", "input_audio", etc., as per OpenAI's vision API format. However, support for all multimodal types may vary; refer to Google's documentation for the exact format. Streaming is supported for incremental responses. No other custom parameters are documented for this model via OrcaRouter.
Migration involves changing the model ID in your API call from, for example, "gpt-4" or "claude-3.5-sonnet" to "google/gemini-2.5-pro". Because OrcaRouter uses the same OpenAI-compatible endpoint, no changes are needed to the base URL or authentication. You may need to adjust your messages format if you were using a non-OpenAI provider. Gemini 2.5 Pro supports system messages and the standard roles. For multimodal content, ensure you format images/audio/video using the OpenAI content array format. Test with a small sample to verify behavior and token usage. Also note that context length is larger, but output length can be set up to 65536 tokens. Update your max_tokens accordingly.
Gemini 2.5 Pro is the successor to Gemini 1.5 Pro. While both have a 1M token context window, Gemini 2.5 Pro is reported to have improved reasoning capabilities, reflected in its 96.7 MATH-500 score (1.5 Pro's score is not provided for direct comparison but is generally lower in unofficial reports). Pricing for 1.5 Pro was $1.25 per 1M input and $5.00 per 1M output tokens, making 2.5 Pro twice as expensive on input and three times on output. Gemini 2.5 Pro also supports more input modalities (video and audio added). For applications that need the latest reasoning, 2.5 Pro is preferable; for cost-sensitive tasks, 1.5 Pro remains a strong option.
GPT-4o, by OpenAI, has a 128K token context window, significantly smaller than Gemini 2.5 Pro's 1M. GPT-4o supports text and image inputs, but not audio or video directly. Pricing for GPT-4o is $2.50 per 1M input and $10.00 per 1M output tokens, making output cheaper than Gemini 2.5 Pro ($15.00). Benchmark scores on MATH-500: GPT-4o's score is not provided, but typically high. The choice depends on context length needs: if you require processing very long documents or multimodal inputs with audio/video, Gemini 2.5 Pro is better; for shorter tasks with lower output cost, GPT-4o may be more economical. Both are accessible via OrcaRouter with the same API format.
Claude 3.5 Sonnet by Anthropic has a 200K token context window, much smaller than Gemini 2.5 Pro's 1M. Claude supports text and image inputs (up to 10 images). Pricing for Claude 3.5 Sonnet is $3.00 per 1M input and $15.00 per 1M output tokens, similar to Gemini 2.5 Pro on output but higher on input. No MATH-500 score is provided for Claude 3.5 Sonnet. Claude is known for strong language understanding and safety. Gemini 2.5 Pro offers a larger context, more modalities (audio, video), and verified math reasoning. The choice depends on whether you need the extra context and multimodal support or prefer Claude's language style.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1https://api.orcarouter.aiimport os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="google/gemini-2.5-pro",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_tokensreasoningresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p| Input / 1M tokens | $2.50 |
| Output / 1M tokens | $15.00 |
| Cache read / 1M | $0.250 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
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GET /api/public/models/google/gemini-2.5-proOpen @misc{orcarouter_gemini_2_5_pro,
title = {Gemini 2.5 Pro API},
author = {Google},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/google/gemini-2.5-pro}
}Google. (2025). Gemini 2.5 Pro API. OrcaRouter. https://www.orcarouter.ai/models/google/gemini-2.5-pro