Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater...
Google Gemini 2.5 Flash is a multimodal language model developed by Google. It belongs to the Gemini 2.5 series, designed for efficient processing of text, images, audio, and video inputs. The model…
Gemini 2.5 Flash accepts five input modalities: files (e.g., PDFs, documents), images, text, audio, and video. This allows users to provide a rich mix of data in a single request. For example, you could submit a video recording, an accompanying text script, and a PDF reference document, and the model will reason across all modalities. The model processes these inputs natively without requiring separate encoding or chunking for most formats. This multimodal support is especially useful for tasks like video summarization, multi-document analysis, and interactive assistants.
With a context window of 1,048,576 tokens, Gemini 2.5 Flash can ingest entire books, long codebases, or hours of transcribed audio in a single turn. This eliminates the need for sliding window techniques or external memory. Use cases include reviewing an entire software project for bugs, analyzing long legal documents with extensive citations, or summarizing a multi-hour meeting. The large context also enables the model to maintain coherence across very long conversations, though output length is capped at 65,536 tokens.
Based on benchmarks, Gemini 2.5 Flash excels at mathematical reasoning, scoring 93.2 on MATH-500. It also demonstrates strong performance in code generation and understanding due to its large context and multimodal training. The model's ability to handle audio and video natively reduces preprocessing overhead. Its low cost per token makes it attractive for batch processing and real-time applications. However, for tasks requiring extremely high creativity or domain-specific expertise, a specialized or larger model might be preferred.
Gemini 2.5 Flash is already priced competitively at $0.30 per 1M input and $2.50 per 1M output tokens. However, for very simple tasks like classification or short-form text generation, a smaller model like Gemini 1.5 Flash or third-party alternatives may offer lower cost per token. Evaluate your expected token usage and latency requirements. If your workload does not require the full 1M context or multimodal inputs, a text-only model with a smaller context window could reduce expenses. OrcaRouter’s catalog provides options for cost comparison.
MATH-500 is a benchmark consisting of 500 challenging math problems covering algebra, geometry, probability, and number theory. A score of 93.2 means the model correctly solved 93.2% of these problems, indicating strong mathematical reasoning and problem-solving ability. This score places Gemini 2.5 Flash among the top-performing models for math tasks. The benchmark tests both computational accuracy and logical reasoning. Developers working on educational tools, scientific computation, or math-heavy workflows can rely on this score as a reference.
Speed depends on request complexity, token length, and server load. Google has not published specific latency figures for Gemini 2.5 Flash. However, the “Flash” designation indicates optimization for low latency compared to the Pro variant. In typical usage through OrcaRouter, response times are competitive for real-time applications. For high-throughput scenarios, consider batching requests or using streaming outputs. OrcaRouter supports streaming responses via its OpenAI-compatible API, which can reduce perceived latency.
Compared to budget models like GPT-4o mini or Claude 3 Haiku, Gemini 2.5 Flash offers a larger context window (1M vs typically 128K-200K) and multimodal input support. Its MATH-500 score of 93.2 is higher than many similarly priced alternatives. The cost is competitive, especially for output tokens at $2.50 per 1M tokens. However, for tasks focused purely on creative writing or conversational fluency, other models may perform better. Benchmark data for non-math tasks is not provided, so direct comparison is limited.
While Gemini 2.5 Flash performs well on math and code, its performance on other dimensions—like factual recall, nuanced language understanding, or creative generation—is not covered by the provided benchmark. Being a “Flash” model, it may trade off some reasoning depth for speed. The maximum output of 65,536 tokens may be insufficient for generating very long reports or summaries of extremely long inputs. Additionally, the model's training data cutoff and potential biases are not specified; users should validate outputs for critical applications.
Pricing is straightforward: $0.30 per 1 million tokens for input and $2.50 per 1 million tokens for output. These rates are billed at Google’s provider rate with zero markup added by OrcaRouter. No hidden fees or surcharges. For example, processing 10 million input tokens would cost $3.00, and generating 1 million output tokens would cost $2.50. The pricing applies to all supported input modalities. Token counts include all text, image patches (after conversion), and audio/video segments as per Google’s tokenization scheme.
Prompt caching can reduce input costs when the same context is reused across multiple requests. Google Gemini models support automatic caching of repeated prefix tokens. On OrcaRouter, caching behavior follows Google's policies. If your application frequently sends the same system instructions or reference documents, caching may lower effective input token costs. The exact savings depend on cache hit rates. No specific caching discounts are provided in the pricing facts, but users should consult Google's documentation for cache eligibility.
Gemini 2.5 Pro typically costs more per token than Flash (exact prices not provided for Pro), but may yield higher quality on complex reasoning tasks. Flash is designed for applications where speed and economy are prioritized. For high-volume production, the lower per-token cost of Flash can lead to significant savings. However, if a task requires the absolute best accuracy (e.g., in legal or medical reasoning), the incremental cost of Pro might be justified. Evaluate both on a sample of your data to determine the optimal price-performance trade-off.
OrcaRouter does not add markup, so the price per token remains at Google’s provider rate. Bulk discounts may be available directly from Google for enterprises, but these are not reflected in the standard pay-as-you-go pricing listed. OrcaRouter offers a simple per-token model with no minimum commitments. Users can contact OrcaRouter for information about potential volume-based arrangements, though no specific discount structure is provided in the facts.
Call Gemini 2.5 Flash via OrcaRouter's OpenAI-compatible API. Set the base URL to https://api.orcarouter.ai/v1 and the model ID to "google/gemini-2.5-flash". Use any OpenAI SDK or HTTP client. Authentication requires an API key from OrcaRouter. Send a POST request to /chat/completions with the model parameter. Example in python: client = OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="YOUR_KEY"); response = client.chat.completions.create(model="google/gemini-2.5-flash", messages=[{"role":"user","content":"Hello"}]). The API supports streaming, function calling, and other standard OpenAI parameters.
All standard OpenAI chat completion parameters are supported, including: messages (array of message objects), temperature (0-2), top_p, max_tokens (up to 65536), frequency_penalty, presence_penalty, stop, stream (boolean), and logprobs. For multimodal input, use the content structure with text and image_url or file_url parts. Audio and video inputs can be provided as base64-encoded data URIs or URLs depending on file size. The API also supports response_format with JSON mode if needed. Refer to OrcaRouter’s documentation for exact formatting details.
Migration is straightforward because OrcaRouter uses an OpenAI-compatible endpoint. If you are using OpenAI, Anthropic, or other providers, change the base URL to https://api.orcarouter.ai/v1 and your API key to an OrcaRouter key. Update the model ID to "google/gemini-2.5-flash". No changes are needed to message formats, streaming, or other call structures. For users coming from Google’s native Vertex AI or Generative AI SDK, you will need to adapt to the OpenAI message format, but many libraries have conversion utilities.
OrcaRouter exposes standard HTTP error codes: 401 for unauthorized, 429 for rate limiting, 500 for server errors. Rate limits depend on your OrcaRouter plan. Google may also impose its own rate limits per API key. The API returns errors in the OpenAI format. For large requests exceeding the 1M context window or 65K output, you will receive a 400 error. OrcaRouter’s documentation provides specific rate limit tiers. If you hit rate limits, consider retrying with exponential backoff.
GPT-4o mini is a smaller, cheaper model from OpenAI with a context window of 128K tokens (8x smaller than Gemini 2.5 Flash). GPT-4o mini is text-only, whereas Gemini 2.5 Flash supports file, image, audio, and video. On MATH-500, GPT-4o mini scores around 70-80 (no exact figure provided for this comparison), while Gemini 2.5 Flash scores 93.2. GPT-4o mini’s pricing is roughly $0.15/$0.60 per 1M tokens (input/output) – cheaper than Gemini Flash for input but more expensive for output. Choose Gemini Flash for multimodal, long-context tasks; choose GPT-4o mini for very low-cost text-only applications.
Gemini 2.0 Flash (previous generation) had a context window of 1M tokens and similar multimodal support. However, Gemini 2.5 Flash improves math reasoning, as evidenced by a MATH-500 score of 93.2 versus 2.0 Flash’s score (not provided, but likely lower). The pricing for 2.5 Flash is $0.30/$2.50 per 1M tokens, while 2.0 Flash was $0.15/$0.60 per 1M tokens – so 2.5 Flash is more expensive per token. The trade-off is better accuracy. For cost-sensitive projects that do not require high math performance, 2.0 Flash may be preferable. OrcaRouter offers both versions.
Other budget multimodal models include Claude 3 Haiku (200K context, text+image) and Llama 3.2 90B (128K context, text+image). Gemini 2.5 Flash offers the largest context window (1M) and supports audio/video natively, which is rare at this price point. Its MATH-500 score of 93.2 is higher than many comparable models. However, for pure text generation, models like Mistral Small may offer lower latency. The optimal choice depends on your specific modality requirements and whether the larger context justifies the cost.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1https://api.orcarouter.aifrom openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="google/gemini-2.5-flash",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_tokensreasoningresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p| Input / 1M tokens | $0.300 |
| Output / 1M tokens | $2.50 |
| Cache read / 1M | $0.030 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/google/gemini-2.5-flashOpen @misc{orcarouter_gemini_2_5_flash,
title = {Gemini 2.5 Flash API},
author = {Google},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/google/gemini-2.5-flash}
}Google. (2025). Gemini 2.5 Flash API. OrcaRouter. https://www.orcarouter.ai/models/google/gemini-2.5-flash