Google's latest multimodal Gemini Pro via OrcaRouter's API at provider rates, zero markup.
Google Gemini Pro Latest is Google’s most recent version of the Gemini Pro model, accessible through OrcaRouter’s OpenAI‑compatible API. It is a multimodal model that accepts text, image, audio,…
The model can process and reason across text, images, audio, video, and files in a single request. For example, you could provide an image of a graph, an audio recording of a meeting, and a text question, and the model will combine information from all sources to generate a coherent answer. It is also capable of generating long‑form text responses of up to 65,536 tokens, making it suitable for tasks like writing detailed reports, creating code templates, or producing extensive documentation. When compared to smaller, cheaper models, Gemini Pro Latest offers superior multimodal grounding and a larger output capacity, but its higher cost means it should be reserved for tasks that genuinely require its advanced capabilities.
For tasks that involve only text and do not require long outputs, a smaller model like Gemini 1.5 Flash or a text‑only variant from Google may be more cost‑effective. If your use case is basic classification, simple Q&A, or short generation (under a few hundred tokens), the higher per‑token cost of Gemini Pro Latest ($4/$18 per 1M tokens) may not be justified. Similarly, if you do not need multimodal input beyond text, a cheaper model that lacks image, audio, or video support will suffice. OrcaRouter provides a range of models so you can select the right trade‑off between capability and cost. Use Gemini Pro Latest when your prompt or expected output is large, multimodal, or requires the latest reasoning improvements.
The model excels in scenarios that combine multiple input modalities and demand long outputs. Typical best use cases include: multimodal reasoning (e.g., explaining a diagram with accompanying audio), audio/video transcription and summarization with follow‑up questions, code generation from screenshots of user interfaces, creating detailed reports that integrate data from images and text files, and interactive applications where the model must process user‑uploaded files. Its high output token limit also makes it suitable for generating entire chapters of a book, lengthy technical documentation, or comprehensive codebases. For any task that benefits from the latest Gemini Pro architecture and broad input flexibility, this model is a strong choice.
Although no specific benchmark scores are provided by the catalog for this particular model, Gemini Pro Latest is understood to incorporate the latest training data and algorithmic improvements from Google DeepMind. Relative to earlier Gemini Pro releases, it likely offers better reasoning, improved multimodal understanding, and reduced hallucination. The large 65,536‑token output window is a significant upgrade over many predecessor models, which were limited to smaller generation sizes. Note that “Latest” refers to the most current stable version of Gemini Pro, not a specific version number. For the exact differences, you should refer to Google’s release notes, but in practice users often report higher quality and consistency compared to Gemini 1.0 Pro.
The catalog facts for google/gemini-pro-latest do not include any specific benchmark numbers. As such, we cannot claim performance on MMLU, GSM8K, HumanEval, or similar standard evaluations. However, Gemini Pro as a family has been evaluated by Google on various benchmarks, and the “Latest” version is expected to be competitive with models like GPT‑4. Without official numbers for this exact version, it is best to evaluate the model on your own data. OrcaRouter allows you to test prompts easily via its API at the provider’s rate, so you can empirically assess quality and speed for your specific use case before committing to production.
Latency figures are not provided in the catalog. In general, output generation speed depends on the length of the response and the current load on Google’s infrastructure. Because Gemini Pro Latest can generate up to 65,536 tokens, longer outputs may take tens of seconds to minutes. Input processing speed also varies with the size and number of multimodal files. For real‑time applications, you may want to test with smaller outputs. OrcaRouter does not add significant latency beyond the underlying Google endpoint; the API proxy is designed to be transparent. If low latency is critical, consider using a faster model like Gemini Flash, though it may offer fewer capabilities.
While powerful, the model has limitations. First, its cost is relatively high: $18 per 1M output tokens can accumulate quickly for long generations. Second, it may still produce inaccurate or hallucinated responses, especially on niche or rapidly changing topics. Third, handling very large multimodal inputs (e.g., long videos or high‑resolution images) may increase processing time and cost. Fourth, because it is a “latest” model with no explicit version, behavior may change over time as Google updates the underlying model. For tasks requiring a stable, immutable model, you may prefer a fixed version like Gemini 1.5 Pro. Lastly, the model is not available through all providers; only OrcaRouter offers the OpenAI‑compatible interface for it at the moment.
With a maximum of 65,536 output tokens, Gemini Pro Latest can produce very long responses in a single generation. This is useful for creating comprehensive reports, extensive code, or maintaining long‑form conversations without truncation. However, it does not necessarily mean the model will always use that many tokens; typical outputs are shorter. The large limit does come with a trade‑off: generating 65K tokens may be slow and expensive. For most practical applications, you can set a lower max_tokens parameter to control cost and speed. The model’s ability to handle long outputs also implies a correspondingly deep attention mechanism, which typically improves reasoning consistency across lengthy texts.
Pricing is exactly the Google provider rate with zero markup: $4.00 per 1 million input tokens and $18.00 per 1 million output tokens. Input tokens include the text portion of your prompt plus any images, audio, video, or files encoded in the request. Output tokens are counted for every token generated in the response. OrcaRouter does not add any platform fees, subscription tiers, or hidden charges. You are billed based on token usage as reported by Google. This transparent pricing makes it easy to estimate costs: for example, a 1,000‑token prompt with a 5,000‑token response would cost ($4 * 0.001) + ($18 * 0.005) = $0.004 + $0.09 = $0.094.
Compared to smaller models like Gemini 1.5 Flash (typically $0.50/$2.00 per 1M tokens), Gemini Pro Latest is more expensive per token. If your workload involves short outputs and text‑only input, you can significantly reduce costs by using a cheaper model. Conversely, for tasks that truly need multimodal inputs or very long outputs, the higher per‑token cost may be justified by the model’s superior capability. There is no caching discount advertised; each token is billed at the standard rate. However, if you repeatedly use the same long prompts, consider them as input tokens each time. OrcaRouter passes through the provider price, so you pay exactly what you would if using Google directly, without any convenience markup.
The catalog facts do not mention any discounts, caching, or volume pricing for google/gemini-pro-latest. OrcaRouter states that pricing is billed at the provider rate with zero markup, implying that the listed prices are the only cost. There is no mention of prompt caching, so each request is billed for the full input tokens, even if earlier responses used similar text. For high‑volume users, it may be worth contacting OrcaRouter directly to inquire about potential enterprise agreements, but no official discount is specified. In the absence of caching, you should optimize your prompts to minimize token usage where possible, for example by reducing multimodal file sizes or truncating conversation history.
You can call the model using any OpenAI‑compatible client library (Python, Node.js, cURL, etc.) by setting the base URL to https://api.orcarouter.ai/v1 and the model parameter to "google/gemini-pro-latest". For example, in Python with the openai package: client = OpenAI(api_key='your_orcarouter_key', base_url='https://api.orcarouter.ai/v1') then response = client.chat.completions.create(model='google/gemini-pro-latest', messages=[{...}], max_tokens=10000). Orcarouter requires an API key, which you can obtain by signing up on their platform. The response format mirrors OpenAI’s, including choices, usage, and finish_reason.
Standard OpenAI chat completion parameters are supported, such as messages, max_tokens, temperature, top_p, frequency_penalty, presence_penalty, stop, and stream. The messages array can include system, user, and assistant roles. For multimodal inputs (images, audio, etc.), you can use the content parts format (if supported by OrcaRouter) or pass base64‑encoded data in a structured way. Refer to OrcaRouter’s documentation for exactly how to send file and media types. The model also respects the max_tokens parameter up to 65,536. Note that not all OpenAI parameters may be implemented; test thoroughly. The API will return token usage in the response under usage.prompt_tokens and usage.completion_tokens.
If you currently use Google’s Vertex AI or Gemini API directly, migration to OrcaRouter is straightforward. You replace your endpoint and authentication method with OrcaRouter’s. Instead of Google’s client libraries, you use OpenAI‑compatible code. Change the base URL to https://api.orcarouter.ai/v1 and the model ID to "google/gemini-pro-latest". You’ll need an OrcaRouter API key. The message format may differ slightly (Google uses “contents” vs. “messages”), so you’ll need to adapt to the OpenAI format. For multimodal content, you may need to encode images or files differently. OrcaRouter’s documentation should provide examples. The pricing remains the same as Google’s direct billing, so your cost per token will not increase. This migration allows you to unify all model calls under a single OpenAI‑compatible interface.
Both models are large, powerful multimodal models, but they have different strengths. GPT‑4 by OpenAI is known for strong reasoning and wide ecosystem support, while Gemini Pro Latest excels on multimodal input diversity (including audio and video natively) and offers a larger output token limit of 65,536 versus GPT‑4’s typical 8,192 or 32,768 in variants. Pricing differs: Gemini Pro Latest costs $4/$18 per 1M tokens, while GPT‑4 Turbo costs $10/$30 (or $20/$60 for GPT‑4). Without direct benchmark data, performance comparisons depend on specific tasks. Gemini Pro Latest may be better for tasks that require understanding of multiple media types simultaneously, while GPT‑4 may have advantages in certain code generation or structured outputs. Testing on your own data is recommended.
Within the Gemini family, this model is the latest “Pro” version, meaning it offers more capability than the smaller Flash variant but less than the Ultra tier (which is not listed here). Compared to Gemini 1.5 Pro (a fixed version), the “Latest” tag means it receives ongoing updates; it may be more current but subject to change. Gemini Flash is cheaper and faster but has fewer multimodal features and a lower output limit. The “Pro Latest” occupies a middle ground: high performance with broad modality support at a moderate price point (mid‑range compared to GPT‑4 and Claude 3 Opus). For users who want the newest improvements without the highest cost, this is a balanced choice.
Anthropic’s Claude 3 models (Haiku, Sonnet, Opus) also support multimodal input and long outputs. Claude 3 Opus has a 200K context window and up to 4,096 output tokens (or longer with extended features). Gemini Pro Latest has a smaller context window (exact max input not provided) but a much larger output limit (65,536 tokens). Pricing for Claude 3 Opus is $15/$75 per 1M tokens, significantly higher than Gemini Pro Latest ($4/$18). Claude 3 Sonnet is closer in price. In terms of safety and alignment, Claude models are known for handling harmful content with more caution. Gemini Pro Latest may be better for very long generations, while Claude may excel on nuanced conversation. Both are accessible via OrcaRouter, allowing you to choose based on task and budget.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1https://api.orcarouter.aifrom openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="google/gemini-pro-latest",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)max_tokensresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_pinclude_reasoningreasoning| Input / 1M tokens | $4.00 |
| Output / 1M tokens | $18.00 |
| Cache read / 1M | $0.400 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/google/gemini-pro-latestOpen @misc{orcarouter_gemini_pro_latest,
title = {google/gemini-pro-latest API},
author = {google},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/google/gemini-pro-latest}
}google. (n.d.). google/gemini-pro-latest API. OrcaRouter. https://www.orcarouter.ai/models/google/gemini-pro-latest