Grok 4.5 is xAI's flagship model — its smartest to date, with frontier performance across coding, knowledge work, and STEM. Built on the 1.5-trillion-parameter V9 foundation and trained alongside the Cursor coding editor, it serves a 500K-token context window and accepts text, image, and file inputs with text output. It emphasizes strong agentic coding with notable token efficiency — resolving software-engineering tasks in far fewer output tokens than comparable frontier models — and is priced aggressively for high-volume production use.
Grok 4.5 is a multimodal large language model created by xAI. It is designed to handle very long inputs, with a context window of 500,000 tokens. This means it can process around 400,000 words of…
Grok 4.5 accepts three input modalities: text, images, and files. Text can be included naturally in the user message. Images can be provided as URLs or as base64-encoded data in the content array, following the OpenAI multimodal format. Files are supported through the same mechanism: users can attach binary data (e.g., PDFs, Word documents) as part of the request. The model processes these inputs together, allowing it to reason across different information types. For example, a prompt can contain a scanned contract (image) along with questions about specific clauses, and Grok 4.5 will extract text from the image and answer accordingly. This multimodal capability eliminates the need to pre-process files before sending them to the API, simplifying pipeline design.
The context window of 500,000 tokens means Grok 4.5 can consider up to half a million tokens of input and output combined in a single request. A token is roughly equivalent to 0.75 words for English text, so the model can handle around 375,000 to 400,000 words. This allows users to input entire documents, long conversations, or large datasets without splitting them into chunks. The model maintains coherence across the entire context, enabling tasks like summarizing a book chapter, analyzing a full code repository, or keeping a multi-turn dialogue intact. However, longer inputs increase cost proportionally because pricing is per token. For the best balance, users should send only the necessary context, but the large window reduces the need for data truncation.
Grok 4.5 excels at tasks requiring deep understanding of long or multimodal content. Key examples include: extracting information from hundreds of pages of research papers, analyzing images of charts and tables together with related text, reviewing legal documents for specific clauses, summarizing lengthy meeting transcripts, and debugging large software projects by examining full log files. The model can also handle complex reasoning chains that rely on earlier parts of a prompt. It is less suited for very simple or one-shot tasks where a smaller, cheaper model would suffice—for instance, generating short email responses or straightforward classification. Users should evaluate whether the task genuinely needs the large context window to justify the higher per-token cost compared to smaller models available on OrcaRouter.
Grok 4.5 is priced at $2.00 per 1M input tokens and $6.00 per 1M output tokens. If your use case does not require the 500k context window or multimodal input, you may save costs by using a smaller model available through OrcaRouter. Examples of tasks better suited to cheaper models include: simple classification or extraction from short snippets, single-turn question answering over small documents, basic sentiment analysis, and generating very short completions. Also, if your application needs low latency for high-volume production traffic, a smaller model may offer faster response times. Evaluate the trade-off: for tasks where the added context and multimodal capability bring no advantage, the extra cost per token is unnecessary.
Specific benchmark scores for Grok 4.5 have not been publicly released by xAI at this time. The model is intended to be a highly capable large language model, but without standardized evaluation numbers, users should assess its performance through their own testing on representative tasks. Factors that influence real-world performance include prompt design, task complexity, and the quality of input data. Given its large context window, the model may perform particularly well on tasks that require retaining information over extremely long sequences. We recommend running benchmark evaluations on your specific use cases using the OrcaRouter API to measure accuracy, latency, and output quality.
Latency for Grok 4.5 is not publicly specified by xAI. However, because the model has a large parameter count and supports a 500k context window, inference time will generally be longer than that of smaller models, especially when processing long inputs. Users can expect higher time-to-first-token and total generation time compared to models with fewer parameters. To optimize, keep input prompts as concise as possible while still providing necessary context. If low latency is critical for your application—such as real-time chat—you may want to test the response speed via the OrcaRouter API with typical input sizes before committing to production use. OrcaRouter does not add significant overhead; the main latency is from the underlying model inference.
Grok 4.5's primary strength is its large 500k context window, enabling coherent reasoning over very long sequences. It also benefits from multimodal input, allowing it to process images and files directly. These features make it powerful for document analysis, research, and any task requiring extended context. Limitations include higher cost per token compared to smaller models, and likely higher latency. Additionally, since xAI has not disclosed benchmark scores, users cannot rely on third-party ranking comparisons. The model's behavior on highly specific domains (e.g., medical or legal) should be validated through testing. Like all LLMs, it may occasionally produce inaccurate or hallucinated content, especially on ambiguous prompts. Always review outputs for critical applications.
Grok 4.5 is priced based on the number of tokens processed. Input tokens cost $2.00 per 1 million tokens, and output tokens cost $6.00 per 1 million tokens. This is the provider rate set by xAI, and OrcaRouter passes it through with zero markup. Billing is usage-based: you pay only for the tokens you actually send and receive. The 500,000-token context window means that a full context prompt (500k input tokens) would cost $1.00 for input alone ($2.00 * 0.5M). Output tokens are billed at three times the input rate. To estimate costs, calculate the total input tokens (including system message, user messages, images and files encoded as tokens) and expected output length. Use the tokenizer provided by OrcaRouter to count tokens before sending.
No. OrcaRouter charges exactly the provider rate for Grok 4.5: $2.00 per 1M input tokens and $6.00 per 1M output tokens. There are no hidden fees, no per-request surcharges, and no markup. Your billing will reflect the raw token usage multiplied by these rates. OrcaRouter may offer API key management and access without additional cost. The only charges are for the compute consumed by the model. Be aware that tokens used for multimodal inputs (images, files) are counted at the same input rate—there is no separate pricing for different modalities. Always verify your token count from the API response to reconcile usage.
Given a 500k context window, the cost of processing a full prompt can be significant. For a single request using 500k input and 1k output tokens, the input cost is $1.00 and output cost $0.006, total ~$1.006. If you send many such requests, costs add up quickly. However, for tasks that benefit from not chunking, the ability to perform a single large query may save engineering time and increase accuracy. Compare this to a smaller model with a shorter context window that would require multiple calls and manual stitching, potentially incurring higher overhead. The cost per token is fixed; the value comes from the context window size and multimodal capability. Users should estimate total monthly tokens and model accordingly.
OrcaRouter does not currently advertise a dedicated cache system for Grok 4.5 responses. Pricing is based on the tokens billed by xAI per request; if you send the same prompt multiple times, each request is billed at full price. There is no discount for repeated or cached outputs. To reduce costs, avoid sending identical or near-identical prompts. You can implement your own caching layer on the client side—for example, store common query results locally and only call the API when a new or modified prompt is needed. Note that input tokens are always charged, even if the output is trivial. For repeated patterns, batch similar inputs together where possible.
To use Grok 4.5, send a POST request to https://api.orcarouter.ai/v1/chat/completions. Set the model parameter to "grok/grok-4.5". The request body follows the OpenAI chat completion format, including messages array with roles (system, user, assistant). For multimodal input, include content parts of type "text" and "image_url". Example (Python): import openai client = openai.OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="YOUR_KEY") response = client.chat.completions.create(model="grok/grok-4.5", messages=[{"role":"user", "content":[{"type":"text","text":"Describe this image:"}, {"type":"image_url", "image_url":{"url":"https://example.com/pic.jpg"}}]}]) print(response.choices[0].message.content) Ensure your API key has access to the grok/grok-4.5 model. Use the same endpoints as OpenAI for streaming, function calling, and other features.
The OrcaRouter API for Grok 4.5 supports standard OpenAI parameters: 'model', 'messages', 'max_tokens', 'temperature', 'top_p', 'frequency_penalty', 'presence_penalty', 'stop', 'stream', 'n', and 'seed'. The 'max_tokens' parameter limits the length of the generated response. 'temperature' controls randomness (0=deterministic, 2=very random). 'top_p' implements nucleus sampling. 'frequency_penalty' and 'presence_penalty' reduce repetition. For streaming, set 'stream' to true to receive tokens incrementally. The 'n' parameter allows generating multiple completions. 'seed' enables deterministic outputs if reproducibility is needed. Note that the model's context window is 500k tokens total (input + output combined). To avoid errors, ensure your input tokens plus requested max_tokens do not exceed that limit. OrcaRouter will return an error if the prompt is too long.
Migrating existing OpenAI-compatible code to call Grok 4.5 via OrcaRouter requires only a change to the base URL and model name. Instead of https://api.openai.com/v1, use https://api.orcarouter.ai/v1. Replace the model identifier with "grok/grok-4.5". Your API key should be the one provided by OrcaRouter. All other parameters and message formats remain the same. For libraries like the openai Python package, update the client initialization: `client = openai.OpenAI(base_url="https://api.orcarouter.ai/v1", api_key=ORCAROUTER_KEY)`. There is no need to modify the message structures, streaming logic, or response handling. Test with a small prompt to confirm connectivity and response format. OrcaRouter also supports the same error codes and rate-limiting headers as OpenAI, making migration straightforward.
Grok 4.5, GPT-4, and Claude are all large language models but differ in key aspects. Grok 4.5 offers a 500k token context window, which is larger than GPT-4 Turbo (128k) and Claude 3.5 Sonnet (200k). This makes Grok 4.5 better suited for very long documents or conversations without chunking. Grok 4.5 also supports image and file input, similar to GPT-4 Vision and Claude’s multimodal capabilities. Pricing: Grok 4.5 is $2/$6 per 1M tokens, GPT-4o is $2.50/$10, Claude 3.5 Sonnet is $3/$15. So Grok 4.5 is cheaper per token for output but identical or slightly cheaper for input versus GPT-4o and Claude. Latency and accuracy vary by use case; without published benchmarks, direct performance comparison is not possible. Developers should test Grok 4.5 against alternatives on their specific tasks.
OrcaRouter provides access to many models besides Grok 4.5. For tasks that do not need the 500k context window, cheaper options include GPT-4o mini ($0.15/$0.60), Claude 3 Haiku ($0.25/$1.25), and Llama 3.1 70B ($0.59/$0.79). For multimodal tasks, GPT-4o and Claude 3.5 Sonnet are alternatives with different pricing and context sizes. For extremely long context, Gemini 1.5 Pro offers 1M tokens but at higher cost. If you require specific features like function calling or JSON mode, many models support them. Choose Grok 4.5 when the large context and zero-markup pricing outweigh the need for lower per-token costs or specific ecosystem integrations. Always consider the trade-offs in latency, accuracy, and cost for your particular workflow.
Across major providers, Grok 4.5's pricing is competitive for its context window size. Input: $2.00/M tokens vs GPT-4o ($2.50), Claude 3.5 Sonnet ($3.00), Gemini 1.5 Pro ($3.50). Output: $6.00/M vs GPT-4o ($10.00), Claude 3.5 Sonnet ($15.00), Gemini 1.5 Pro ($10.50). Grok 4.5 is cheaper on both input and output compared to those models. However, smaller models like GPT-4o mini or Claude 3 Haiku are significantly cheaper. The value proposition of Grok 4.5 lies in its 500k context window—if you do not need that, a cheaper model is better. Additionally, OrcaRouter's zero-markup policy ensures you pay exactly xAI's listed price. No other gateway fees are added, making Grok 4.5 one of the most cost-effective options for long-context multimodal tasks.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="grok/grok-4.5",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltyinclude_reasoninglogprobsmax_tokenspresence_penaltyreasoningresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_logprobstop_p| Input / 1M tokens | $2.00 |
| Output / 1M tokens | $6.00 |
| Cache read / 1M | $0.500 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
@misc{orcarouter_grok_4_5,
title = {Grok 4.5 API},
author = {xAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/grok/grok-4.5}
}xAI. (2026). Grok 4.5 API. OrcaRouter. https://www.orcarouter.ai/models/grok/grok-4.5