Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...
Claude Opus 4.5 is Anthropic’s flagship language model, built for tasks that benefit from deep reasoning, large context windows, and high output lengths. It processes text, images, and file uploads,…
Claude Opus 4.5 excels at complex reasoning tasks that require careful step‑by‑step logic, such as mathematical proofs, legal analysis, and multi‑hop questions. Its training emphasizes factual consistency and resistance to hallucinations, making it a strong choice for domains where accuracy is critical. The model also demonstrates advanced coding abilities, including writing efficient algorithms, debugging intricate code, and translating between programming languages. In creative writing, the model can maintain narrative consistency over long outputs, and it can handle nuanced instructions for style and tone. When combined with file and image inputs, it can analyze charts, extract text from scanned documents, and answer questions about visual content. These capabilities make it suitable for enterprise automation, research assistance, and high‑stakes decision support scenarios.
Because Claude Opus 4.5 is priced at $5.00 per million input tokens and $25.00 per million output tokens, it is more expensive than many smaller or distilled models available through OrcaRouter. For tasks that do not require deep reasoning or large context—such as simple text classification, basic summarization of short texts, or straightforward chat—a lighter model can deliver adequate results at a lower cost. Consider using a cheaper model when your use case involves a high volume of short prompts, no image or file processing, and tolerance for slightly lower accuracy. For example, a customer support bot answering common questions may not need the full power of Opus 4.5. Conversely, when correctness and depth matter more than speed or cost, Opus 4.5 is the appropriate choice. Always benchmark your specific task against alternative models to find the best cost‑performance trade‑off.
Like all large language models, Claude Opus 4.5 has limitations. It can produce incorrect or outdated information (hallucinations), especially for niche or highly specialized topics where training data may be scarce. The model’s knowledge cutoff depends on the version—you should verify the cutoff date via Anthropic’s documentation. It also may exhibit biases present in its training data. Performance degrades when the model is pushed to the boundaries of its 200K context window; retrieval of information near the beginning of a very long prompt may be less reliable than from the middle. Additionally, the model does not support real‑time browsing, code execution, or direct database querying—those capabilities require integration with external tools. For tasks that demand continuous updating or dynamic data retrieval, you will need to build a pipeline that feeds fresh information into the prompt.
Claude Opus 4.5 achieves a score of 88.9 on the MMLU‑Pro benchmark. MMLU‑Pro is a more challenging variant of the Massive Multitask Language Understanding dataset, designed to test a model’s world knowledge and reasoning across 57 subjects including science, law, history, and mathematics. The benchmark requires the model to select the correct answer from multiple choices after processing a question or prompt. A score of 88.9 indicates that Claude Opus 4.5 performs strongly on this test, outperforming many earlier models. However, benchmarks do not capture every real‑world scenario—for instance, they typically do not test long‑context handling, multi‑modal inputs, or instruction following in open‑ended tasks. Use the MMLU‑Pro score as one indicator of general‑purpose reasoning capability, but evaluate the model on your own specific tasks for a complete picture.
Latency for Claude Opus 4.5 depends on the length of the input and output tokens, as well as the underlying provider infrastructure. Because it is a large model, processing very long prompts (close to 200K tokens) will increase time to first token. Output generation is autoregressive, so generating 64,000 tokens will take longer than a short answer. Throughput is also influenced by concurrent requests and rate limits set by Anthropic and OrcaRouter. For production deployments, you should test with realistic prompt lengths and request volumes to determine end‑to‑end latency. Streaming support via OrcaRouter’s API allows you to receive tokens as they are generated, which can improve user experience. If low latency is a priority, consider whether a smaller, faster model can meet your requirements for the majority of requests.
Claude Opus 4.5’s strength in the MMLU‑Pro benchmark (88.9) reflects its robust knowledge base and logical reasoning. It generally performs well on tasks requiring multi‑step deduction, such as solving mathematical word problems or interpreting legal scenarios. The model also tends to produce clear, well‑structured responses that are easy to parse. However, no single benchmark is definitive. The model may underperform on tasks requiring precise numerical calculations or very recent factual knowledge (depending on its training cutoff). It can also struggle with tasks that inherently require external tools, such as retrieving real‑time data. Additionally, adversarial prompts designed to confuse the model may reduce accuracy. Users should treat benchmark scores as directional guidance and conduct their own evaluations—especially for domain‑specific applications—to understand where the model excels and where it may need augmentation.
Claude Opus 4.5 is billed at the provider’s rate with zero markup on OrcaRouter. The price is $5.00 per 1 million tokens for input (the text, images, and files you send to the model) and $25.00 per 1 million tokens for output (the text the model generates). There are no additional per‑request fees or subscription costs—you pay only for the tokens consumed. Because the model supports up to 200,000 input tokens per request, a single large prompt can cost up to $1.00 in input tokens (at 200K tokens * $5/M). Outputs up to 64,000 tokens may cost up to $1.60 per generation. These are maximums; typical usage will be lower. The zero‑markup pricing means you pay exactly what Anthropic charges, without any increase from OrcaRouter.
Input and output tokens are billed differently, so the ratio of prompt length to generated text significantly affects total cost. For tasks that require long input (e.g., analyzing a 100‑page PDF) but generate a short summary, input cost will dominate. Conversely, tasks that generate long outputs (e.g., writing a full article) from a short prompt will be driven by output cost. There is no separate pricing for image or file processing—those modalities are billed as token equivalents per the provider’s conversion rates. For high‑volume applications, even small savings per call add up. Evaluate whether a cheaper model (e.g., Claude Haiku or a smaller open‑source model) can achieve acceptable quality for your specific task. If you are processing many short queries, the input cost per call may be very low, but output costs still apply.
The provided facts do not mention any caching or discount options specifically for Claude Opus 4.5. OrcaRouter bills at the provider’s rate with zero markup, meaning the price you see ($5/$25 per million tokens) is what you pay. Whether caching of prompts or responses is available depends on OrcaRouter’s current feature set; you should check OrcaRouter’s documentation for any caching mechanisms that could reduce redundant input costs. In general, caching can lower costs if you repeatedly send the same prompt (e.g., system instructions or a fixed document). Without caching, every token in every request is billed. For predictable workloads, consider batching requests or reusing identical system messages to minimize input token volume. No special pricing tiers have been announced for this model.
No. OrcaRouter bills Claude Opus 4.5 at the provider’s exact rate with zero markup. The price you see—$5.00 per million input tokens and $25.00 per million output tokens—is the total cost. There are no platform fees, monthly minimums, or per‑request surcharges. However, you will still be responsible for any applicable taxes (e.g., VAT) depending on your jurisdiction. OrcaRouter may have its own rate limits that could affect production usage, but these are not the same as cost add‑ons. Always review OrcaRouter’s pricing page for the most up‑to‑date information, as provider prices (and thus the billed amount) may change over time.
You access Claude Opus 4.5 through OrcaRouter’s OpenAI‑compatible API. Set your base URL to https://api.orcarouter.ai/v1 and include your OrcaRouter API key in the Authorization header. The model ID is "anthropic/claude-opus-4.5". You can send a standard chat completion request with a messages array that includes system, user, and assistant roles. Example Python request using the OpenAI SDK: ```python import openai client = openai.OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="YOUR_KEY") response = client.chat.completions.create( model="anthropic/claude-opus-4.5", messages=[{"role": "user", "content": "Explain quantum computing in simple terms."}], max_tokens=1024 ) print(response.choices[0].message.content) ``` Adjust parameters like temperature, top_p, and max_tokens as needed.
When you call Claude Opus 4.5 through OrcaRouter, you can use many standard OpenAI‑compatible parameters. Key ones include: model (set to "anthropic/claude-opus-4.5"), messages (array of role/content objects), max_tokens (up to 64,000), temperature (0–2, default 1), top_p (0–1), frequency_penalty, presence_penalty, stop sequences, and stream (true/false). Note: Not all parameters supported by Anthropic’s native API may be exposed through OrcaRouter’s interface. For example, some advanced features like pre‑filling assistant responses or using the Anthropic‑specific content block format may require adaptation. Always refer to OrcaRouter’s documentation for the exact mapping. For image and file inputs, you can include them as part of the content array using the standard multimodal format (e.g., with image_url or text blocks).
If you currently use Anthropic’s API directly, migrating to OrcaRouter requires two main changes. First, update your client’s base URL to https://api.orcarouter.ai/v1. Second, replace your Anthropic API key with an OrcaRouter API key. The message format may differ: OrcaRouter expects the OpenAI‑compatible messages structure (roles: system, user, assistant) rather than Anthropic’s native format. You may need to adjust your messages to fit the OpenAI schema. For instance, turn a system prompt into a message with role "system". File and image inputs should be formatted as content blocks with type "image_url" or "text". Test with a few representative calls to ensure behavior matches. OrcaRouter’s zero‑markup pricing means your costs remain the same as direct Anthropic billing, but you gain the convenience of a single API endpoint for multiple providers.
Claude Opus 4.5 is Anthropic’s largest and most capable model, positioned above Claude Sonnet and Claude Haiku in the product line. While Sonnet and Haiku offer lower latency and lower cost, Opus 4.5 provides higher accuracy on complex reasoning benchmarks, a larger context window (200K vs. 150K for some earlier versions), and the highest output limit (64K tokens). For tasks that require deep analytical thinking or handling very long documents, Opus 4.5 is the recommended choice. For simpler or higher‑volume tasks, Sonnet or Haiku may be more cost‑effective. The MMLU‑Pro score of 88.9 for Opus 4.5 typically exceeds scores of smaller Claude variants, though exact comparisons depend on the version. If you are currently using Claude 3 Opus, note that Opus 4.5 may offer improvements in instruction following and reduced refusal rates.
Claude Opus 4.5 competes with other frontier models such as OpenAI’s GPT‑4 family and Google’s Gemini Ultra. While direct benchmark comparisons are model‑version‑dependent, Claude Opus 4.5’s MMLU‑Pro score of 88.9 places it in the top tier. Its 200K context window is larger than many alternatives (GPT‑4 Turbo offers 128K), and the 64K output limit is among the highest available. Strengths of Claude Opus 4.5 often cited include detailed and well‑structured responses, strong refusal behavior, and multi‑modal capabilities. Weaknesses may include higher latency than smaller models and a more conservative tone in some responses. The choice between Claude Opus 4.5 and a comparable model should be guided by your specific task, your preference for output style, and integration requirements—especially since OrcaRouter makes it easy to switch model IDs without changing the API endpoint.
When selecting a model through OrcaRouter, consider these factors: task complexity, context length required, output length needed, latency expectations, cost sensitivity, and modality support. Claude Opus 4.5 is best for high‑complexity tasks with long context and high accuracy requirements. For short, simple queries, a cheaper model like Claude Haiku or GPT‑3.5 Turbo may suffice. Also consider the model’s behavior: Claude Opus 4.5 tends to provide thorough, careful answers. If you need quick, creative responses or want to minimize token usage, a more concise model might be better. OrcaRouter’s OpenAI‑compatible API allows you to experiment with multiple models easily—just change the model string. Run A/B tests on your own data to compare quality and cost before committing to a single model for production.
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="anthropic/claude-opus-4.5",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_tokensreasoningresponse_formatstopstreamstructured_outputstemperaturethinkingtool_choicetoolstop_ktop_p| Input / 1M tokens | $5.00 |
| Output / 1M tokens | $25.00 |
| Cache read / 1M | $0.500 |
| Cache write / 1M | $6.25 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/anthropic/claude-opus-4.5Open @misc{orcarouter_claude_opus_4_5,
title = {Claude Opus 4.5 API},
author = {Anthropic},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/anthropic/claude-opus-4.5}
}Anthropic. (2025). Claude Opus 4.5 API. OrcaRouter. https://www.orcarouter.ai/models/anthropic/claude-opus-4.5