Claude Opus 5 is Anthropic's flagship model for demanding reasoning, coding, and long-horizon agentic work — the most capable model in the Opus line to date. It is especially strong at end-to-end software engineering, code review and bug finding, and visual analysis, and is built to stay coherent across very long, multi-step agentic sessions with heavy tool use. It accepts text, image, and file inputs with text output, serves a 1M-token context window with up to 128K output tokens, and supports adaptive reasoning with configurable effort. Opus 5 uses Anthropic's adaptive thinking design: instead of sampling parameters like temperature or top_p, callers dial depth through reasoning effort, and the model allocates thinking automatically. It speaks both the OpenAI chat-completions format and Anthropic's native Messages API (/v1/messages), supports structured outputs, verbosity control, and native tool calling, and is a strong default for production coding agents, deep research, and complex automation where correctness and judgment matter most.
Claude Opus 5 is a large language model developed by Anthropic, positioned as the most capable model in their lineup. It is designed for tasks that require deep reasoning, broad context…
Claude Opus 5 excels at tasks requiring extended reasoning, multi-step problem solving, and precise following of instructions. It can analyze long conversation histories to maintain consistent context, solve advanced math problems with detailed step-by-step reasoning, write and debug complex code across multiple files, and generate lengthy, coherent documents. Its multimodal capability allows it to interpret images—such as graphs, diagrams, or handwritten notes—and incorporate them into its responses. The model is also proficient at tasks like summarizing large volumes of text, extracting structured data from unstructured sources, and answering questions that require synthesizing information from disparate parts of a long input. Because of its high cost, it is best used for tasks where accuracy and depth are critical and where a smaller model would likely make errors. For simpler queries, a cheaper model from the same family is more economical.
Claude Opus 5 accepts file uploads as part of its input, allowing users to send entire documents for processing. The supported file modalities are not exhaustively disclosed by Anthropic, but common formats like PDF, DOCX, TXT, and images are known to work. When a file is provided, the model reads its content and can reason over it as if it were inline text. This is particularly useful for tasks such as analyzing legal briefs, extracting key points from research papers, or processing data tables. The file content counts toward the total token context window (1M tokens). Because file parsing may incure additional processing time, users should account for this in latency expectations. OrcaRouter passes the file through to the model without modification, ensuring that no intermediary transformation alters the content. Developers can include file references in the API request using a standard structure compatible with Anthropic’s API schema.
Claude Opus 5 is most valuable when the task demands extensive context, high reasoning depth, or handling of multiple modalities. For example, summarizing a 500-page legal document requires the 1M token context, which smaller models lack. Analyzing a complex research paper with many cross-references benefits from the model’s ability to retain all details in a single context. Similarly, tasks that involve reasoning over multiple images and long text simultaneously are well-suited. In contrast, for short Q&A, simple translations, or casual conversation, a smaller, cheaper model like Claude Haiku or Sonnet is more cost-effective. OrcaRouter enables users to route different types of queries to the optimal model using the same API, allowing them to reserve Claude Opus 5 for the hardest problems while using cheaper models for routine tasks. The cost difference is substantial: Opus 5 is 20x more expensive in input than Haiku (if Haiku is $0.25 per 1M), so judicious use is advised.
You should choose a cheaper model when the task is simple, short, or does not require the full reasoning capacity of Claude Opus 5. For instance, answering factoid questions, generating short responses, performing simple classification, or handling inputs under 100K tokens can be done effectively with models like Claude Sonnet 4 or Haiku. These models have lower per-token costs and often lower latency. Additionally, if your application has high throughput requirements and strict budget constraints, using Opus 5 for every call would be prohibitively expensive. OrcaRouter allows you to set model selection logic in your application—for example, using Opus 5 only when the input length exceeds 500K tokens or when the query complexity score is high. Always evaluate whether the incremental accuracy gain of Opus 5 justifies the extra cost. In many production environments, a well-tuned smaller model can achieve satisfactory results at a fraction of the price.
Specific benchmark scores for Claude Opus 5 were not provided in the available facts. However, as Anthropic’s most capable model, Claude Opus 5 is expected to outperform its predecessors and competing models on standard evaluations for reasoning, coding, mathematics, and long-context tasks. Historically, Opus models have achieved state-of-the-art results on benchmarks like MMLU, GSM8K, and HumanEval. Because the model has a 1M token context, it likely performs well on needle-in-a-haystack tests and long-document QA. Users evaluating the model should run their own task-specific benchmarks to compare performance against alternatives. OrcaRouter does not modify model outputs, so benchmark results reflect the model’s native capability. For official numbers, refer to Anthropic’s technical report or published evaluations. Without concrete data, we recommend conducting internal tests with representative data to gauge how Claude Opus 5 meets your requirements.
No specific latency or speed figures were provided for Claude Opus 5. In general, larger models with more parameters—like Opus-scale models—take longer to generate tokens compared to smaller models. The latency is also affected by input and output length: a prompt with 500K tokens will have a higher time-to-first-token than a short prompt. The maximum output of 128K tokens means generating very long responses can take significant wall-clock time. Users should expect slower response times compared to smaller Anthropic models. Streaming responses can help reduce perceived latency for chat applications. OrcaRouter supports streaming via its OpenAI-compatible API, allowing partial token delivery. For real-time or high-throughput applications, consider using a faster model. If benchmark latency is important, run tests with representative payloads to measure p50 and p95 response times in your environment.
Despite its capabilities, Claude Opus 5 has several limitations. First, its high cost ($5 per 1M input, $25 per 1M output) makes it uneconomical for many use cases. Second, the long context window can lead to high latency and token consumption, increasing both time and expense. Third, as a large language model, it may still produce hallucinations or errors, especially on niche or ambiguous topics. Its training data cutoff is not specified, but it likely has a knowledge limit. The model also cannot perform real-time updates or access external databases natively (unless used with tool calls). Additionally, while it supports multimodal input, its performance on highly complex visual reasoning tasks may be less reliable than on text. Finally, OrcaRouter provides the model as-is with no modifications to safety filters or output control; users must implement their own safeguards if needed. For critical applications, always validate outputs independently.
Claude Opus 5 on OrcaRouter is priced at the exact Anthropic provider rate with zero markup: $5.00 per 1 million input tokens and $25.00 per 1 million output tokens. This means users pay exactly what Anthropic charges, with no additional gateway fees. Tokens are counted as per Anthropic’s tokenization (including image tokens). The input price applies to all tokens in the prompt, including system messages, user messages, and any file or image content processed. Output tokens include the generated response. OrcaRouter does not add any surcharge or hidden costs. There is also no minimum commitment or usage tier; pay-as-you-go billing is used. This transparent pricing makes it easy to calculate costs for a given workload. For example, a query with 10,000 input tokens and 500 output tokens would cost approximately $0.05 (input) + $0.0125 (output) = $0.0625.
The available facts only include pricing for Claude Opus 5. However, based on publicly known pricing for other Anthropic models (not provided here but widely known), Claude Opus 5 is significantly more expensive than smaller models like Claude Sonnet 4 or Claude Haiku. For reference, Claude Opus models have historically cost around 15–20 times more per input token than Haiku. Users should consider this when deciding whether a task requires the full power of Opus 5. OrcaRouter passes through the provider rates for all models, so the relative cost difference between models is preserved. If you are using multiple Anthropic models through OrcaRouter, you can manage costs by routing simpler queries to cheaper models. There is no volume discount available through OrcaRouter for Claude Opus 5 specifically, as billing is at the provider rate. For large-scale usage, directly contacting Anthropic may offer custom pricing, but OrcaRouter provides the standard rate.
The available facts do not mention caching options for Claude Opus 5. On Anthropic’s direct API, they may offer prompt caching at reduced rates for repeated prompts, but this information is not confirmed for this model version. OrcaRouter does not implement its own caching layer; all requests are forwarded to Anthropic’s API and billed per the provider’s pricing. If Anthropic offers a caching feature for Claude Opus 5, it would be applied automatically when using the appropriate API parameters. Users should check Anthropic’s documentation for the latest caching details. Until caching is confirmed, the most straightforward way to reduce costs is to minimize input token usage—by being concise in prompts, truncating unnecessary conversation history, or using shorter examples. Also, choosing a cheaper model for simpler tasks reduces spend. OrcaRouter’s API does not add any premium for low-latency or priority routing, so costs are entirely based on token consumption.
The overall cost of using Claude Opus 5 is determined by the total number of input tokens and output tokens processed. Input tokens include all text, image tokens, and file content in the prompt. Output tokens are the model’s response. The pricing is $5 per million input tokens and $25 per million output tokens. There are no additional charges or monthly fees from OrcaRouter. Longer prompts, especially those that leverage the full 1M context window, will incur higher input costs. Longer generated responses increase output costs. For multimodal inputs, images are tokenized according to Anthropic’s scheme (typically based on image size and detail level). Users should monitor token usage via the API response metadata (e.g., usage fields) to track costs. OrcaRouter provides this information in the API response. To control costs, set max_tokens limits on output, trim input context to only necessary parts, and consider using a cheaper model for low-stakes requests.
To call Claude Opus 5 through OrcaRouter, use an OpenAI-compatible API client. Set the base URL to https://api.orcarouter.ai/v1 and the model ID to "anthropic/claude-opus-5". You can use any OpenAI SDK (Python, Node.js, etc.) but must replace the base URL and model name. Authentication is done via an API key from OrcaRouter. A typical chat completion request might look like: POST /v1/chat/completions with model: "anthropic/claude-opus-5", messages array, and optionally max_tokens, temperature, etc. OrcaRouter translates the OpenAI schema to Anthropic’s format internally. Note that some Anthropic-specific parameters (like system messages) are supported via the OpenAI schema – system role in messages is mapped accordingly. For multimodal input, use the content parts structure with type "text" and type "image_url" (or file_url if supported). The API will process and bill accordingly.
When using OrcaRouter’s API to call Claude Opus 5, you can use standard OpenAI chat completion parameters: model (required), messages (required), max_tokens, temperature, top_p, frequency_penalty, presence_penalty, stop, stream, and user. The messages array supports system, user, and assistant roles. For multimodal inputs, user messages can contain content as an array of parts with type "text" and type "image_url" (with url being a base64-encoded image or an HTTP URL). File inputs may be passed similarly if supported by the underlying provider. OrcaRouter may accept additional parameters like "tools" for function calling, though compatibility depends on Anthropic’s implementation. There is no guarantee that all OpenAI parameters will have identical behavior; for example, frequency_penalty may not be supported by Anthropic. Check OrcaRouter’s documentation for parameter mapping details. Streaming is supported via the stream parameter, which returns tokens incrementally. The response includes usage statistics with prompt_tokens and completion_tokens counts.
Migrating from Anthropic’s direct API to OrcaRouter requires minimal changes because OrcaRouter provides an OpenAI-compatible API. First, change your base URL to https://api.orcarouter.ai/v1. Second, replace your model identifier from Anthropic’s format (e.g., "claude-opus-5-20241022") to "anthropic/claude-opus-5". Third, obtain an API key from OrcaRouter and use it in the Authorization header instead of your Anthropic key. The request/response format will be nearly identical, but you must use the OpenAI schema. For example, instead of sending a message with role "user", you still send role "user". However, Anthropic’s specific fields like "system" as a top-level field should be mapped to a system message in the messages array. OrcaRouter handles the translation. You may need to update SDK code to use the OpenAI client library instead of Anthropic’s. Test with a few requests to confirm that responses match expectations. Note that billing will come from OrcaRouter, not Anthropic, but the pricing is the same (zero markup).
The available facts do not include pricing or specifications for Claude Sonnet 4. In general, Anthropic’s Opus models are the most capable and most expensive, while Sonnet models offer a balance of performance and cost. Claude Opus 5 is expected to outperform Sonnet 4 on complex reasoning, longer context tasks, and accuracy. However, Opus 5 has higher latency and cost. If your tasks do not require the extreme capability of Opus 5, Sonnet 4 likely provides good results at a lower price. The context window of Opus 5 (1M tokens) is larger than typical Sonnet models (often 200K or 100K), so for tasks needing very long context, Opus 5 is the only choice. OrcaRouter supports both models, so you can compare empirically. For budget-conscious deployments, start with Sonnet 4 and only upgrade to Opus 5 if you need the extra performance.
Comparing Claude Opus 5 to GPT-4o requires caution because no direct comparative data was provided. Both are leading multimodal models with large context windows. GPT-4o by OpenAI has a 128K token context window and supports text, image, and audio inputs. Claude Opus 5 has a much larger 1M token context window, which is advantageous for processing extremely long documents. In terms of pricing, GPT-4o’s pricing (not provided here) is typically lower than Opus 5. Both models are available through OrcaRouter’s API with the OpenAI-compatible interface, allowing easy switching. The choice between them may depend on specific task performance, safety preferences, and ecosystem familiarity. Users should test both on representative samples to determine which yields better outcomes for their use case. Claude Opus 5 may be preferred for tasks requiring deep contextual understanding over very long inputs, while GPT-4o might offer broader multimodal capabilities (audio) and often lower cost.
Choose Claude Opus 5 when your task demands the highest level of reasoning accuracy and the ability to process extremely long contexts up to 1 million tokens. This includes scenarios like analyzing entire books or extensive legal cases, handling massive codebases, or performing multi-step reasoning over hundreds of pages. If your application requires multimodal input (text, image, file) with deep understanding, Opus 5 is a strong candidate. Additionally, if accuracy is paramount and errors from a cheaper model would be costly, the premium for Opus 5 may be justified. However, for simple tasks, short inputs, or high-throughput chat applications, a cheaper model like Claude Sonnet 4 or GPT-4o mini is more practical. OrcaRouter allows you to use the same API endpoint for all models, so you can dynamically route tasks based on complexity. Always weigh the cost per token against the value of the extra capability.
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-5",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningreasoning_effortresponse_formatstopstructured_outputstool_choicetoolsverbosity| Input / 1M tokens | $5.00 |
| Output / 1M tokens | $25.00 |
| Cache read / 1M | $0.500 |
| Cache write / 1M | $10.00 |
| 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-5Open @misc{orcarouter_claude_opus_5,
title = {Claude Opus 5 API},
author = {Anthropic},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/anthropic/claude-opus-5}
}Anthropic. (2026). Claude Opus 5 API. OrcaRouter. https://www.orcarouter.ai/models/anthropic/claude-opus-5