Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on...
Claude Opus 4.7 is a large language model developed by Anthropic, positioned as their most advanced offering in the Claude family. It is designed for users who need deep reasoning, nuanced…
Based on its purpose in the Claude lineup and the GPQA Diamond score, Claude Opus 4.7 excels at tasks requiring deep reasoning, multi-step analysis, and accurate understanding of complex instructions. Its extremely large context window allows it to maintain coherence over very long conversations or documents. It can handle multi-modal inputs simultaneously, enabling richer interactions. For users who require the highest possible quality on intricate problems — such as graduate-level science, legal reasoning, or advanced coding — this model is designed to deliver.
Given the $5/$25 per million tokens pricing, Claude Opus 4.7 is expensive compared to smaller models like Claude Sonnet or Haiku. If your task is simple (e.g., basic summarization, translation, or classification) and does not require deep reasoning or huge context, a cheaper model will be more cost-effective. Also, if you do not need the full 1M token context — for instance, most requests are under 10K tokens — you pay for unused capacity. Evaluate whether the higher accuracy justifies the cost for your specific use case.
The model is well-suited for advanced research assistance, including literature review, hypothesis generation, and data interpretation. It performs strongly on complex coding challenges such as debugging multi-file projects, writing libraries, or explaining intricate algorithms. Its multi-modal capability makes it effective for diagram analysis, scanned document understanding, and captioning. Additionally, it can handle very long content generation — like creating detailed reports or extended creative writing — while maintaining coherence across thousands of tokens.
Claude Opus 4.7 scores 91.4 on the GPQA Diamond benchmark. GPQA Diamond is a graduate-level multiple-choice reasoning test covering physics, chemistry, biology, and other sciences. It is designed to be difficult for humans and models alike, with expert-level questions. A score of 91.4 indicates strong performance in complex scientific reasoning. This is the only benchmark figure provided; other common benchmarks (like MMLU, HumanEval, or MATH) have not been disclosed in this context.
Like all large language models, Claude Opus 4.7 can sometimes produce plausible-sounding but incorrect answers, especially on niche topics. Its high cost makes it impractical for high-volume, low-complexity tasks. No latency or throughput figures have been provided, so users should expect response times to vary based on input length and server load. Additionally, while it supports images and files, its performance on highly complex visual reasoning tasks (e.g., detailed medical imaging) is not independently verified. Users should test thoroughly for their domain.
With 1,000,000 tokens, Claude Opus 4.7 has one of the largest context windows available on OrcaRouter. For comparison, GPT-4o offers 128K tokens, and Claude Sonnet 4.5 also offers 200K tokens. The larger context allows Opus to handle entire codebases, long transcripts, or extensive research documents in a single prompt. This is a key differentiator for users who need to analyze very long documents without chunking. However, the cost per token is higher, so using the full context window incurs significant expense.
Because benchmarks are aggregate and may not reflect your domain, it is recommended to run your own evaluation with representative data. Use a small set of test prompts that match your expected workload and compare outputs from Claude Opus 4.7 with alternative models. Track accuracy, relevance, and cost per task. OrcaRouter's API makes it easy to swap model IDs for A/B testing. If your task requires high reliability on factual questions, consider adding verification steps in your pipeline.
Pricing follows the provider rate with zero markup: $5.00 per 1 million input tokens and $25.00 per 1 million output tokens. Input tokens include the prompt and any images or files (tokens are counted according to Anthropic's scheme). Output tokens are the generated text. There are no additional gateway fees. Because of the high per-token cost, this model is best suited for tasks where high-quality output justifies the expense. No caching discount or special pricing tier has been announced on this platform.
Claude Opus 4.7 is the most expensive model in Anthropic's lineup. For context, Claude Sonnet (a mid-tier model) typically costs around $3 per million input tokens and $15 per million output tokens, while Claude Haiku (fast/cheap) is around $0.25 and $1.25 respectively. Opus 4.7's pricing of $5/$25 means it is roughly 60% more expensive than Sonnet for inputs and 66% more expensive for outputs. The trade-off is higher reasoning ability and larger context window. Always compare benchmark performance on your specific task before deciding.
No specific caching or batch discount information has been provided for Claude Opus 4.7 on OrcaRouter. The pricing is strictly per-token at provider rates with zero markup. Some providers offer prompt caching where repeated prefixes are charged at a lower rate, but that has not been confirmed for this model via this platform. Users looking to reduce costs should consider optimizing prompt length, using shorter context windows if possible, or switching to a cheaper model for less demanding tasks.
To call Claude Opus 4.7, use OrcaRouter's OpenAI-compatible API endpoint at https://api.orcarouter.ai/v1 with the model ID "anthropic/claude-opus-4.7". The API accepts standard OpenAI chat completions parameters, including messages, temperature, max_tokens, top_p, and stop sequences. The max_tokens parameter can be set up to 128,000. You do not need an Anthropic API key; simply authenticate with your OrcaRouter key. The response format mirrors OpenAI's structure with choices, usage, and object fields.
Standard API parameters include: model (set to "anthropic/claude-opus-4.7"), messages (array of message objects with role and content), temperature (float, suggested range 0-1), top_p (float), max_tokens (integer, up to 128,000), stop (array of strings), frequency_penalty, presence_penalty, and stream (boolean). For image inputs, include an object with type "image_url" in the content array. File inputs can be passed via the multipart form upload or by referencing previously uploaded file IDs, depending on how OrcaRouter handles file support.
Yes, because OrcaRouter exposes an OpenAI-compatible API, you can replace the model ID and base URL in your existing OpenAI client code to call Claude Opus 4.7. For example, in Python with the openai library, set openai.base_url to "https://api.orcarouter.ai/v1" and openai.api_key to your OrcaRouter key, then use model="anthropic/claude-opus-4.7". Note that some Anthropic-specific parameters (like "thinking" or custom stop sequences) may not be available; stick to standard parameters that both APIs support.
Migration requires three steps: (1) Obtain an OrcaRouter API key and add credits. (2) Change your code's base URL to https://api.orcarouter.ai/v1 and update the model parameter to "anthropic/claude-opus-4.7". (3) Adjust any parameters that are not supported by the OpenAI-compatible interface — for example, Anthropic's native API has a 'betas' header, which is not available here. Test with a small number of requests to confirm that the response format matches your expectations. OrcaRouter's documentation provides further guidance.
Claude Opus 4.7 is Anthropic's flagship model, positioned above Sonnet in terms of capability. While specific benchmark comparisons beyond GPQA Diamond are not provided, Opus is generally expected to outperform Sonnet on complex reasoning, coding, and long-context tasks. The trade-off is cost: Opus ($5/$25 per million tokens) is significantly more expensive than Sonnet (typically $3/$15). For users whose tasks do not require Opus-level reasoning, Sonnet offers a more economical choice with faster average response times.
Both models are top-tier large language models. GPT-4o, developed by OpenAI, has a context window of 128K tokens — much smaller than Opus's 1M. Pricing for GPT-4o is around $2.50 per million input tokens and $10 per million output tokens, making it cheaper than Opus 4.7. No direct benchmark comparisons are available, but GPQA Diamond scores for GPT-4o have been reported in the low-to-mid 80s by independent evaluators, while Opus scores 91.4. However, performance varies by task; users should evaluate both on their specific dataset.
Data sent through OrcaRouter is processed by Anthropic's underlying API. OrcaRouter acts as a gateway and does not log or store prompt and response contents beyond what is necessary for request routing and billing. Anthropic's own data policy applies: prompts are not used for model training unless explicitly opted in. For sensitive data, review Anthropic's privacy policy and consider using OrcaRouter's data handling options (if available). No fine-tuning or custom model training is offered through this gateway.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1https://api.orcarouter.aifrom openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="anthropic/claude-opus-4.7",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_tokensoutput_configreasoningresponse_formatstopstreamstructured_outputstemperaturethinkingtool_choicetoolstop_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-4.7Open @misc{orcarouter_claude_opus_4_7,
title = {Claude Opus 4.7 API},
author = {Anthropic},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/anthropic/claude-opus-4.7}
}Anthropic. (2026). Claude Opus 4.7 API. OrcaRouter. https://www.orcarouter.ai/models/anthropic/claude-opus-4.7