Sonnet 4.6 is Anthropic's most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It excels at iterative development, complex codebase navigation, end-to-end project management with...
Claude Sonnet 4.6 is an AI model from Anthropic that processes text, images, and files. It has a context window of 1,000,000 tokens and a maximum output of 64,000 tokens. The model is accessed…
The model accepts three input modalities: text, image, and file. Text inputs can be natural language prompts or structured data. Images are processed as is, allowing the model to perform tasks like visual question answering, diagram interpretation, and OCR. File inputs can include documents in common formats such as PDFs, Word files, or code files. OrcaRouter's API accepts these inputs through standard OpenAI-compatible parameters, making integration straightforward.
With a context window of 1,000,000 tokens, Claude Sonnet 4.6 can process very large documents in a single request. This is useful for tasks like summarizing entire book-length texts, performing code reviews across large repositories, or analyzing extensive legal contracts. The model can maintain coherence across the full window, which reduces the need for chunking and reassembly. OrcaRouter handles the routing without additional engineering overhead.
Claude Sonnet 4.6 demonstrates strong performance on reasoning tasks, as indicated by a 79.9 score on GPQA Diamond, a graduate-level science benchmark. It can write, debug, and refactor code in multiple programming languages. The model's 64K max output allows it to generate long code blocks or detailed explanations. For simpler tasks, a smaller model might be more cost-effective; OrcaRouter offers a range of options.
If your task does not require multimodal input or very long context, a smaller or cheaper model may be more economical. For example, simple text classification, short-form question answering, or basic chat can be handled by models with lower token costs. Claude Sonnet 4.6 is priced at $3/$15 per 1M tokens, which is higher than some lightweight alternatives. Evaluate your typical input/output lengths and complexity before committing.
GPQA Diamond is a benchmark consisting of graduate-level multiple-choice questions in physics, chemistry, and biology. A score of 79.9 means the model answered nearly 80% of these challenging questions correctly. This suggests strong reasoning ability in scientific domains. However, benchmark scores do not guarantee performance on all real-world tasks. The figure is provided as a reference point; OrcaRouter users can test the model on their own data.
Latency depends on input length, output length, and request load. While no specific speed figures are available for this model, Anthropic's Claude Sonnet series generally offers faster response times than larger models. Users can expect reasonable throughput for most use cases. OrcaRouter's API supports streaming responses to reduce perceived latency. For high-volume applications, consider testing the model under your own workload conditions.
Based on the GPQA Diamond score of 79.9, the model shows strong performance on complex reasoning tasks. Its 1M token context window enables deep analysis of lengthy documents. Multimodal input allows it to handle images and files alongside text. The 64K max output is useful for generating comprehensive answers. These strengths make it suitable for research, coding, and document-intensive workflows.
Like all language models, Claude Sonnet 4.6 may produce incorrect or hallucinated information, especially on niche topics outside its training data. Its pricing is higher than smaller models, so it is not optimal for trivial tasks. While it handles images, it may not be as accurate on detailed visual tasks as specialized vision models. Users should validate critical outputs, particularly in regulated domains. OrcaRouter provides access but does not modify the model's behavior.
Pricing is $3.00 per 1 million input tokens and $15.00 per 1 million output tokens. This is the provider rate with zero markup. OrcaRouter does not add any extra fees. Input tokens include the prompt and any attached files or images. Output tokens include the generated response. Billing is transparent, and users can estimate costs based on token usage. There are no hidden charges.
Because output tokens are five times more expensive than input tokens ($15 vs $3 per 1M), minimizing output length can reduce costs. For tasks that require long outputs, the price may add up. Compare with smaller models that cost less per token. For high-volume use, evaluate whether the model's performance justifies the expense. OrcaRouter also provides other models at various price points for different needs.
OrcaRouter's pricing is straightforward with no markups. While the provider may implement caching on their end, OrcaRouter does not advertise specific caching discounts for this model. Users should refer to Anthropic's documentation for any cache-related pricing details. The zero-markup structure means you pay exactly the provider rate. For cost control, consider adjusting max_tokens and temperature to reduce output size.
Use the base URL https://api.orcarouter.ai/v1 and the model ID "anthropic/claude-sonnet-4.6". The API is OpenAI-compatible, so you can use any OpenAI SDK or direct HTTP requests. For example, set the model parameter to "anthropic/claude-sonnet-4.6" in your chat completion call. Authentication requires an API key from OrcaRouter. The endpoint supports text, image, and file inputs through the standard message format.
Standard OpenAI parameters such as messages, model, max_tokens, temperature, top_p, frequency_penalty, presence_penalty, and stream are supported. Max_tokens should be set up to 64000 to match the model's limit. For multimodal inputs, include image_url with base64 or URL in the content array. File uploads can be passed as file inputs. OrcaRouter maps these to the underlying provider format automatically.
If you currently use Anthropic's direct API, you can switch to OrcaRouter by changing the base URL and model ID. You do not need to modify your message formatting or authentication logic significantly. OrcaRouter's endpoint is OpenAI-compatible, so you can reuse existing code written for OpenAI's API. Update your config to point to https://api.orcarouter.ai/v1 and set model to "anthropic/claude-sonnet-4.6". Test with a small request first.
Yes, streaming is supported by setting the stream parameter to true. The response will be a stream of chunks like OpenAI's format. Function calling (tools) is also supported; you can define tools in the request and the model can output tool call arguments. OrcaRouter passes these through to the provider. Check the provider's documentation for any limitations on tool use with multimodal inputs.
Claude Opus is generally more capable on complex reasoning and creative tasks but costs more per token. Claude Sonnet 4.6 offers a lower price point ($3/$15 vs higher rates for Opus) while still providing strong performance (79.9 on GPQA Diamond). For tasks that do not require the highest possible accuracy, Sonnet 4.6 may be a cost-effective alternative. Opus may also have a different context window limit; check the specific model versions.
GPT-4o is OpenAI's multimodal model with similar capabilities. Both support text, images, and files. Pricing structures differ; Claude Sonnet 4.6's rates are $3/$15 per 1M tokens, while GPT-4o has its own pricing. Benchmark scores vary by task. For long-context work, Claude Sonnet 4.6's 1M token window gives it an advantage. Neither model is universally superior; the best choice depends on the specific use case and budget.
Open-source models like Llama 3 or Mixtral may have lower per-token costs if self-hosted, but they often have smaller context windows and lower benchmark scores (e.g., GPQA Diamond). Claude Sonnet 4.6 provides a 1M token context and strong reasoning out of the box. Self-hosting requires infrastructure, while OrcaRouter provides instant access. For many teams, the managed API route saves time and ensures reliability.
Use Claude Sonnet 4.6 when you need a model that can handle very long inputs (up to 1M tokens) and output up to 64K tokens, without sacrificing reasoning quality. It is a good match for scientific document analysis, codebase understanding, and multimodal tasks that combine text and images. If your task is simple or short, consider a cheaper model. OrcaRouter provides access to both options, so you can experiment.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1https://api.orcarouter.aifrom openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="anthropic/claude-sonnet-4.6",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_tokensoutput_configreasoningresponse_formatstopstreamstructured_outputstemperaturethinkingtool_choicetoolstop_p| Input / 1M tokens | $3.00 |
| Output / 1M tokens | $15.00 |
| Cache read / 1M | $0.300 |
| Cache write / 1M | $3.75 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/anthropic/claude-sonnet-4.6Open @misc{orcarouter_claude_sonnet_4_6,
title = {Claude Sonnet 4.6 API},
author = {Anthropic},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/anthropic/claude-sonnet-4.6}
}Anthropic. (2026). Claude Sonnet 4.6 API. OrcaRouter. https://www.orcarouter.ai/models/anthropic/claude-sonnet-4.6