Grok 4.6 is SpaceXAI's smartest model to date, with frontier performance across coding, knowledge work, and STEM. It is the current flagship of the Grok line — listed as "Latest" in xAI's own developer docs — and succeeds Grok 4.5 with the same 500K-token context window, the same text / image / file input surface, and the same base pricing. It supports configurable reasoning effort, native tool calling, structured outputs, and the full sampling surface (temperature / top_p / seed / logprobs / penalties), so it drops into existing integrations unchanged. As a first-class OpenAI Responses model on api.x.ai it plugs directly into agent frameworks and tool-calling loops without a translation layer. Pricing is tiered by prompt length: requests above 200K input tokens bill at double the base rate. Use Grok 4.6 as the high-capability tier for complex coding agents, research, and multi-step automation where quality matters more than cost.
SpaceXAI: Grok 4.6 is a large language model from the provider listed as Grok, and it is available on OrcaRouter through an OpenAI-compatible API. The model has a context window of 500,000 tokens,…
Grok 4.6 accepts three input modalities: text, image, and file. That means you can send a plain-text prompt, attach an image for visual context, and include a file for document-level reasoning in the same request, subject to OrcaRouter and provider request limits. The model's context window is 500,000 tokens, so the combined size of your text, image, and file inputs can be large, though the exact number of tokens per image or file depends on the provider's encoding. Output is returned through the standard chat-completion response. OrcaRouter does not mark up the provider's token price, so each request is billed at $2.00 per 1M input tokens and $6.00 per 1M output tokens. If you are building an application that switches between text and image inputs, the same model id works across both use cases. You should confirm the maximum file size and request timeout with OrcaRouter before relying on large attachments. For simple text-only calls, no extra setup is required.
Common fits for Grok 4.6 are tasks that need a long context plus high-accuracy reasoning. The 500,000-token window is useful for summarization, question answering, and analysis over large documents, code repositories, or multi-part conversations. The 94.9 GPQA Diamond score indicates strong performance on graduate-level scientific reasoning questions, which can be relevant to research assistants, tutoring tools, or evaluation pipelines. The text, image, and file input modalities make it possible to analyze a chart, a scanned table, and an accompanying report in one pass. For simpler tasks, a smaller or cheaper model might be the better choice; Grok 4.6 is intended for work where the extra context and benchmark performance justify the token price. You should measure quality on your own examples before committing to any model. The provided facts do not include coding or instruction-following benchmarks, so test those separately. If your workload can be answered from a short prompt, the 500,000-token window is not a benefit. Use a sample set that represents your real user requests.
Not every prompt needs 500,000 tokens or a 94.9 GPQA Diamond score. If your workload is short, single-turn, or only requires basic extraction, a cheaper model may reduce cost while returning adequate answers. The main trade-off is token price: Grok 4.6 is priced at $2.00 per 1M input and $6.00 per 1M output tokens at the provider rate. If most of your requests use small inputs and simple instructions, the per-request cost of a lower-priced model may be much lower. Also consider image and file input. If you only send plain text, you are paying for multimodal capability you may not use. A good approach is to run a sample of your production prompts through Grok 4.6 and a cheaper open alternative, then compare output quality, latency, and cost before selecting a default. OrcaRouter's OpenAI-compatible API makes switching model ids straightforward. The cheaper model may also return faster, depending on provider infrastructure. That matters for interactive applications with strict response-time targets.
GPQA Diamond is a benchmark composed of graduate-level multiple-choice questions in science fields such as physics, chemistry, and biology. It is designed to be difficult for general internet search, so a model cannot simply retrieve the answer. Grok 4.6 reports a score of 94.9 on GPQA Diamond. That number is a single benchmark score, not a guarantee of performance on every domain. A 94.9 means that, on the evaluated set, the model answers most questions correctly, but it still leaves room for errors. When interpreting the score, consider that GPQA evaluates reasoning and knowledge in a multiple-choice format; your tasks may involve open-ended generation, code, or multimodal input, which require separate validation. OrcaRouter does not alter the model's weights, so the benchmark result is not affected by the API layer. The question set is often described as Google-proof because finding the answer through a normal search is not straightforward. Treat the score as a signal about domain knowledge, not a general intelligence score.
The 94.9 on GPQA Diamond is the only benchmark figure provided for Grok 4.6. It is a high result relative to the 100-point scale, but it should not be used as a universal quality measure. If your application resembles GPQA's format, this score is one useful signal. For general reasoning, coding, mathematics, or multimodal tasks, the provided facts do not include a benchmark. You should construct an evaluation set from your own production data and compare Grok 4.6 with other models on your metric of choice. Because the model accepts 500,000 tokens, long-context benchmarks may be more relevant than multiple-choice science questions. If you are reporting the model's performance to stakeholders, cite only the verification source from the provider and avoid claiming scores that were not supplied. OrcaRouter provides access to the model but does not publish independent benchmark results for it. The actual score can vary by version and evaluation methodology. Check the provider's documentation for the exact dataset.
No specific latency or throughput figures are provided for Grok 4.6. Real-world speed will depend on the provider's serving infrastructure, request size, load, and the number of output tokens. A 500,000-token input can be expected to take more time to process than a short prompt, simply because the model must attend to the entire context. Output generation time also increases with the number of generated tokens, usually at a rate determined by the model and provider. To measure latency for your workload, send representative requests through OrcaRouter's OpenAI-compatible API and record end-to-end time, including connection time and token stream time. Because OrcaRouter bills at provider rates without markup, the API layer does not add a per-request premium, but it also cannot control provider-side response times. If latency is critical, run tests during your expected peak hours. You may also compare streaming response behavior across models. A high number of concurrent requests will affect observed latency.
The provided facts do not list explicit limitations for Grok 4.6. That means you should establish limits empirically. The only benchmark supplied is GPQA Diamond, so areas such as code execution, multilingual fluency, safety behavior, and instruction following are not covered by a provided score. Image and file inputs are supported, but the quality of vision and document reasoning is not backed by a published number in the information given. The 500,000-token context window is a capacity, not a promise that every token in the middle will be attended to equally; long-context models can still struggle with needle-in-a-haystack tasks. The model is also priced at $2.00 per 1M input tokens and $6.00 per 1M output tokens, so long prompts can become expensive. You should test with adversarial cases, verify outputs, and keep human review for high-stakes decisions. The provider may also enforce rate limits that are not described in the facts. Plan for graceful handling of timeouts and retries.
Grok 4.6 is priced at $2.00 per 1M input tokens and $6.00 per 1M output tokens. These are provider rates, and OrcaRouter bills them with zero markup. Input tokens include the text, image, and file content that you send, as counted by the provider's tokenization. Output tokens are the text generated by the model in response. There is no additional fee from OrcaRouter in the provided facts. Your total bill is the sum of input and output token usage multiplied by these rates. The large 500,000-token context means a single long document can consume many input tokens, so consider whether you need the full context for every call. If you do, the per-token price is transparent. If you do not, a shorter prompt will lower your invoice. OrcaRouter does not mark up the provider rate, so budgeting is straightforward. Token counts are reported in the API response, so you can audit your usage per request. This makes cost monitoring easier when you scale from development to production.
OrcaRouter passes through the provider rate: $2.00 per 1M input tokens and $6.00 per 1M output tokens, with zero markup. That means OrcaRouter does not add a percentage on top of the model's published price, based on the facts you have. You are billed for the tokens the provider counts as input and output. If you send a request with text, an image, or a file, those inputs contribute to the input token count. The model id for this configuration is grok/grok-4.6, and the OpenAI-compatible base URL is https://api.orcarouter.ai/v1. In practice, you can estimate cost by multiplying your average input tokens per request by $2.00 per million and your average output tokens per request by $6.00 per million, then summing across all requests. There are no tiered rates or commitment discounts in the provided facts. Check OrcaRouter's dashboard or documentation for current billing details. Any differences in the final invoice should come from the provider's token count, not from a surcharge added by OrcaRouter.
The provided facts do not mention prompt caching for Grok 4.6. Without a stated cache discount, you should assume each request is billed at the standard input rate of $2.00 per 1M input tokens and the standard output rate of $6.00 per 1M output tokens. If OrcaRouter or the provider introduces caching later, a cache hit might be billed differently, but that would be a new fact. For cost optimization, focus on what you can control: reduce unnecessary tokens in your prompt, avoid repeating long prefixes unless they are functionally needed, and design your application to reuse results rather than re-sending the same content. Because the context window is 500,000 tokens, a large prompt can be expensive if you send it many times. You may also use shorter prompts for tasks that do not need the full context. Confirm current caching and billing behavior in OrcaRouter's documentation before building an assumption into your cost model. This is especially important for batch workloads.
To call Grok 4.6 through OrcaRouter, use an OpenAI-compatible API client. Set the base URL to https://api.orcarouter.ai/v1 and the model id to grok/grok-4.6. For example, in a common OpenAI SDK, you would configure the client with the base URL and your OrcaRouter API key, then send a chat completion request with the model set to grok/grok-4.6. The messages payload contains your prompt; because the model accepts text, image, and file inputs, you can include those in the message format supported by the API. No special OrcaRouter-specific SDK is mentioned in the facts, so most existing code that talks to an OpenAI-compatible endpoint can be adapted by changing the endpoint and model name. OrcaRouter does not alter the model provider, so the model behavior you receive is the same as the provider's Grok 4.6 response. Use HTTPS for all requests, and keep your API key in a secure environment. Streaming and non-streaming calls should use the same base URL.
Because the endpoint is described as OpenAI-compatible, you can use the typical parameters available in OpenAI-style chat completions. That includes messages, model, temperature, max_tokens or max_completion_tokens, top_p, and other sampling controls supported by the provider. The exact set of accepted parameters depends on the provider's implementation, so you should refer to the API response for unsupported fields. For multimodal input, the message content is usually structured as an array that can include text and image parts; file input may also be represented as an attachment depending on OrcaRouter's API. The model id grok/grok-4.6 is used in all requests. Context and token usage appear in the response under the standard usage fields. If a parameter does not apply to this model, OrcaRouter or the provider may ignore it or return an error, so test your configuration with a small request before processing high volume. Keep in mind that the response format may include the same fields you already parse. Validate them against your schema before production.
Yes, because OrcaRouter exposes Grok 4.6 through an OpenAI-compatible API. The migration steps are the same as switching to any OpenAI-compatible model: change the base URL to https://api.orcarouter.ai/v1, set the model to grok/grok-4.6, and keep your message structure. If your application was built for text-only chat, you can continue to use it as-is, because text is a supported input modality. If you want to use image or file inputs, extend the message format to include those media parts. The response shape should remain familiar, with the generated text and usage metadata. OrcaRouter bills at provider rates, so the migration does not introduce a markup. Still, verify your application's error handling, because the model may produce different content, token counts, or refusal patterns. Run a small parity test comparing old and new endpoints before changing production traffic. Include at least one long-context test and one image test if those are supported in your workflow. This will catch format issues early.
The main differences are context size, benchmark score, and price. Grok 4.6 has a 500,000-token context, accepts text, image, and file inputs, and scores 94.9 on GPQA Diamond. It costs $2.00 per 1M input and $6.00 per 1M output tokens at provider rates. A smaller or cheaper model is likely to have a shorter context and lower benchmark score, but it may be faster and less expensive for simple tasks. If your workload fits comfortably in a smaller context and does not need graduate-level scientific reasoning, the cheaper model may be the better economic choice. If you need long context, multimodal inputs, and the benchmark strength of Grok 4.6, the higher token price can be justified. You can compare on your own data by using OrcaRouter's OpenAI-compatible API to send the same prompt to multiple model ids and measuring quality and cost. Your chosen metric should reflect the outcome you actually care about.
The provided facts do not include benchmark scores or prices for other long-context models, so a direct numeric comparison is not possible. What is known is that Grok 4.6 provides a 500,000-token context window, $2.00 per 1M input and $6.00 per 1M output tokens, and a 94.9 GPQA Diamond score. Other long-context models may have different context limits, token prices, or benchmark results. When comparing, evaluate the context length your task actually requires; a 500,000-token limit is useful only if your prompts need that much data. Also compare input and output pricing, because two models with the same context length can have very different costs. For multimodal work, check whether the other model also accepts text, image, and file inputs. Finally, measure end-to-end quality on your own documents rather than relying only on headline scores. Consider whether the alternative offers the same OpenAI-compatible integration simplicity through OrcaRouter. OrcaRouter lets you test multiple model ids without changing your client.
Start with your objective requirements. Does your task need the 500,000-token context window? Does it benefit from the 94.9 GPQA Diamond score? Do you need text, image, and file inputs? If all three are required, Grok 4.6 is a candidate. Then compare the cost: $2.00 per 1M input and $6.00 per 1M output tokens. Another model may have a lower price but a shorter context or weaker benchmark. You should also estimate the volume of input and output tokens for your workload, since a high-performing model can be expensive when used with long prompts. Run representative examples through OrcaRouter using each model id, compare output quality, and calculate the price per successful task. If the quality difference is small for your domain, the lower-cost model may be preferable. If Grok 4.6 reduces downstream errors, the higher token price may be worth it. Errors include rework time, moderation overhead, and user-facing failures. Quantify them when you can.
GPQA Diamond is a multiple-choice science benchmark. A score of 94.9 indicates strength on that specific evaluation, but it does not cover every production task. Code generation, safety behavior, factual grounding, instruction following, and long-context comprehension are all separate concerns. The model also supports image and file input, but no vision benchmark is provided. If you choose Grok 4.6 because of the benchmark score, you should validate it on your actual prompts. There is also a cost risk: the $2.00 per 1M input token rate means that using the full 500,000-token context can be expensive, especially if you send many requests. If your task is simpler, a lower-priced model might meet your needs. Keep the benchmark as one data point, and maintain an evaluation harness that includes edge cases relevant to your application. The model may still fail on ambiguous or adversarial inputs. A small evaluation set is better than no evaluation set. Review your results before deployment.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="grok/grok-4.6",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltyinclude_reasoninglogprobsmax_tokenspresence_penaltyreasoningreasoning_effortresponse_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.
What developers are saying this week
@misc{orcarouter_grok_4_6,
title = {Grok 4.6 API},
author = {SpaceXAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/grok/grok-4.6}
}SpaceXAI. (2026). Grok 4.6 API. OrcaRouter. https://www.orcarouter.ai/models/grok/grok-4.6