xAI's flagship model, offering top-tier intelligence and a massive context window, but at a slower speed and higher cost.
Grok 4 is the premier large language model from xAI, engineered to compete at the highest echelons of artificial intelligence. Positioned as a direct rival to models like GPT-4 Turbo and Claude 3 Opus, Grok 4 distinguishes itself with a unique combination of elite intelligence, an exceptionally large 256,000-token context window, and native real-time web access. This allows it to tackle complex, knowledge-intensive tasks that require both deep reasoning and access to current information. Scoring an impressive 65 on the Artificial Analysis Intelligence Index, it firmly establishes itself as a top-tier model for tasks demanding sophisticated analysis and generation.
However, Grok 4's high intelligence comes with significant trade-offs in performance and cost. With an output speed of just under 34 tokens per second, it is one of the slower models in its class, ranking 73rd out of 101 models benchmarked. This is compounded by a high time-to-first-token (TTFT) of nearly 11 seconds, making it ill-suited for interactive, real-time applications like chatbots where users expect immediate responses. This performance profile suggests Grok 4 is optimized for asynchronous, heavy-duty analytical tasks rather than rapid, conversational exchanges.
The cost structure of Grok 4 is another critical consideration. Priced at $3.00 per million input tokens and a steep $15.00 per million output tokens, it sits at the premium end of the market. The model's pronounced verbosity exacerbates this high output cost; during our Intelligence Index evaluation, it generated 120 million tokens—more than four times the average of comparable models. This tendency to produce lengthy, detailed responses means that even seemingly simple prompts can result in unexpectedly high costs. The total expense to run Grok 4 through our standard intelligence benchmark was a substantial $1,887.94, highlighting the need for careful cost management when deploying this model at scale.
Despite these challenges, Grok 4's technical capabilities are formidable. The 256k context window enables it to process and reason over vast amounts of information, such as entire books, extensive legal documents, or large codebases, all within a single prompt. Furthermore, its multimodal nature—the ability to accept and interpret images alongside text—unlocks a new range of applications, from analyzing charts and diagrams to generating descriptions of visual content. For users whose primary need is maximum analytical power and who can accommodate its slower pace and higher price, Grok 4 presents a compelling, if specialized, option.
65 (11 / 101)
33.7 tokens/s
$3.00 / 1M tokens
$15.00 / 1M tokens
120M tokens
10.94s TTFT
| Spec | Details |
|---|---|
| Model Owner | xAI |
| License | Proprietary |
| Context Window | 256,000 tokens |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Architecture | Proprietary (Likely Mixture-of-Experts) |
| Training Data | Proprietary dataset with real-time web access |
| API Providers | xAI, Microsoft Azure |
| Intelligence Score | 65 (Rank 11/101) |
| Speed | 33.7 tokens/s (Rank 73/101) |
Grok 4 is available through a limited number of API providers. While performance varies slightly between them, the primary differentiator is cost. Our analysis reveals a clear winner for nearly all use cases, making the choice of provider a critical first step in managing your budget.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Cost | xAI | Significantly cheaper on input ($3.00 vs $5.50), output ($15.00 vs $27.50), and blended price ($6.00 vs $11.00). | None. xAI is also the faster provider. |
| Highest Speed | xAI | Delivers ~34 tokens/s, which is over 25% faster than Azure's ~27 tokens/s. | None. xAI is also the cheaper provider. |
| Lowest Latency | xAI | At 10.94s TTFT, it begins generating output almost two seconds faster than Azure (12.60s). | None. It is the superior choice on all performance and cost metrics. |
| Enterprise Integration | Microsoft Azure | Leverages Azure's robust infrastructure, security, compliance, and billing, which may be a hard requirement for large organizations. | Substantially higher cost and slower performance across the board. |
Unless deep integration with the Microsoft Azure ecosystem is a non-negotiable requirement, xAI is the unequivocally better choice for accessing Grok 4, offering superior performance at a much lower price point.
To understand the real-world cost implications of Grok 4's pricing and verbosity, we've estimated the cost for several common tasks. These examples use xAI's lower pricing tier for the most optimistic scenario. Note how the cost is heavily influenced by the verbose output.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Summarize a Research Paper | 10,000 tokens | 2,000 tokens | Academic or R&D analysis requiring a detailed summary. | ~$0.06 |
| Code Generation & Explanation | 500 tokens | 4,000 tokens | A common developer task, highlighting extreme verbosity in comments and explanations. | ~$0.062 |
| Analyze Earnings Call Transcript | 15,000 tokens | 5,000 tokens | Financial analysis using the large context window for key takeaways and sentiment. | ~$0.12 |
| Draft a Long-Form Blog Post | 200 tokens | 8,000 tokens | Content creation where the model's verbosity is leveraged to produce a comprehensive article. | ~$0.121 |
| Customer Support Session | 2,000 tokens | 4,000 tokens | A conversational task showing high cost and potential latency issues for real-time chat. | ~$0.066 |
While individual tasks may seem inexpensive, the costs accumulate quickly, especially for applications involving frequent or verbose outputs. The model's strength in long-form generation comes at a premium, and its use in high-volume, low-margin tasks should be carefully evaluated against cheaper, more concise alternatives.
Grok 4's high output price and natural verbosity make cost management essential. Implementing specific strategies can prevent expenses from spiraling out of control, particularly when scaling up usage. Below are several tactics to control your API spend without sacrificing too much of the model's powerful capabilities.
Directly counter the model's natural verbosity by including explicit constraints in your prompts. This is the most effective way to control output length and, by extension, cost.
This is the single most impactful cost-saving decision you can make. As our analysis shows, accessing Grok 4 via the direct xAI API is dramatically cheaper than via Microsoft Azure.
Unless you have a strict requirement for Azure's enterprise features, choosing xAI will cut your costs nearly in half while also improving performance.
Use the max_tokens parameter in your API calls to set a hard ceiling on the output length. This acts as a crucial safety net to prevent runaway costs from an unexpectedly verbose response. It's better for a response to be truncated than to receive a bill for ten times your expected cost.
Don't use a sledgehammer to crack a nut. Grok 4's intelligence is expensive. For simpler, high-volume tasks, build a routing or cascading system that uses a more cost-effective model first.
Grok 4 is a flagship large language model from xAI. It is designed for high-level performance, featuring top-tier intelligence, a very large 256,000-token context window, multimodal (text and image) input capabilities, and real-time access to web information.
Grok 4 competes in the same intelligence class as GPT-4 Turbo. Key differences include:
Yes. Grok 4 can process both text and images as input. This allows it to perform tasks like describing a picture, answering questions about a chart, or interpreting visual data. However, it can only output text.
Grok 4 excels at tasks that leverage its high intelligence and massive context window. Ideal use cases include in-depth analysis of long documents (e.g., legal contracts, financial reports), complex R&D problem-solving, processing large codebases, and generating detailed, long-form content. It is less suitable for real-time chat or high-volume, low-cost tasks.
The model's large size and complex architecture, which is likely a Mixture-of-Experts (MoE) design, demand significant computational power for each generated token. This results in slower output speeds, higher latency, and increased operational costs, which are passed on to the user as higher API prices.
You can control its verbosity through two main methods. First, use specific instructions in your prompt, such as "Be concise," "Answer in one paragraph," or "Use bullet points." Second, always set the max_tokens parameter in your API call to enforce a hard limit on the output length and prevent unexpected costs.