A top-tier multimodal model from OpenAI, offering exceptional intelligence and speed with a massive context window, balanced by moderate pricing and high verbosity.
GPT-5.1 (high) represents a significant leap forward in the landscape of large language models, positioning itself as a premier offering from OpenAI. This model distinguishes itself not just through raw intellectual horsepower but through a finely tuned balance of speed, massive context processing, and advanced multimodality. It is designed for developers and enterprises tackling complex, high-stakes problems that demand nuanced understanding, sophisticated reasoning, and the ability to synthesize information from vast and varied sources. With the ability to process both text and images and generate them in kind, it unlocks a new class of applications, from detailed visual analysis to the creation of rich, illustrated content.
In our comprehensive benchmarking, GPT-5.1 (high) achieved a remarkable score of 70 on the Artificial Analysis Intelligence Index, placing it at an elite rank of #4 out of 101 models. This score, significantly above the class average of 44, underscores its advanced capabilities in areas like logic, reasoning, and comprehension. However, this intelligence comes with a notable characteristic: extreme verbosity. During the intelligence evaluation, the model generated a staggering 81 million tokens, nearly three times the average of 28 million. This tendency to provide exhaustive, detailed responses is a double-edged sword. While beneficial for tasks requiring thoroughness, it can lead to runaway costs and information overload if not carefully managed through precise prompting and parameter controls.
Performance-wise, GPT-5.1 (high) is a powerhouse. It delivers an output speed of approximately 125 tokens per second, placing it among the fastest models in its class and well ahead of the average (71 t/s). This rapid generation makes for a fluid user experience in many applications. The trade-off for this speed appears in its latency, or time to first token (TTFT), which stands at a high 23.69 seconds. This means users will experience a noticeable pause before the model begins generating its response, a critical factor to consider for real-time, interactive applications. For asynchronous tasks like report generation or batch processing, this delay is less of a concern, but for a chatbot, it could be a deal-breaker.
From a cost perspective, GPT-5.1 (high) presents a complex picture. The input price of $1.25 per million tokens is moderately priced and more affordable than the class average of $1.60. The output price of $10.00 per million tokens sits exactly at the class average. The danger lies in the combination of this 8-to-1 output-to-input price ratio and the model's high verbosity. A short prompt can easily trigger a long, expensive response. Our own evaluation on the Intelligence Index cost a total of $859.06, a testament to how quickly costs can accumulate when the model is allowed to generate text freely. This makes cost-control strategies not just a recommendation, but a necessity for any production deployment.
70 (4 / 101)
124.7 tokens/s
$1.25 / 1M tokens
$10.00 / 1M tokens
81M tokens
23.69 seconds
| Spec | Details |
|---|---|
| Model Owner | OpenAI |
| License | Proprietary |
| Context Window | 400,000 tokens |
| Knowledge Cutoff | September 2024 |
| Input Modalities | Text, Image |
| Output Modalities | Text, Image |
| Intelligence Index Score | 70 / 100 |
| Intelligence Rank | #4 / 101 |
| Output Speed | ~125 tokens/s |
| Latency (TTFT) | ~24 seconds |
| Input Price | $1.25 / 1M tokens |
| Output Price | $10.00 / 1M tokens |
Choosing a provider for GPT-5.1 (high) is a subtle decision, as both benchmarked providers, OpenAI and Databricks, offer nearly identical pricing and performance. The differences are marginal, meaning the best choice often depends more on your existing cloud ecosystem and specific priorities than on a clear-cut performance winner.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Latency | OpenAI | Marginally faster time-to-first-token in our tests (23.69s vs. 31.01s). | The difference is small and may not be a deciding factor for asynchronous workloads. |
| Highest Throughput | OpenAI | Slightly faster output speed at 125 tokens/s compared to Databricks' 120 t/s. | A 4% speed advantage that is unlikely to be perceptible to end-users. |
| Lowest Price | Tie | Both providers have identical list prices for input ($1.25/M) and output ($10.00/M) tokens. | No cost advantage either way. Choice may depend on negotiated rates or bundled platform services. |
| Platform Integration | Databricks | The ideal choice for teams already embedded in the Databricks Data Intelligence Platform, allowing for a unified workflow. | Adds another vendor and potential integration complexity if you are not already a Databricks customer. |
Performance metrics are based on benchmarks conducted by Artificial Analysis. Blended price is a weighted average reflecting typical usage patterns. Your actual costs and performance may vary based on workload and negotiated pricing.
Theoretical prices per million tokens can be abstract. To make the cost of using GPT-5.1 (high) more tangible, we've estimated the expense for several real-world scenarios. These examples highlight how the model's high verbosity and 8:1 output-to-input price ratio directly impact your budget, even for seemingly small tasks.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Customer Support Email Triage | 500 tokens | 2,000 tokens | Summarizing and categorizing an incoming customer email. | ~$0.021 |
| Complex Document Summary | 100,000 tokens | 5,000 tokens | Condensing a 75-page report into a multi-page executive summary. | ~$0.175 |
| Creative Content Generation | 200 tokens | 4,000 tokens | Writing a detailed blog post from a short prompt. | ~$0.040 |
| Multi-turn Chatbot Session | 3,000 tokens (total) | 15,000 tokens (total) | A 10-turn conversation where the model's responses are much longer than the user's. | ~$0.154 |
| Image Analysis & Description | 1,500 tokens (image + prompt) | 8,000 tokens | Analyzing a complex diagram and providing a highly detailed textual explanation. | ~$0.082 |
The consistent theme across all workloads is the disproportionate cost of output. In every scenario, the cost of the generated text far exceeds the cost of the prompt. This is a direct consequence of the model's natural verbosity combined with the 8x price premium on output tokens, making output management the single most important factor in controlling costs.
Given GPT-5.1 (high)'s tendency for high verbosity and expensive output, proactive cost management is essential. Implementing specific strategies in your application logic can prevent runaway expenses and ensure a sustainable operational budget. Below are several techniques to control token generation and optimize spending without sacrificing quality.
The most direct way to control costs is to set a hard limit on the number of tokens the model can generate. By using the max_tokens parameter in your API call, you can prevent the model from producing excessively long and expensive responses.
max_tokens value (e.g., 150) to guarantee brevity.You can guide the model toward shorter answers through careful prompt engineering. Explicit instructions to be brief are often respected and can significantly reduce output token count.
The 400k context window is a powerful tool, but filling it unnecessarily is a costly mistake. Instead of passing entire documents for every query, use more efficient techniques.
When you need structured data (like JSON), prompting the model to generate it as plain text is unreliable and token-inefficient. Use the API's built-in function calling or tool use features instead.
GPT-5.1 (high) is a premier, proprietary large language model from OpenAI. It is distinguished by its top-tier intelligence, high-speed output, massive 400k token context window, and multimodal capabilities (processing both text and images).
It ranks among the most intelligent and fastest models available. However, its key differentiators are also its primary trade-offs: its high intelligence is paired with extreme verbosity, and its fast output is preceded by high latency. Its pricing is moderate, but costs can escalate quickly due to its verbose nature.
Multimodality means the model can natively understand and generate more than one type of data. GPT-5.1 (high) can accept a combination of text and images as input and can produce both text and images as output, enabling more complex and creative applications.
It depends on the use case. For real-time, interactive applications like a customer service chatbot, a ~24-second wait for a response can be unacceptable. For asynchronous, backend tasks like generating a report or summarizing a document, this initial delay is often negligible.
The most effective cost-control measures directly target its high verbosity. The two best strategies are: 1) Programmatically enforcing output limits using the max_tokens parameter, and 2) Using precise prompt engineering to explicitly request concise, brief answers.
The performance and pricing between OpenAI and Databricks are nearly identical. OpenAI has a slight edge on latency and speed, but the difference is minimal. The best choice often comes down to non-performance factors, such as existing platform integrations (favoring Databricks) or a desire to work directly with the model's creator (favoring OpenAI).
The "(high)" designation likely indicates that this is a specific variant of the base GPT-5.1 model. This version may be fine-tuned or optimized for higher performance on complex reasoning and intelligence benchmarks, potentially at the expense of other metrics like latency or cost-efficiency.