An elite model from OpenAI that pairs top-tier intelligence with exceptional speed, making it a powerhouse for complex tasks, albeit with a significant cost for verbose outputs.
GPT-5.1 Codex mini (high) emerges as a formidable contender in the AI landscape, representing a specialized offering from OpenAI. The 'Codex' designation signals a strong aptitude for code-related tasks, while the 'mini (high)' suffix suggests a model that is both more efficient than a full-scale flagship and highly tuned for performance. Our benchmarks confirm this positioning, revealing a model that achieves a rare combination of elite intelligence and remarkable speed. With an Artificial Analysis Intelligence Index score of 62, it ranks #4 out of 134 models, placing it firmly in the top echelon of reasoning and problem-solving capabilities, far surpassing the average score of 36.
Performance is a standout characteristic. GPT-5.1 Codex mini (high) clocks in at an impressive 161.3 tokens per second, making it one of the fastest models we've tested. This high throughput is complemented by a low time-to-first-token (TTFT) of just 8.26 seconds via the OpenAI API, ensuring a responsive and fluid user experience in interactive applications. This speed makes it an excellent candidate for real-time use cases, such as live code completion, dynamic data visualization, or agentic systems that require rapid decision-making. While Microsoft Azure also provides access, its performance lags behind OpenAI's native offering in both output speed and latency.
However, this premium performance comes with a carefully structured cost. The input price of $0.25 per million tokens is standard and competitive. The output price, on the other hand, is a steep $2.00 per million tokens—two and a half times the average. This asymmetric pricing model has profound implications for application development. It heavily penalizes verbosity; the model's tendency to generate 71 million tokens during our intelligence evaluation (more than double the average) means that unconstrained outputs can quickly become expensive. The total evaluation cost of $159.19 serves as a stark reminder of how costs can accumulate on large-scale, output-heavy tasks.
Equipped with a massive 400k token context window and multimodal capabilities—accepting and generating both text and images—GPT-5.1 Codex mini (high) is built for complexity. It can analyze entire codebases, digest lengthy legal documents, or create visual assets from textual descriptions. The ideal use case for this model involves tasks that demand its high intelligence and speed but where the output can be constrained. It excels at analysis, summarization, and function calling, where the value of the insight justifies the cost and the output length is naturally limited. For developers, this model is a precision instrument: incredibly powerful when used correctly, but requiring careful handling to manage its operational cost.
62 (#4 / 134)
161.3 tokens/s
$0.25 / 1M tokens
$2.00 / 1M tokens
71M tokens
8.26 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 |
| Architecture | Transformer-based (specifics not disclosed) |
| Specialization | Code generation, complex reasoning |
| API Providers | OpenAI, Microsoft Azure |
Choosing a provider for GPT-5.1 Codex mini (high) is less about price—since OpenAI and Microsoft Azure offer identical rates—and more about performance and ecosystem integration. Our benchmarks reveal a clear winner for raw speed, but enterprise needs may point toward the alternative.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Raw Performance | OpenAI | Significantly faster output (161 vs 136 t/s) and much lower latency (8.26s vs 13.61s) in our benchmarks. | Lacks the deep enterprise compliance and support structures of Azure. |
| Enterprise Integration | Microsoft Azure | Offers seamless integration with the Azure ecosystem, robust security, data privacy controls, and enterprise-grade support. | Noticeably slower performance compared to the native OpenAI API. |
| Simplicity & Early Access | OpenAI | Direct access from the model creator, which often means getting the latest features and updates first. Simpler to get started for developers and startups. | Fewer built-in tools for enterprise governance and private networking. |
| Lowest Cost | Tie | Both providers have identical pricing for input ($0.25/M) and output ($2.00/M). | Cost is not a differentiating factor; the choice must be made on other criteria. |
Performance metrics are based on our independent benchmarks across multiple regions and times. Your results may vary based on geography, server load, and specific API configurations.
To understand the real-world cost implications of GPT-5.1 Codex mini (high)'s pricing structure, let's model a few common scenarios. The key takeaway is how the volume of output tokens dramatically impacts the final cost, making input-heavy tasks far more economical than output-heavy ones.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Code Generation & Debugging | 2k tokens (code, error log) | 1k tokens (fix, explanation) | A typical developer interaction. | ~$2.05 |
| Summarizing a Research Paper | 10k tokens (document text) | 500 tokens (bullet points) | An input-heavy, output-light task. | ~$3.50 |
| Verbose Conversational Agent | 20k tokens (chat history) | 20k tokens (agent replies) | A chat-based application with long responses. | ~$45.00 |
| Large Document Analysis | 100k tokens (legal contract) | 5k tokens (key clauses, risks) | A large context, high-value analysis. | ~$35.00 |
| Image Generation from Prompt | 500 tokens (detailed prompt) | 1 image (~1k output tokens) | A multimodal generation task. | ~$2.13 |
The model is highly cost-effective for tasks that are input-heavy but require concise outputs, like summarization and analysis. However, its cost escalates dramatically in conversational or generative scenarios where output token counts are high, making it crucial to manage verbosity to maintain budget control.
Given the premium price on output tokens, managing costs for GPT-5.1 Codex mini (high) is essential for production use. The goal is to leverage its intelligence and speed without incurring runaway expenses. The most effective strategies focus on minimizing the number of expensive output tokens the model generates.
The most direct way to control output cost is to instruct the model to be brief. By adding constraints to your prompt, you can significantly reduce the number of tokens it generates.
Use the max_tokens parameter in your API call as a hard ceiling on output length. This acts as a safety net to prevent unexpectedly long and expensive responses.
Employ a model cascade or chain-of-thought where a less expensive model handles initial, simpler tasks. This reserves GPT-5.1 Codex mini (high) for the final, most complex step.
Many applications receive the same or similar user queries repeatedly. Caching the model's responses for these queries can eliminate a significant number of API calls.
GPT-5.1 Codex mini (high) is an advanced AI model from OpenAI. The name suggests it's part of the next-generation GPT-5 family, with 'Codex' indicating a specialization in understanding and generating programming code. 'Mini' implies it is a more computationally efficient version than a full flagship model, while '(high)' suggests it has been specifically tuned for high performance and capability within its size class.
Historically, OpenAI has used the 'Codex' moniker for models that are fine-tuned on a massive dataset of public source code from GitHub and other sources. This training gives them exceptional abilities in tasks related to programming, such as writing new code from a natural language prompt, debugging existing code, translating code between different languages, and explaining what a piece of code does.
While its intelligence and speed are more than sufficient for a chatbot, its cost structure makes it a risky choice. The combination of a high $2.00/M output token price and a natural tendency towards verbosity means that an unconstrained, conversational application could become prohibitively expensive very quickly. It is better suited for specialized, high-value bots where conciseness can be enforced or the cost is justified.
This is a strategy known as asymmetric pricing. It reflects the underlying computational costs: processing input tokens (ingestion) is generally less resource-intensive than generating new tokens (inference). By pricing output higher, providers encourage use cases that are analytical and transformative (e.g., summarizing a large document into a few key points) rather than purely generative and verbose. It shifts the economic incentive toward input-heavy, output-light tasks.
Multimodality means the model can process and generate information in more than one format (or 'modality'). For GPT-5.1 Codex mini (high), this means it can accept a combination of text and images as input and can produce both text and images as output. This allows for sophisticated applications like generating a website mockup from a sketch and a description, or answering questions about a chart or diagram.
A 400,000-token context window is exceptionally large. It allows the model to 'remember' and reason over approximately 300,000 words in a single prompt. In practice, this means you can feed it entire books, extensive legal contracts, or full software codebases for analysis. It eliminates the need for complex chunking and embedding strategies for many large-document tasks, simplifying application development and enabling more coherent, context-aware outputs.