A 14B parameter model from Microsoft, offering a balance of performance and cost for specific tasks.
Phi-3 Medium 14B is a significant offering from Microsoft, positioned as a compact yet capable open-weight language model. With 14 billion parameters, it aims to strike a balance between performance and accessibility, particularly for developers operating within the Microsoft Azure ecosystem. This Instruct variant is designed for conversational and instruction-following tasks, making it a versatile choice for a range of applications where a smaller, more efficient model is preferred over larger, more resource-intensive alternatives.
Our analysis reveals Phi-3 Medium 14B scores 14 on the Artificial Analysis Intelligence Index, placing it below the average of 20 for comparable models. While its raw intelligence score might suggest limitations for complex reasoning, it distinguishes itself by generating remarkably concise outputs, producing 5.3 million tokens during evaluation compared to an average of 13 million. This conciseness, combined with a low latency of 0.43 seconds and a solid output speed of 43 tokens per second on Azure, makes it well-suited for applications requiring quick, direct responses without excessive verbosity.
From a cost perspective, Phi-3 Medium 14B presents a mixed picture. Its input token price of $0.17 per 1 million tokens is somewhat expensive compared to the average of $0.10, and its output token price of $0.68 per 1 million tokens is notably high, significantly exceeding the average of $0.20. This pricing structure results in a blended cost of $0.30 per 1 million tokens (based on a 3:1 input-to-output ratio). Users must carefully manage output generation to keep costs in check, especially for tasks that produce lengthy responses.
The model boasts a substantial 128k token context window, allowing it to process and understand extensive inputs, which is a considerable advantage for tasks like document summarization or long-form content analysis. Its knowledge cutoff is September 2023, ensuring it has a relatively up-to-date understanding of world events and information. Phi-3 Medium 14B is best utilized in scenarios where speed, conciseness, and a large context window are paramount, and where the core task does not demand advanced logical reasoning or highly creative, expansive generation.
14 (#37 / 55 / 14 Billion)
43 tokens/s
$0.17 /M tokens
$0.68 /M tokens
5.3M tokens
0.43 seconds
| Spec | Details |
|---|---|
| Model Name | Phi-3 Medium 14B Instruct |
| Developer | Microsoft |
| License | Open |
| Parameter Count | 14 Billion |
| Context Window | 128k tokens |
| Knowledge Cutoff | September 2023 |
| Model Type | Non-reasoning, Open-weight |
| Primary Provider | Microsoft Azure |
| Intelligence Index Score | 14 (out of 55) |
| Output Speed (Azure) | 43 tokens/s |
| Latency (Azure) | 0.43 seconds |
| Blended Price (Azure) | $0.30 / 1M tokens (3:1 blend) |
| Input Token Price (Azure) | $0.17 / 1M tokens |
| Output Token Price (Azure) | $0.68 / 1M tokens |
Phi-3 Medium 14B is primarily optimized for deployment and performance within the Microsoft Azure ecosystem. Azure offers a managed service that simplifies access and scales the model effectively. However, as an open-weight model, it also provides the flexibility for self-hosting, which can be a strategic choice for specific use cases.
When selecting a provider, consider your priorities: whether it's maximizing cost efficiency, ensuring the lowest latency, or maintaining full control over the deployment environment.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Priority | Pick | Why | Tradeoff |
| Cost Efficiency | Microsoft Azure | Leverages Azure's optimized infrastructure and blended pricing, which can be competitive for balanced workloads. | Higher output token costs can accumulate quickly for verbose applications. |
| Performance (Latency/Speed) | Microsoft Azure | Demonstrates excellent time to first token (0.43s) and solid output speed (43 tokens/s) within Azure's managed environment. | Performance benefits are tied to Azure's infrastructure; self-hosting may require significant optimization to match. |
| Data Privacy & Control | Self-Host | Provides complete control over data, infrastructure, and security, ideal for highly sensitive applications. | Requires significant investment in hardware, maintenance, and operational expertise. |
| Integration & Ecosystem | Microsoft Azure | Seamless integration with other Azure services and Microsoft's developer tools, simplifying development workflows. | Potential for vendor lock-in and reliance on Azure's service availability and pricing structure. |
Note: Pricing and performance metrics are subject to change and can vary based on region, specific Azure SKUs, and real-world usage patterns.
Understanding the real-world cost implications of Phi-3 Medium 14B requires looking beyond raw token prices. Its unique blend of below-average intelligence, concise output, and high output token cost means that different types of applications will incur vastly different expenses. The following scenarios illustrate estimated costs for common tasks, assuming deployment on Microsoft Azure with the observed pricing.
These estimates help contextualize the model's cost-effectiveness for various use cases, highlighting where its strengths (conciseness) can mitigate its weaknesses (high output price) and where costs might unexpectedly escalate.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Scenario | Input | Output | What it represents | Estimated cost |
| Short Q&A | 100 tokens | 50 tokens | Quick factual queries, simple chatbots, interactive prompts. | ~$0.00005 |
| Email Draft | 200 tokens | 300 tokens | Generating short professional communications, internal memos. | ~$0.00024 |
| Document Summarization | 5,000 tokens | 500 tokens | Condensing reports, articles, meeting notes into key takeaways. | ~$0.00119 |
| Code Generation (small) | 1,000 tokens | 800 tokens | Generating functions, code snippets, or script automation. | ~$0.00071 |
| Content Expansion | 300 tokens | 1,500 tokens | Drafting blog post sections, marketing copy, or social media updates. | ~$0.00107 |
| Long-form Q&A | 1,000 tokens | 1,000 tokens | Detailed explanations, complex customer support responses. | ~$000.00085 |
These examples highlight that while Phi-3 Medium 14B's concise output can be a cost-saver for tasks like summarization, its high output token price means that any scenario requiring substantial generation, such as content expansion or detailed explanations, will quickly become more expensive than models with lower output costs. Strategic prompting to minimize output length is crucial for cost management.
Optimizing costs with Phi-3 Medium 14B requires a strategic approach, particularly given its higher output token pricing. By implementing smart prompting techniques and leveraging its specific architectural advantages, developers can significantly reduce operational expenses while still achieving desired outcomes.
Here are key strategies to consider for a cost-effective deployment of Phi-3 Medium 14B:
Given the high output token price, minimizing the length of generated responses is paramount. Design prompts to explicitly request concise, to-the-point answers.
Phi-3 Medium 14B's 128k context window is a powerful feature, but it comes with an input token cost. Use it efficiently.
Recognize Phi-3 Medium 14B's strengths and weaknesses and allocate tasks accordingly. It excels at quick, concise responses but struggles with complex reasoning.
Proactive monitoring of token usage and costs is essential to identify inefficiencies and unexpected spikes.
Phi-3 Medium 14B is a 14-billion parameter, open-weight language model developed by Microsoft. It's designed for instruction-following and conversational tasks, offering a balance of performance and efficiency, particularly within the Azure ecosystem.
It scores 14 on the Artificial Analysis Intelligence Index, which is below the average of 20 for comparable models. This indicates it may not be as capable for complex reasoning tasks as some larger or more specialized models.
Its key strengths include very low latency (0.43s TTFT), concise output generation, a large 128k token context window, and its open-weight nature allowing for flexible deployment and fine-tuning. It's also optimized for Microsoft Azure.
Its main drawbacks are a below-average intelligence score for complex reasoning and a relatively high output token price ($0.68/M tokens), which can make verbose applications costly. Its input token price is also somewhat above average.
Phi-3 Medium 14B features a substantial 128,000 token context window, allowing it to process and retain a large amount of information within a single interaction.
No, as a non-reasoning model with a below-average intelligence score, it is not ideally suited for tasks requiring complex logical deduction, multi-step problem-solving, or highly nuanced understanding. It performs best on more direct, instruction-following tasks.
To optimize costs, focus on minimizing output token length through precise prompting, leverage its large context window efficiently by pre-processing inputs, strategically allocate tasks to match its strengths, and continuously monitor your token usage and spending.
The model's knowledge base is current up to September 2023, meaning it may not have information on events or developments that occurred after that date.