Phi-4 is a compact, non-reasoning model from Microsoft Azure, offering above-average intelligence and conciseness, though with moderate speed and pricing.
Phi-4, developed by Microsoft Azure, stands out as a compact yet capable language model. Operating under an open license, it offers developers flexibility and accessibility. With a substantial 16k token context window and knowledge updated through May 2024, Phi-4 is well-equipped for a variety of tasks requiring a decent grasp of recent information and the ability to process moderately long inputs.
In our Artificial Analysis Intelligence Index, Phi-4 achieved a score of 23 out of 55, placing it above the average for comparable models (which typically score around 20). This indicates a strong performance in intelligence benchmarks, especially considering its smaller footprint. A notable characteristic is its conciseness: during the Intelligence Index evaluation, Phi-4 generated 8.8 million tokens, significantly less than the average of 13 million, suggesting an efficient and to-the-point output style.
However, Phi-4 presents a mixed bag when it comes to speed and pricing. At an average output speed of 19.2 tokens per second, it is notably slower than many of its counterparts, which could impact real-time or high-throughput applications. Pricing is also somewhat above average, with input tokens costing $0.13 per 1M (average: $0.10) and output tokens at $0.50 per 1M (average: $0.20). The total cost to evaluate Phi-4 on the Intelligence Index was $6.92, reflecting these pricing tiers.
Despite these considerations, Phi-4's open license, above-average intelligence, and impressive conciseness make it an attractive option for developers looking for a capable model that can deliver precise outputs without excessive verbosity. Strategic provider selection, such as Deepinfra, can significantly mitigate its average speed and pricing concerns, transforming it into a highly competitive choice for specific use cases.
23 (23 / 55 / 55)
19.2 tokens/s
$0.13 /M tokens
$0.50 /M tokens
8.8M tokens
0.27 s
| Spec | Details |
|---|---|
| Owner | Microsoft Azure |
| License | Open |
| Context Window | 16k tokens |
| Knowledge Cutoff | May 2024 |
| Intelligence Index Score | 23 / 55 |
| Average Output Speed | 19.2 tokens/s |
| Average Input Price | $0.13 / 1M tokens |
| Average Output Price | $0.50 / 1M tokens |
| Intelligence Index Eval Cost | $6.92 |
| Intelligence Index Verbosity | 8.8M tokens |
| Fastest Latency (Deepinfra) | 0.27s |
| Fastest Output Speed (Deepinfra) | 27 tokens/s |
| Lowest Blended Price (Deepinfra) | $0.09 / 1M tokens |
Choosing the right API provider for Phi-4 is crucial for optimizing both performance and cost. Our benchmarks reveal significant differences across key metrics, with Deepinfra consistently outperforming Microsoft Azure in efficiency and affordability.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Balanced Performance | Deepinfra | Offers the best blend of speed, latency, and price. | Slightly less integrated with Azure ecosystem. |
| Lowest Latency | Deepinfra | Achieves the fastest time to first token (0.27s). | Still requires managing overall output speed. |
| Highest Output Speed | Deepinfra | Delivers 27 tokens/s, significantly faster than Azure. | May still be slower than top-tier models from other families. |
| Lowest Blended Price | Deepinfra | Most cost-effective at $0.09/M tokens. | Input/output prices are still distinct, requiring careful management. |
| Azure Ecosystem Integration | Microsoft Azure | Native integration for existing Azure users. | Higher costs and slower performance compared to Deepinfra. |
Provider data based on Artificial Analysis benchmarks. Performance and pricing can vary based on region, load, and specific API configurations.
Understanding the real-world cost of Phi-4 involves translating its per-token pricing into practical scenarios. Below are estimated costs for common tasks, assuming Deepinfra's optimized pricing ($0.07/M input, $0.14/M output) for the best-case scenario.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Scenario | Input | Output | What it represents | Estimated cost |
| Short Q&A | 500 tokens | 100 tokens | Answering a concise question based on provided context. | $0.000049 |
| Content Summarization | 5,000 tokens | 500 tokens | Summarizing a medium-length article or document. | $0.00042 |
| Code Generation | 1,000 tokens | 300 tokens | Generating a small code snippet or function. | $0.000112 |
| Long-form Article Draft | 1,500 tokens | 2,000 tokens | Drafting a substantial blog post or report. | $0.000385 |
| Customer Support Response | 800 tokens | 200 tokens | Generating a detailed response to a customer query. | $0.000084 |
| Data Extraction (Structured) | 3,000 tokens | 150 tokens | Extracting specific entities from a large text block. | $0.000231 |
These estimates highlight Phi-4's cost-effectiveness for many common tasks, especially when leveraging optimized providers like Deepinfra. The conciseness of its output further contributes to keeping costs low, even for tasks that might generate longer responses from other models.
To maximize the value and minimize the cost of using Phi-4, consider implementing these strategic approaches:
The choice of API provider dramatically impacts both performance and cost. Deepinfra, for instance, offers significantly better pricing and speed for Phi-4 compared to Microsoft Azure.
While Phi-4 is inherently concise, careful prompt engineering can further reduce unnecessary output tokens, directly impacting costs.
Given Phi-4's moderate output speed, batching requests can improve overall throughput for non-real-time applications.
Phi-4's 16k context window is generous, but managing its usage efficiently can prevent unnecessary input token costs.
Phi-4 is a compact, non-reasoning language model developed by Microsoft Azure. It features an open license, a 16k token context window, and knowledge up to May 2024, making it suitable for a range of text generation and understanding tasks.
Phi-4 scores 23 out of 55 on the Artificial Analysis Intelligence Index, placing it above the average for comparable models. This indicates strong performance relative to its size and class.
While its average output speed is moderate (19.2 tokens/s), optimized providers like Deepinfra offer excellent latency (0.27s TTFT) and higher output speeds (27 tokens/s). This makes it viable for real-time applications, especially when provider choice is carefully considered.
The most effective ways to reduce costs include choosing an optimized provider like Deepinfra, which offers significantly lower blended prices. Additionally, managing output verbosity through precise prompting and using stop sequences can minimize output token usage, further saving costs.
Phi-4 has a 16k token context window, allowing it to process substantial amounts of information. Its knowledge base is current up to May 2024, providing access to relatively recent information.
Phi-4 is owned and developed by Microsoft Azure. It is released under an open license, which provides flexibility for developers and allows for broad integration into various applications and services.
Phi-4 is notably concise, generating fewer tokens for similar evaluation tasks compared to other models. This characteristic is beneficial for applications where brevity is valued, and it directly contributes to lower output token costs, making it more economical for certain use cases.