Context Window
—
Input Price/1M
$0.07
#237 of 806
Output Price/1M
$0.30
Parameters
—
Speed
46 tok/s
#359 of 651
Latency (TTFT)
820ms
Cost per Task
48.0
AA Score / US$ · higher = better
Phi-4 Mini Instruct results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 0.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 13.0 | 100.0 | Artificial Analysis official API |
| SciCode | 11.0 | 100.0 | — |
| AA Coding Index | 3.8 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 47.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 14.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MATH-500 | 69.6 | 100.0 | Artificial Analysis official API |
| AIME 2025 | 7.0 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 6.3 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 46.5 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 33.0 | 100.0 | Artificial Analysis official API |
| IFBench | 21.0 | 100.0 | — |
| HLE | 4.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 8.0 | 100.0 | — |
Phi-4 Mini Instruct is an AI model developed by Microsoft, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via Microsoft's cloud API.
Phi-4 Mini Instruct is usage-based, priced at $0.075/1M input tokens and $0.3/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
Phi-4 Mini Instruct was evaluated on 14 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Phi-4 Mini Instruct specializes in text, offering advanced capabilities for creating and processing text content.
In the 2026 AI model ecosystem, Phi-4 Mini Instruct competes directly with similarly capable models. Microsoft competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Phi-4 Mini Instruct is an AI model developed by Microsoft. It is a text model.
Phi-4 Mini Instruct costs $0.075/1M input tokens and $0.3/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, Phi-4 Mini Instruct scored: Terminal-Bench Hard: 0/100, LiveCodeBench: 13/100, SciCode: 11/100. See the full table above for a detailed comparison.
No, Phi-4 Mini Instruct is a proprietary model from Microsoft. It is available via cloud API. For open source alternatives, check our open source model ranking.
Phi-4 Mini Instruct excels at general-purpose language tasks.
Last updated: October 16, 2026 • View methodology →