- Instruction Following
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Phi 4 Mini Instruct
- Instruction Following
Phi 4 Mini Instruct is a lightweight, 3.8B-parameter open large language model from Microsoft focused on strong reasoning, long-context understanding, and efficient deployment on modest hardware.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Phi 4 Mini Instruct?
Phi 4 Mini Instruct is a compact, instruction-tuned language model from Microsoft’s Phi-4 family designed for high‑quality reasoning with a long context window. It is mainly used for general chat-style assistants, question answering, and content generation where low latency and small resource requirements are important. It is also widely adopted as a budget-friendly baseline model for research, fine-tuning, and domain adaptation on limited compute. As part of the Phi-4 model family, it descends from earlier Phi and Phi-3 generations while serving as the backbone text model for Phi-4-multimodal variants.
Model capabilities
5 Core Capabilities
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Conversational Chat
Handles multi-turn instructions and general conversation, following prompts to generate coherent, context-aware English responses for many domains.
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Code Assistance
Helps with programming tasks like explaining code, drafting snippets, and suggesting fixes across common languages within its training scope.
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Text Understanding
Understands and summarizes English text, answering questions, extracting key points, and transforming content while preserving core meaning.
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Language Translation
Provides basic translation support between major languages, enabling users to understand or rephrase text in different languages.
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Image Reasoning
When enabled with vision, can interpret images, identify elements, and answer questions grounded in visual content.
Use cases
6 Most Valuable Use Cases
- Edge device inference
- Customer support chatbot
- Code authoring assistant
- Document question answering
- Productivity copilot features
- Monitoring and analytics
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for Phi 4 Mini–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~80 tps | ~99.99% | $0.03 | $0.06 | 128K |
| Microsoft Azure | Global | ~200ms | ~40 tps | 99.9% | ~$0.20 | ~$0.20 | 128K |
| OpenAI | Global | ~180ms | ~50 tps | 99.9% | ~$0.10 | ~$0.24 | ~128K |
| Google Cloud (Vertex AI / custom) | EU West | ~240ms | ~75 tps | ~99.9% | ~$0.11 | ~$0.22 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Phi 4 Mini Instruct (Microsoft) | Llama 3.1 8B Instruct (Meta) | Mistral 7B Instruct (Mistral) |
|---|---|---|---|
| Avg Latency | ~200ms | ~220ms | ~210ms |
| Context Window | 128K | 128K | 32K |
| Input Price ($/1M tokens) | $0.10 | $0.20 | $0.25 |
| Output Price ($/1M tokens) | $0.40 | $0.80 | $0.80 |
| Max Output Tokens | 8K | 8K | 4K |
| Throughput | ~80 tps | ~70 tps | ~60 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 12.4B
- Prompt tokens processed (last 30 days)
- 9.1B
- Completion tokens generated (last 30 days)
- 5.3M
- API requests served (last 30 days)
- 98.9%
- Average uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—no client changes or per-vendor SDK juggling required.
One endpoint, every model -
Cost-Aware Orchestration
Control spend with policy-based routing, rate limits, and tiered model selection so you can experiment freely without surprise bills or manual cost tuning.
Optimize quality per dollar -
Resilient Fallback Flows
Define automatic fallbacks across providers and models so requests keep succeeding through outages, quota limits, or timeouts—without adding complex retry logic in your app.
Stay online, by design -
End-to-End Observability
Get centralized traces, metrics, and logs for every provider and model—see latency, errors, and cost per request to debug faster and tune performance confidently.
See every token, everywhere -
Task-Level Abstractions
Describe tasks like chat, tools, ranking, or embeddings once and let LLM.API map them to the right model APIs, simplifying integrations and future migrations.
Think tasks, not APIs -
High-Throughput Batch
Submit large batches across providers with automatic chunking, retries, and aggregation to maximize throughput and minimize cost for data labeling, evaluation, and backfill jobs.
Scale jobs, not code
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a very low-cost model for simple question answering and summaries.
- You need to power lightweight chatbots that handle straightforward, low-risk customer queries.
- Your use case involves basic data extraction or classification from short, well-structured text.
- Your use case involves prototyping features quickly without consuming significant inference budget.
- You need a compact model suitable for smaller deployments with modest CPU or GPU resources.
- You need a fallback model for non-critical requests when higher-end models are unnecessary.
- Your use case involves simple instructional content generation, templates, or boilerplate drafting.
Avoid if...
- You need state-of-the-art reasoning for complex multi-step planning, coding, or mathematics.
- Your workload requires handling very long documents or extensive context without information loss.
- You need highly nuanced domain expertise, such as legal, medical, or financial analysis.
- Your workload requires strong reliability under ambiguous instructions and tricky edge-case prompts.
- You need top-tier code generation, debugging, or refactoring for large production codebases.
- Your workload requires minimizing hallucinations in high-stakes environments with strict accuracy needs.
- You need sophisticated multi-modal reasoning that tightly integrates text with complex external tools.
FAQ
Frequently Asked Questions
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What is Phi 4 Mini Instruct?
Phi 4 Mini Instruct is a lightweight Microsoft instruction-tuned language model aimed at fast, low-cost completion and chat-style tasks via LLM.API.
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What is Phi 4 Mini Instruct best suited for?
It is best for everyday chatbots, short-form content generation, code helpers, and utility functions where low latency and low cost are priorities.
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What is the context window of Phi 4 Mini Instruct?
Phi 4 Mini Instruct supports a 4K token context window on LLM.API, suitable for short conversations and small documents.
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How fast is Phi 4 Mini Instruct on LLM.API?
Phi 4 Mini Instruct is optimized for low latency, typically returning short responses in under a second depending on load and prompt size.
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Which modalities does Phi 4 Mini Instruct support?
Phi 4 Mini Instruct supports text input and text output only; it does not handle images, audio, or video.
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How do I call Phi 4 Mini Instruct through LLM.API?
Specify the provider as "microsoft" and the model name "phi-4-mini-instruct" in your LLM.API completion or chat request payload.
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How does the cost of Phi 4 Mini Instruct compare to larger models?
Phi 4 Mini Instruct is priced significantly cheaper per token than larger flagship models, making it economical for high-volume or latency-sensitive workloads.
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How does Phi 4 Mini Instruct compare to larger Phi or frontier models?
Compared to larger Phi or frontier models, it is faster and cheaper but less capable on complex reasoning, long-context tasks, and nuanced understanding.
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What are the main limitations of Phi 4 Mini Instruct?
It can struggle with very long documents, multi-step reasoning, domain-expert tasks, and may still hallucinate or produce incorrect answers.
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Can I fine-tune or customize Phi 4 Mini Instruct via LLM.API?
Direct fine-tuning is not exposed; you should instead use prompt engineering, system messages, and retrieval to adapt behavior.
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