Phi-4 Mini Instruct Intelligence, Performance & Price Analysis
Model summary
Intelligence
Artificial Analysis Intelligence Index
Speed
Output tokens per second
Input Price
USD per 1M tokens
Output Price
USD per 1M tokens
Verbosity
Output tokens from Intelligence Index
Metrics are compared against models of the same class:
- Non-reasoning models → compared only with other non-reasoning models
- Reasoning models → compared across both reasoning and non-reasoning
- Open weights models → compared only with other open weights models of the same size class:
- Tiny: ≤4B parameters
- Small: 4B–40B parameters
- Medium: 40B–150B parameters
- Large: >150B parameters
- Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:
- <$0.15 per 1M tokens
- $0.15–$1 per 1M tokens
- >$1 per 1M tokens
| Reasoning | No This page shows the non-reasoning version of this model. A reasoning variant may also exist. |
|---|---|
| Input modality | Supports: This information is still being updated |
| Output modality | This information is still being updated |
| Knowledge cutoff | Jun 1, 2024 |
| Context window | 128k ~192 A4 pages of size 12 Arial font |
| Total parameters | 3.8B |
| License | MIT |
| Model weights | Hugging Face |
Phi-4 Mini Instruct is above average in intelligence and well priced when comparing to other open weight non-reasoning models of similar size. It's also notably slow and very verbose. The model has a 128k tokens context window with knowledge up to June 2024.
Phi-4 Mini Instruct scores 8 on the Artificial Analysis Intelligence Index, placing it above average among comparable models (averaging 8). When evaluating the Intelligence Index, it generated 31M tokens, which is very verbose in comparison to the average of 5.7M.
Pricing for Phi-4 Mini Instruct is $0.00 per 1M input tokens (competitively priced, average: $0.00) and $0.00 per 1M output tokens (competitively priced, average: $0.00). In total, it cost $0.00 to evaluate Phi-4 Mini Instruct on the Intelligence Index.
At 43 tokens per second, Phi-4 Mini Instruct is notably slow (94).
Intelligence
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Intelligence Evaluations
Openness
Artificial Analysis Openness Index: Results
Intelligence Index Comparisons
Intelligence vs. Price
Intelligence Index Token Use & Cost
Output Tokens Used to Run Artificial Analysis Intelligence Index
Cost to Run Artificial Analysis Intelligence Index
Context Window
Context Window
Pricing
Pricing: Input and Output Prices
Intelligence vs. Price (Log Scale)
Pricing Comparison of Phi-4 Mini Instruct API Providers
Speed
Measured by Output Speed (tokens per second)
Output Speed
Output Speed vs. Price
Latency
Measured by Time (seconds) to First Token
Latency: Time To First Answer Token
End-to-End Response Time
Seconds to output 500 Tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed
End-to-End Response Time
Model Size (Open Weights Models Only)
Model Size: Total and Active Parameters
Comparisons to Phi-4 Mini
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Claude 4.5 Haiku- Claude Sonnet 4.6 (max)
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Mistral Large 3
DeepSeek V3.2
Grok 4.20 Beta 0309
Nova 2.0 Pro Preview (medium)
MiniMax-M2.5
NVIDIA Nemotron 3 Super- NVIDIA Nemotron 3 Nano
Kimi K2.5
K-EXAONE
MiMo-V2-Flash (Feb 2026)- MiMo-V2-Pro
K2 Think V2
Mi:dm K 2.5 Pro
GLM-5
Qwen3.5 397B A17B
Frequently Asked Questions
Common questions about Phi-4 Mini Instruct
Phi-4 Mini Instruct was released on February 26, 2024.
Phi-4 Mini Instruct was created by Microsoft Azure.
Phi-4 Mini Instruct scores 8 on the Artificial Analysis Intelligence Index, placing it above average among other open weight non-reasoning models of similar size (median: 8).
Phi-4 Mini Instruct generates output at 42.5 tokens per second (based on Microsoft Azure's API), which is at the lower end compared to other open weight non-reasoning models of similar size (median: 93.9 t/s).
Phi-4 Mini Instruct has a time to first token (TTFT) of 0.85s (based on Microsoft Azure's API), which is better than average compared to other open weight non-reasoning models of similar size (median: 0.94s).
When evaluated on the Intelligence Index, Phi-4 Mini Instruct generated 31M output tokens, which is at the higher end compared to other open weight non-reasoning models of similar size (median: 5.7M).
No, Phi-4 Mini Instruct is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.
Phi-4 Mini Instruct supports text only input.
Phi-4 Mini Instruct supports text only output.
No, Phi-4 Mini Instruct does not support image input. It can only process text.
No, Phi-4 Mini Instruct is not multimodal. It only supports text only input.
Phi-4 Mini Instruct has a context window of 130k tokens. This determines how much text and conversation history the model can process in a single request.
Yes, Phi-4 Mini Instruct is open weights. The model weights are publicly available and can be downloaded for self-hosting.
Phi-4 Mini Instruct has 3.84 billion parameters.
Phi-4 Mini Instruct is released under the MIT license. This license allows commercial use. View license
Phi-4 Mini Instruct achieves a score of 8 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Phi-4 Mini Instruct has a knowledge cutoff of June 2024. The model's training data includes information up to this date.
Yes, Phi-4 Mini Instruct is available via API through 2 providers. Compare API providers
Phi-4 Mini Instruct is available through 2 API providers. Compare providers