Microsoft Phi-4 (14B)
MIT Open SourceDeveloped by Microsoft · Released 2025-01-01
What are the token costs and operational benchmarks for Microsoft Phi-4 (14B)?
Architectural Overview
Frontier foundation model developed by Microsoft featuring Dense Small Language Model architecture.
Optimal Production Use Cases
High-efficiency local math reasoning, embedded device processing, on-device SLM logic, and low-latency classification.
Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$0.12/1M in · $0.36/1M out
$0.18/1M in · $0.72/1M out
Head-to-Head Comparisons Involving Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) vs Claude 3.5 Haiku
Microsoft Phi-4 (14B) is 85.0% cheaper for input tokens ($0.12 vs. $0.80 per 1M tokens) and $0.36 vs. $4.00 for output tokens (6.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (30ms faster than Claude 3.5 Haiku). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Microsoft Phi-4 (14B) vs Claude 3.5 Sonnet
Microsoft Phi-4 (14B) is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.36 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (210ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Claude 3.7 Sonnet
Microsoft Phi-4 (14B) is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.36 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (540ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Claude Opus 5
Microsoft Phi-4 (14B) is 97.6% cheaper for input tokens ($0.12 vs. $5.00 per 1M tokens) and $0.36 vs. $25.00 for output tokens (41.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (230ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Codestral 25.01
Microsoft Phi-4 (14B) is 60.0% cheaper for input tokens ($0.12 vs. $0.30 per 1M tokens) and $0.36 vs. $0.90 for output tokens (2.5x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (40ms faster than Codestral 25.01). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Composer 2.5
Microsoft Phi-4 (14B) is 93.3% cheaper for input tokens ($0.12 vs. $1.80 per 1M tokens) and $0.36 vs. $7.20 for output tokens (15.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (70ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs DeepSeek-R1
Microsoft Phi-4 (14B) is 78.2% cheaper for input tokens ($0.12 vs. $0.55 per 1M tokens) and $0.36 vs. $2.19 for output tokens (4.6x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (1690ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs DeepSeek-V3
Microsoft Phi-4 (14B) is 14.3% cheaper for input tokens ($0.12 vs. $0.14 per 1M tokens) and $0.36 vs. $0.28 for output tokens (1.2x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (230ms faster than DeepSeek-V3). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 42.0% for DeepSeek-V3.
Microsoft Phi-4 (14B) vs DeepSeek-V4 Flash
Microsoft Phi-4 (14B) is 14.3% cheaper for input tokens ($0.12 vs. $0.14 per 1M tokens) and $0.36 vs. $0.56 for output tokens (1.2x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (40ms faster than DeepSeek-V4 Flash). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Fable 5
Microsoft Phi-4 (14B) is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.36 vs. $8.00 for output tokens (16.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (130ms faster than Fable 5). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Gemini 2.0 Flash
Gemini 2.0 Flash is 16.7% cheaper for input tokens ($0.10 vs. $0.12 per 1M tokens) and $0.40 vs. $0.36 for output tokens (1.2x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (270ms faster than Gemini 2.0 Flash). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Gemini 3.7 Flash
Gemini 3.7 Flash is 33.3% cheaper for input tokens ($0.08 vs. $0.12 per 1M tokens) and $0.32 vs. $0.36 for output tokens (1.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (35ms faster than Microsoft Phi-4 (14B)). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs GLM 5.3 Flash
Microsoft Phi-4 (14B) is 20.0% cheaper for input tokens ($0.12 vs. $0.15 per 1M tokens) and $0.36 vs. $0.50 for output tokens (1.2x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (20ms faster than GLM 5.3 Flash). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs OpenAI GPT-4o
Microsoft Phi-4 (14B) is 95.2% cheaper for input tokens ($0.12 vs. $2.50 per 1M tokens) and $0.36 vs. $10.00 for output tokens (20.8x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (170ms faster than OpenAI GPT-4o). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Microsoft Phi-4 (14B) vs GPT-5.6 Luna
Microsoft Phi-4 (14B) is 33.3% cheaper for input tokens ($0.12 vs. $0.18 per 1M tokens) and $0.36 vs. $0.72 for output tokens (1.5x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (20ms faster than Microsoft Phi-4 (14B)). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs GPT-5.6 Sol
Microsoft Phi-4 (14B) is 98.5% cheaper for input tokens ($0.12 vs. $8.00 per 1M tokens) and $0.36 vs. $32.00 for output tokens (66.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (310ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs GPT-5.6 Terra
Microsoft Phi-4 (14B) is 92.0% cheaper for input tokens ($0.12 vs. $1.50 per 1M tokens) and $0.36 vs. $6.00 for output tokens (12.5x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (100ms faster than GPT-5.6 Terra). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Grok 3
Microsoft Phi-4 (14B) is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.36 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (740ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs xAI Grok 4.6
Microsoft Phi-4 (14B) is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.36 vs. $6.00 for output tokens (16.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (170ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Llama 3.3 70B Instruct
Microsoft Phi-4 (14B) is 33.3% cheaper for input tokens ($0.12 vs. $0.18 per 1M tokens) and $0.36 vs. $0.40 for output tokens (1.5x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (310ms faster than Llama 3.3 70B Instruct). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Microsoft Phi-4 (14B) vs Mistral Large 2
Microsoft Phi-4 (14B) is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.36 vs. $6.00 for output tokens (16.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (440ms faster than Mistral Large 2). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 39.0% for Mistral Large 2.
Microsoft Phi-4 (14B) vs OpenAI o1
Microsoft Phi-4 (14B) is 99.2% cheaper for input tokens ($0.12 vs. $15.00 per 1M tokens) and $0.36 vs. $60.00 for output tokens (125.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (740ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs o3-mini
Microsoft Phi-4 (14B) is 89.1% cheaper for input tokens ($0.12 vs. $1.10 per 1M tokens) and $0.36 vs. $4.40 for output tokens (9.2x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (1090ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Qwen 2.5 72B Instruct
Microsoft Phi-4 (14B) is 65.7% cheaper for input tokens ($0.12 vs. $0.35 per 1M tokens) and $0.36 vs. $0.40 for output tokens (2.9x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (310ms faster than Qwen 2.5 72B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Qwen 2.5 Max
Microsoft Phi-4 (14B) is 57.1% cheaper for input tokens ($0.12 vs. $0.28 per 1M tokens) and $0.36 vs. $0.84 for output tokens (2.3x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (370ms faster than Qwen 2.5 Max). Qwen 2.5 Max leads coding benchmarks at 44.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Qwen 3.8 Flash Next
Both models share identical input pricing at $0.12 per 1M tokens. In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (10ms faster than Qwen 3.8 Flash Next). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Frequently Asked Questions & Query Fan-Out
How much does Microsoft Phi-4 (14B) cost per 1M tokens?
Microsoft Phi-4 (14B) costs $0.12 per million prompt (input) tokens and $0.36 per million completion (output) tokens.
What is the context window for Microsoft Phi-4 (14B)?
Microsoft Phi-4 (14B) supports a maximum context window of 32,768 tokens, with a maximum single-generation output of 16,384 tokens.
What are the primary use cases for Microsoft Phi-4 (14B)?
High-efficiency local math reasoning, embedded device processing, on-device SLM logic, and low-latency classification.