Llama 3.3 70B vs Microsoft Phi-4

Performance benchmarks + pricing comparison — updated April 2026

Llama 3.3 70B

Meta

Meta's open-weight 70B model. Strong coding and general capability, widely supported across AI platforms.

Input$0.250/M
Output$1.00/M
Context128K tokens
Best ForSelf-hosted deployments, cost-effective coding, custom fine-tuning

Microsoft Phi-4

Microsoft

Microsoft's compact 14B model with strong reasoning and coding capability. Excellent value for small-scale deployments.

Input$0.100/M
Output$0.300/M
Context128K tokens
Best ForEdge deployments, local inference, budget coding
Benchmark45/100

Cost Comparison by Scenario

Estimated cost per project with 30% cache hit rate. Actual costs may vary based on usage patterns.

ScenarioLlama 3.3 70BMicrosoft Phi-4Savings
Small Script (1K lines) $0.04 $0.01 Microsoft Phi-4 saves $0.03 (68%)
Medium Feature (10K lines) $0.29 $0.10 Microsoft Phi-4 saves $0.19 (67%)
Large Project (50K lines) $1.44 $0.47 Microsoft Phi-4 saves $0.96 (67%)
Code Review (5K lines) $0.07 $0.02 Microsoft Phi-4 saves $0.04 (64%)

Verdict

Microsoft Phi-4 wins on both price and performance — $0.100/M input with a benchmark score of N/A/100.

For most developers, this is the clear choice between these two models.

Compare with Other Models