Microsoft Phi-4 vs Llama 3.1 70B
Performance benchmarks + pricing comparison — updated April 2026
Microsoft Phi-4
MicrosoftMicrosoft's compact 14B model with strong reasoning and coding capability. Excellent value for small-scale deployments.
| Input | $0.100/M |
| Output | $0.300/M |
| Context | 128K tokens |
| Best For | Edge deployments, local inference, budget coding |
| Benchmark | 45/100 |
Llama 3.1 70B
MetaMeta's mid-size Llama 3.1. Strong general performance with open weights for custom deployment.
| Input | $0.200/M |
| Output | $0.400/M |
| Context | 128K tokens |
| Best For | General AI tasks, custom deployment, fine-tuning |
Cost Comparison by Scenario
Estimated cost per project with 30% cache hit rate. Actual costs may vary based on usage patterns.
| Scenario | Microsoft Phi-4 | Llama 3.1 70B | Savings |
|---|---|---|---|
| Small Script (1K lines) | $0.01 | $0.02 | Microsoft Phi-4 saves <$0.01 (34%) |
| Medium Feature (10K lines) | $0.10 | $0.15 | Microsoft Phi-4 saves $0.06 (37%) |
| Large Project (50K lines) | $0.47 | $0.75 | Microsoft Phi-4 saves $0.28 (37%) |
| Code Review (5K lines) | $0.02 | $0.04 | Microsoft Phi-4 saves $0.02 (44%) |
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.
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