Model Comparison
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
vs. Phi-4 Mini Instruct
Comparing 2 AI models · 12 benchmarks · Anthropic, Microsoft
Recommended Pick
An Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) 11 metric wins Strongest on: Throughput, Reasoning, Intelligence
Lowest Price
Phi-4 Mini Instruct
$0.00/1M input price
Best Reasoning
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
56.4 reasoning score
Blends available reasoning benchmarks
Best for Coding
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
50.9 coding index
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Price gap
Phi-4 Mini Instruct is ∞x cheaper on input tokens than Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort).
Speed gap
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) generates about 2.8x as many tokens per second as Phi-4 Mini Instruct.
Reasoning gap
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) leads Phi-4 Mini Instruct by 37.6 points on reasoning.
Coding gap
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) leads Phi-4 Mini Instruct by 47.3 points on coding.
Top-pick rationale
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) wins 11 measurable categories, including Throughput, Reasoning, Intelligence, Coding.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
Anthropic
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Phi-4 Mini Instruct
Microsoft
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
Full Comparison
| Metric | Top Pick An Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) | Mi Phi-4 Mini Instruct |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $3.00/1M | $0.00/1M |
| Output Cost | $15.00/1M | $0.00/1M |
| Blended (3:1) | $6.00/1M | — |
| Specifications | ||
| Organization | Anthropic | Microsoft |
| Release Date | Feb 17, 2026 | Feb 26, 2024 |
| Performance & Speed | ||
| Throughput | 62.0 tok/s | 22.1 tok/s |
| TTFT | 70526ms | 418ms |
| Latency | 70526ms | 418ms |
| Composite Indices | ||
| Value Score | 100.0 | — |
| Reasoning Score | 56.4 | 18.8 |
| Intelligence | 51.7 | 8.4 |
| Coding | 50.9 | 3.6 |
| Math | — | 6.7 |
| Standard Benchmarks | ||
| GPQA | 87.5% | 33.1% |
| MMLU Pro | — | 46.5% |
| HLE | 30.0% | 4.2% |
| LiveCodeBench | — | 12.6% |
| MATH 500 | — | 69.6% |
| AIME 2025 | — | 6.7% |
| AIME (Original) | — | 3.0% |
| SciCode | 46.8% | 10.8% |
| LCR | 70.7% | 13.7% |
| IFBench | 56.6% | 21.1% |
| TAU-bench v2 | 75.7% | 8.2% |
| TerminalBench Hard | 53.0% | 0.0% |
Key Takeaways
Phi-4 Mini Instruct offers the best value at $0.00/1M, making it ideal for high-volume applications and cost-conscious projects.
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) has the strongest reasoning profile with a 56.4 reasoning score, combining the available reasoning-heavy benchmarks.
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) reaches a 50.9 coding index, making it the top choice for software development and code generation tasks.
All models support context windows of ∞+ tokens, suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
Phi-4 Mini Instruct
- Cost-sensitive applications
- High-volume processing
