Model Comparison
Phi-4
vs. R1 1776
Comparing 2 AI models · 12 benchmarks · Microsoft, Perplexity
Recommended Pick
PeR1 17767 metric winsStrongest on: Input price, Output price, TTFT
Lowest Price
R1 1776
$0.00/1M input price
Best Reasoning
R1 1776
50.8 reasoning score
Blends available reasoning benchmarks
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Price gap
R1 1776 is ∞x cheaper on input tokens than Phi-4.
Reasoning gap
R1 1776 leads Phi-4 by 22.6 points on reasoning.
Top-pick rationale
R1 1776 wins 7 measurable categories, including Input price, Output price, TTFT, Latency.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Phi-4
Microsoft
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
R1 1776
Perplexity
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
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Full Comparison
| Metric | Mi Phi-4 | Top Pick Pe R1 1776 |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.13/1M | $0.00/1M |
| Output Cost | $0.50/1M | $0.00/1M |
| Blended (3:1) | $0.22/1M | — |
| Specifications | ||
| Organization | Microsoft | Perplexity |
| Release Date | Dec 12, 2024 | Feb 18, 2025 |
| Performance & Speed | ||
| Throughput | 27.5 tok/s | — |
| TTFT | 654ms | — |
| Latency | 654ms | — |
| Composite Indices | ||
| Value Score | 100.0 | — |
| Reasoning Score | 28.3 | 50.8 |
| Intelligence | 4.9 | 6.3 |
| Math | 18.0 | — |
| Standard Benchmarks | ||
| GPQA | 57.5% | — |
| MMLU Pro | 71.4% | — |
| HLE | 4.1% | — |
| LiveCodeBench | 23.1% | — |
| MATH 500 | 81.0% | 95.4% |
| AIME 2025 | 18.0% | — |
| AIME (Original) | 14.3% | — |
| SciCode | 26.0% | — |
| LCR | 0.0% | — |
| IFBench | 23.5% | — |
| TAU-bench v2 | 0.0% | — |
| TerminalBench Hard | 3.8% | — |
Key Takeaways
R1 1776 offers the best value at $0.00/1M,making it ideal for high-volume applications and cost-conscious projects.
R1 1776 has the strongest reasoning profile with a 50.8 reasoning score,combining the available reasoning-heavy benchmarks.
All models support context windows of ∞+ tokens,suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
Phi-4
- General-purpose AI
- Versatile applications
R1 1776
- Cost-sensitive applications
- High-volume processing
- Complex reasoning tasks
- Research & analysis
