Model Comparison
Phi-4 Mini Instruct
vs. Trinity Large Thinking
Comparing 2 AI models · 12 benchmarks · Microsoft, Arcee AI
Recommended Pick
ArTrinity Large Thinking10 metric winsStrongest on: Throughput, Reasoning, Intelligence
Lowest Price
Phi-4 Mini Instruct
$0.00/1M input price
Best Reasoning
Trinity Large Thinking
38.1 reasoning score
Blends available reasoning benchmarks
Best for Coding
Phi-4 Mini Instruct
3.8 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 Trinity Large Thinking.
Speed gap
Trinity Large Thinking generates about 4.6x as many tokens per second as Phi-4 Mini Instruct.
Reasoning gap
Trinity Large Thinking leads Phi-4 Mini Instruct by 19.7 points on reasoning.
Top-pick rationale
Trinity Large Thinking wins 10 measurable categories, including Throughput, Reasoning, Intelligence, GPQA.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Phi-4 Mini Instruct
Microsoft
TTFT
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Time
—
tok/s
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Tokens
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Cost
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Trinity Large Thinking
Arcee AI
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
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Full Comparison
| Metric | Mi Phi-4 Mini Instruct | Top Pick Ar Trinity Large Thinking |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.00/1M | $0.23/1M |
| Output Cost | $0.00/1M | $0.88/1M |
| Blended (3:1) | — | $0.40/1M |
| Specifications | ||
| Organization | Microsoft | Arcee AI |
| Release Date | Feb 26, 2024 | Apr 1, 2026 |
| Performance & Speed | ||
| Throughput | 46.3 tok/s | 212.2 tok/s |
| TTFT | 338ms | 717ms |
| Latency | 338ms | 10141ms |
| Composite Indices | ||
| Value Score | — | 100.0 |
| Reasoning Score | 18.5 | 38.1 |
| Intelligence | 6.0 | 24.5 |
| Coding | 3.8 | — |
| Math | 6.7 | — |
| Standard Benchmarks | ||
| GPQA | 33.1% | 75.2% |
| MMLU Pro | 46.5% | — |
| HLE | 4.2% | 14.7% |
| LiveCodeBench | 12.6% | — |
| MATH 500 | 69.6% | — |
| AIME 2025 | 6.7% | — |
| AIME (Original) | 3.0% | — |
| SciCode | 10.8% | 36.1% |
| LCR | 13.7% | 33.0% |
| IFBench | 21.1% | 56.3% |
| TAU-bench v2 | 8.2% | 90.1% |
| TerminalBench Hard | 0.0% | 22.7% |
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.
Trinity Large Thinking has the strongest reasoning profile with a 38.1 reasoning score,combining the available reasoning-heavy benchmarks.
Phi-4 Mini Instruct reaches a 3.8 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
Phi-4 Mini Instruct
- Cost-sensitive applications
- High-volume processing
- Code generation
- Software development
Trinity Large Thinking
- Complex reasoning tasks
- Research & analysis
