Model Comparison
Phi-4 Multimodal Instruct
vs. Trinity Large Thinking
Comparing 2 AI models · 11 benchmarks · Microsoft, Arcee AI
Recommended Pick
ArTrinity Large Thinking6 metric winsStrongest on: Throughput, Reasoning, Intelligence
Lowest Price
Phi-4 Multimodal Instruct
$0.00/1M input price
Best Reasoning
Trinity Large Thinking
38.1 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
Phi-4 Multimodal Instruct is ∞x cheaper on input tokens than Trinity Large Thinking.
Speed gap
Trinity Large Thinking generates about 11.7x as many tokens per second as Phi-4 Multimodal Instruct.
Reasoning gap
Trinity Large Thinking leads Phi-4 Multimodal Instruct by 14.3 points on reasoning.
Top-pick rationale
Trinity Large Thinking wins 6 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 Multimodal Instruct
Microsoft
TTFT
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Time
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tok/s
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Tokens
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Cost
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Trinity Large Thinking
Arcee AI
TTFT
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Time
—
tok/s
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Tokens
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Cost
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Which answer was more useful?
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Full Comparison
| Metric | Mi Phi-4 Multimodal 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, 2025 | Apr 1, 2026 |
| Performance & Speed | ||
| Throughput | 16.5 tok/s | 193.2 tok/s |
| TTFT | 357ms | 739ms |
| Latency | 357ms | 11092ms |
| Composite Indices | ||
| Value Score | — | 100.0 |
| Reasoning Score | 23.8 | 38.1 |
| Intelligence | 4.5 | 24.5 |
| Standard Benchmarks | ||
| GPQA | 31.5% | 75.2% |
| MMLU Pro | 48.5% | — |
| HLE | 4.4% | 14.7% |
| LiveCodeBench | 13.1% | — |
| MATH 500 | 69.3% | — |
| AIME (Original) | 9.3% | — |
| SciCode | 11.0% | 36.1% |
| LCR | — | 33.0% |
| IFBench | — | 56.3% |
| TAU-bench v2 | — | 90.1% |
| TerminalBench Hard | — | 22.7% |
Key Takeaways
Phi-4 Multimodal 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.
All models support context windows of ∞+ tokens,suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
Phi-4 Multimodal Instruct
- Cost-sensitive applications
- High-volume processing
Trinity Large Thinking
- Complex reasoning tasks
- Research & analysis
