Model Comparison
R1 1776
vs. Trinity Large Thinking
Comparing 2 AI models · 8 benchmarks · Perplexity, Arcee AI
Recommended Pick
Pe R1 1776 5 metric wins Strongest on: Input price, Output price, TTFT
Lowest Price
R1 1776
$0.00/1M input price
Best Reasoning
R1 1776
53.7 reasoning score
Blends available reasoning benchmarks
Best for Coding
Trinity Large Thinking
27.2 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
R1 1776 is ∞x cheaper on input tokens than Trinity Large Thinking.
Reasoning gap
R1 1776 leads Trinity Large Thinking by 13.1 points on reasoning.
Top-pick rationale
R1 1776 wins 5 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.
R1 1776
Perplexity
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Trinity Large Thinking
Arcee AI
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
Full Comparison
| Metric | Top Pick Pe R1 1776 | 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 | Perplexity | Arcee AI |
| Release Date | Feb 18, 2025 | Apr 1, 2026 |
| Performance & Speed | ||
| Throughput | — | 183.4 tok/s |
| TTFT | — | 818ms |
| Latency | — | 11720ms |
| Composite Indices | ||
| Value Score | — | 100.0 |
| Reasoning Score | 53.7 | 40.6 |
| Intelligence | 12.0 | 31.9 |
| Coding | — | 27.2 |
| Standard Benchmarks | ||
| GPQA | — | 75.2% |
| HLE | — | 14.7% |
| MATH 500 | 95.4% | — |
| SciCode | — | 36.1% |
| LCR | — | 33.0% |
| IFBench | — | 56.3% |
| TAU-bench v2 | — | 90.1% |
| TerminalBench Hard | — | 22.7% |
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 53.7 reasoning score, combining the available reasoning-heavy benchmarks.
Trinity Large Thinking reaches a 27.2 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
R1 1776
- Cost-sensitive applications
- High-volume processing
- Complex reasoning tasks
- Research & analysis
Trinity Large Thinking
- Code generation
- Software development
