Model Comparison
R1 1776
vs. M (Reasoning)
Comparing 2 AI models · 11 benchmarks · Perplexity, Sarvam
Recommended Pick
Pe R1 1776 3 metric wins Strongest on: Reasoning, Intelligence, MATH 500
Lowest Price
R1 1776
$0.00/1M input price
Best Reasoning
R1 1776
53.7 reasoning score
Blends available reasoning benchmarks
Best for Coding
M (Reasoning)
7.5 coding index
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Reasoning gap
R1 1776 leads M (Reasoning) by 22.0 points on reasoning.
Top-pick rationale
R1 1776 wins 3 measurable categories, including Reasoning, Intelligence, MATH 500.
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
—
M (Reasoning)
Sarvam
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
Full Comparison
| Metric | Top Pick Pe R1 1776 | Sa M (Reasoning) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.00/1M | $0.00/1M |
| Output Cost | $0.00/1M | $0.00/1M |
| Specifications | ||
| Organization | Perplexity | Sarvam |
| Release Date | Feb 18, 2025 | May 23, 2025 |
| Performance & Speed | ||
| Throughput | — | — |
| TTFT | — | — |
| Latency | — | — |
| Composite Indices | ||
| Reasoning Score | 53.7 | 31.7 |
| Intelligence | 12.0 | 8.4 |
| Coding | — | 7.5 |
| Standard Benchmarks | ||
| GPQA | — | 41.6% |
| MMLU Pro | — | 69.6% |
| HLE | — | 3.3% |
| LiveCodeBench | — | 29.5% |
| MATH 500 | 95.4% | 84.7% |
| AIME (Original) | — | 20.3% |
| SciCode | — | 17.8% |
| LCR | — | 0.0% |
| IFBench | — | 31.8% |
| TAU-bench v2 | — | 0.0% |
| TerminalBench Hard | — | 2.3% |
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.
M (Reasoning) reaches a 7.5 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
M (Reasoning)
- Code generation
- Software development
