Model Comparison
o3-mini
vs. M (Reasoning)
Comparing 2 AI models · 11 benchmarks · OpenAI, Sarvam
Recommended Pick
Opo3-mini12 metric winsStrongest on: Throughput, Reasoning, Intelligence
Lowest Price
M (Reasoning)
$0.00/1M input price
Best Reasoning
o3-mini
55.4 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
M (Reasoning) is ∞x cheaper on input tokens than o3-mini.
Reasoning gap
o3-mini leads M (Reasoning) by 24.8 points on reasoning.
Top-pick rationale
o3-mini wins 12 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.
o3-mini
OpenAI
TTFT
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Time
—
tok/s
—
Tokens
—
Cost
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M (Reasoning)
Sarvam
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
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Full Comparison
| Metric | Top Pick Op o3-mini | Sa M (Reasoning) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $1.10/1M | $0.00/1M |
| Output Cost | $4.40/1M | $0.00/1M |
| Blended (3:1) | $1.93/1M | — |
| Specifications | ||
| Organization | OpenAI | Sarvam |
| Release Date | Jan 31, 2025 | May 23, 2025 |
| Performance & Speed | ||
| Throughput | 223.3 tok/s | — |
| TTFT | 5663ms | — |
| Latency | 5663ms | — |
| Composite Indices | ||
| Value Score | 100.0 | — |
| Reasoning Score | 55.4 | 30.6 |
| Intelligence | 19.0 | 3.0 |
| Standard Benchmarks | ||
| GPQA | 74.8% | 41.6% |
| MMLU Pro | 79.1% | 69.6% |
| HLE | 8.7% | 3.3% |
| LiveCodeBench | 71.7% | 29.5% |
| MATH 500 | 97.3% | 84.7% |
| AIME (Original) | 77.0% | 20.3% |
| SciCode | 39.9% | 17.8% |
| LCR | — | 0.0% |
| IFBench | — | 31.8% |
| TAU-bench v2 | 28.7% | 0.0% |
| TerminalBench Hard | 6.8% | 2.3% |
Key Takeaways
M (Reasoning) offers the best value at $0.00/1M,making it ideal for high-volume applications and cost-conscious projects.
o3-mini has the strongest reasoning profile with a 55.4 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
o3-mini
- Complex reasoning tasks
- Research & analysis
M (Reasoning)
- Cost-sensitive applications
- High-volume processing
