Model Comparison
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning)
vs. M2.7
Comparing 2 AI models · 12 benchmarks · NVIDIA, MiniMax
Recommended Pick
Mi M2.7 13 metric wins Strongest on: Value, Input price, Output price
Best Value
M2.7
100.0 value score
55.0 reasoning / $0.52/1M
Lowest Price
M2.7
$0.30/1M input price
Best Reasoning
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning)
56.2 reasoning score
Blends available reasoning benchmarks
Best for Coding
M2.7
41.9 coding index
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Best value
M2.7 has the strongest quality-to-price mix at 100.0 out of 100 value points.
Price gap
M2.7 is 2.0x cheaper on input tokens than Llama 3.1 Nemotron Ultra 253B v1 (Reasoning).
Speed gap
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) generates about 1.2x as many tokens per second as M2.7.
Reasoning gap
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) leads M2.7 by 1.1 points on reasoning.
Coding gap
M2.7 leads Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) by 28.8 points on coding.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning)
NVIDIA
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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M2.7
MiniMax
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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Which answer was more useful?
Full Comparison
| Metric | NV Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) | Top Pick Mi M2.7 |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.60/1M | $0.30/1M |
| Output Cost | $1.80/1M | $1.20/1M |
| Blended (3:1) | $0.90/1M | $0.52/1M |
| Specifications | ||
| Organization | NVIDIA | MiniMax |
| Release Date | Apr 7, 2025 | Mar 18, 2026 |
| Performance & Speed | ||
| Throughput | 51.5 tok/s | 43.6 tok/s |
| TTFT | 725ms | 1596ms |
| Latency | 39547ms | 58127ms |
| Composite Indices | ||
| Value Score | 59.5 | 100.0 |
| Reasoning Score | 56.2 | 55.0 |
| Intelligence | 15.0 | 49.6 |
| Coding | 13.1 | 41.9 |
| Math | 63.7 | — |
| Standard Benchmarks | ||
| GPQA | 72.8% | 87.4% |
| MMLU Pro | 82.5% | — |
| HLE | 8.1% | 28.1% |
| LiveCodeBench | 64.1% | — |
| MATH 500 | 95.2% | — |
| AIME 2025 | 63.7% | — |
| AIME (Original) | 74.7% | — |
| SciCode | 34.7% | 47.0% |
| LCR | 7.3% | 68.7% |
| IFBench | 38.2% | 75.7% |
| TAU-bench v2 | 11.4% | 84.8% |
| TerminalBench Hard | 2.3% | 39.4% |
Key Takeaways
M2.7 offers the best value at $0.30/1M, making it ideal for high-volume applications and cost-conscious projects.
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) has the strongest reasoning profile with a 56.2 reasoning score, combining the available reasoning-heavy benchmarks.
M2.7 reaches a 41.9 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
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning)
- Complex reasoning tasks
- Research & analysis
M2.7
- Cost-sensitive applications
- High-volume processing
- Code generation
- Software development
