Model Comparison
Llama 3.3 Nemotron Super 49B v1 (Non-reasoning)
vs. Magistral Small 1.2
Comparing 2 AI models · 12 benchmarks · NVIDIA, Mistral
Recommended Pick
Mi Magistral Small 1.2 14 metric wins Strongest on: Throughput, Reasoning, Intelligence
Lowest Price
Llama 3.3 Nemotron Super 49B v1 (Non-reasoning)
$0.00/1M input price
Best Reasoning
Magistral Small 1.2
50.2 reasoning score
Blends available reasoning benchmarks
Best for Coding
Magistral Small 1.2
14.8 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
Llama 3.3 Nemotron Super 49B v1 (Non-reasoning) is ∞x cheaper on input tokens than Magistral Small 1.2.
Reasoning gap
Magistral Small 1.2 leads Llama 3.3 Nemotron Super 49B v1 (Non-reasoning) by 24.3 points on reasoning.
Coding gap
Magistral Small 1.2 leads Llama 3.3 Nemotron Super 49B v1 (Non-reasoning) by 7.2 points on coding.
Top-pick rationale
Magistral Small 1.2 wins 14 measurable categories, including Throughput, Reasoning, Intelligence, Coding.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Llama 3.3 Nemotron Super 49B v1 (Non-reasoning)
NVIDIA
TTFT
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Time
—
tok/s
—
Tokens
—
Cost
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Magistral Small 1.2
Mistral
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
Full Comparison
| Metric | NV Llama 3.3 Nemotron Super 49B v1 (Non-reasoning) | Top Pick Mi Magistral Small 1.2 |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.00/1M | $0.50/1M |
| Output Cost | $0.00/1M | $1.50/1M |
| Blended (3:1) | — | $0.75/1M |
| Specifications | ||
| Organization | NVIDIA | Mistral |
| Release Date | Mar 18, 2025 | Sep 17, 2025 |
| Performance & Speed | ||
| Throughput | — | 111.3 tok/s |
| TTFT | — | 418ms |
| Latency | — | 18388ms |
| Composite Indices | ||
| Value Score | — | 100.0 |
| Reasoning Score | 26.0 | 50.2 |
| Intelligence | 14.3 | 18.2 |
| Coding | 7.6 | 14.8 |
| Math | 7.7 | 80.3 |
| Standard Benchmarks | ||
| GPQA | 51.7% | 66.3% |
| MMLU Pro | 69.8% | 76.8% |
| HLE | 3.5% | 6.1% |
| LiveCodeBench | 28.0% | 72.3% |
| MATH 500 | 77.5% | — |
| AIME 2025 | 7.7% | 80.3% |
| AIME (Original) | 19.3% | — |
| SciCode | 22.9% | 35.2% |
| LCR | 11.3% | 16.3% |
| IFBench | 39.5% | 44.4% |
| TAU-bench v2 | — | 27.8% |
| TerminalBench Hard | 0.0% | 4.5% |
Key Takeaways
Llama 3.3 Nemotron Super 49B v1 (Non-reasoning) offers the best value at $0.00/1M, making it ideal for high-volume applications and cost-conscious projects.
Magistral Small 1.2 has the strongest reasoning profile with a 50.2 reasoning score, combining the available reasoning-heavy benchmarks.
Magistral Small 1.2 reaches a 14.8 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 3.3 Nemotron Super 49B v1 (Non-reasoning)
- Cost-sensitive applications
- High-volume processing
Magistral Small 1.2
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
