Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1: The Ultimate Performance & Pricing Comparison

Deep dive into reasoning, benchmarks, and latency insights.

The Final Verdict in the Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 Showdown

After a comprehensive analysis, Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) emerges as the superior choice for users prioritizing raw intelligence and reasoning. However, Magistral Small 1 remains a highly competitive option, especially for scenarios demanding faster response times and cost-efficiency. This detailed Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 comparison will break down every critical metric to help you make the best decision.

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Model Snapshot

Key decision metrics at a glance.

Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning)

Other

Reasoning: 5
Coding: 6
Multimodal: 1
Long Context: 2
Blended Price / 1M tokens: $0.015
P95 Latency: 1000ms
Tokens per second: —

Magistral Small 1

Mistral

Reasoning: 4
Coding: 1
Multimodal: 1
Long Context: 2
Blended Price / 1M tokens: $0.001
P95 Latency: 1000ms
Tokens per second: 70.065tokens/sec

Overall Capabilities

The capability radar provides a holistic view of the Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 matchup. This chart illustrates each model's strengths and weaknesses at a glance, forming a cornerstone of our Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 analysis.

This radar chart visually maps the core capabilities (reasoning, coding, math proxy, multimodal, long context) of `Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning)` vs `Magistral Small 1`.

Benchmark Breakdown

For a granular look, this chart directly compares scores across standardized benchmarks. In the critical MMLU Pro test, a key part of the Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 debate, Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) scores 50 against Magistral Small 1's 40. This data-driven approach is essential for any serious Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 comparison.

This grouped bar chart provides a side-by-side comparison for each benchmark metric.

Speed & Latency

Speed is a crucial factor in the Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 decision for interactive applications. The metrics below highlight the trade-offs you should weigh before shipping to production.

Time to First Token

Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning)300ms

Magistral Small 1300ms

Tokens per Second

Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning)54

Magistral Small 170.065

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The Economics of Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1

Power is only one part of the equation. This Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 pricing analysis gives you a true sense of value.

Pricing Breakdown

Compare input and output pricing at a glance.

Real-World Cost Scenario

To process 1M input tokens and generate 250k output tokens, Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) would cost $0.018, whereas Magistral Small 1 would cost $0.001. This practical calculation is vital for any developer considering the Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 choice.

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Which Model Wins the Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 Battle for You?

Choose Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) if...

You are working in a technical or scientific field requiring the highest accuracy.

Cost is a secondary concern to power in your Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 decision.

You need the most advanced reasoning capabilities available.

Choose Magistral Small 1 if...

You need a highly responsive model for user-facing applications.

Your budget is a primary consideration in the Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 choice.

You are developing at scale where operational costs are critical.

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Your Questions about the Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) vs Magistral Small 1 Comparison

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Data source: https://artificialanalysis.ai/