Model Comparison
Llama 3.1 Nemotron Instruct 70B
vs. Qwen3.5 9B (Reasoning)
Comparing 2 AI models · 12 benchmarks · NVIDIA, Alibaba
Recommended Pick
Al Qwen3.5 9B (Reasoning) 14 metric wins Strongest on: Value, Input price, Output price
Best Value
Qwen3.5 9B (Reasoning)
100.0 value score
39.6 reasoning / $0.11/1M
Lowest Price
Qwen3.5 9B (Reasoning)
$0.10/1M input price
Best Reasoning
Qwen3.5 9B (Reasoning)
39.6 reasoning score
Blends available reasoning benchmarks
Best for Coding
Qwen3.5 9B (Reasoning)
28.7 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
Qwen3.5 9B (Reasoning) has the strongest quality-to-price mix at 100.0 out of 100 value points.
Price gap
Qwen3.5 9B (Reasoning) is 12x cheaper on input tokens than Llama 3.1 Nemotron Instruct 70B.
Speed gap
Llama 3.1 Nemotron Instruct 70B generates about 4.4x as many tokens per second as Qwen3.5 9B (Reasoning).
Reasoning gap
Qwen3.5 9B (Reasoning) leads Llama 3.1 Nemotron Instruct 70B by 14.1 points on reasoning.
Top-pick rationale
Qwen3.5 9B (Reasoning) wins 14 measurable categories, including Value, Input price, Output price, Blended price.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Llama 3.1 Nemotron Instruct 70B
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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Qwen3.5 9B (Reasoning)
Alibaba
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 Instruct 70B | Top Pick Al Qwen3.5 9B (Reasoning) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $1.20/1M | $0.10/1M |
| Output Cost | $1.20/1M | $0.15/1M |
| Blended (3:1) | $1.20/1M | $0.11/1M |
| Specifications | ||
| Organization | NVIDIA | Alibaba |
| Release Date | Oct 15, 2024 | Mar 2, 2026 |
| Performance & Speed | ||
| Throughput | 297.6 tok/s | 67.8 tok/s |
| TTFT | 3542ms | 780ms |
| Latency | 3542ms | 30264ms |
| Composite Indices | ||
| Value Score | 6.0 | 100.0 |
| Reasoning Score | 25.5 | 39.6 |
| Intelligence | 7.6 | 25.0 |
| Coding | — | 28.7 |
| Math | 11.0 | — |
| Standard Benchmarks | ||
| GPQA | 46.5% | 80.6% |
| MMLU Pro | 69.0% | — |
| HLE | 4.6% | 13.3% |
| LiveCodeBench | 16.9% | — |
| MATH 500 | 73.3% | — |
| AIME 2025 | 11.0% | — |
| AIME (Original) | 24.7% | — |
| SciCode | 23.3% | 27.5% |
| LCR | 7.0% | 59.0% |
| IFBench | 30.7% | 66.7% |
| TAU-bench v2 | 23.1% | 86.8% |
| TerminalBench Hard | 4.5% | 24.2% |
Key Takeaways
Qwen3.5 9B (Reasoning) offers the best value at $0.10/1M, making it ideal for high-volume applications and cost-conscious projects.
Qwen3.5 9B (Reasoning) has the strongest reasoning profile with a 39.6 reasoning score, combining the available reasoning-heavy benchmarks.
Qwen3.5 9B (Reasoning) reaches a 28.7 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 Instruct 70B
- General-purpose AI
- Versatile applications
Qwen3.5 9B (Reasoning)
- Cost-sensitive applications
- High-volume processing
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
