Model Comparison
Llama 3.3 Nemotron Super 49B v1 (Reasoning)
vs. Qwen3.6 27B (Reasoning)
Comparing 2 AI models · 12 benchmarks · NVIDIA, Alibaba
Recommended Pick
Al Qwen3.6 27B (Reasoning) 11 metric wins Strongest on: Throughput, Reasoning, Intelligence
Lowest Price
Llama 3.3 Nemotron Super 49B v1 (Reasoning)
$0.00/1M input price
Best Reasoning
Qwen3.6 27B (Reasoning)
50.5 reasoning score
Blends available reasoning benchmarks
Best for Coding
Qwen3.6 27B (Reasoning)
36.5 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 (Reasoning) is ∞x cheaper on input tokens than Qwen3.6 27B (Reasoning).
Reasoning gap
Qwen3.6 27B (Reasoning) leads Llama 3.3 Nemotron Super 49B v1 (Reasoning) by 0.1 points on reasoning.
Coding gap
Qwen3.6 27B (Reasoning) leads Llama 3.3 Nemotron Super 49B v1 (Reasoning) by 27.1 points on coding.
Top-pick rationale
Qwen3.6 27B (Reasoning) wins 11 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 (Reasoning)
NVIDIA
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
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Qwen3.6 27B (Reasoning)
Alibaba
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
Full Comparison
| Metric | NV Llama 3.3 Nemotron Super 49B v1 (Reasoning) | Top Pick Al Qwen3.6 27B (Reasoning) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.00/1M | $0.60/1M |
| Output Cost | $0.00/1M | $3.60/1M |
| Blended (3:1) | — | $1.35/1M |
| Specifications | ||
| Organization | NVIDIA | Alibaba |
| Release Date | Mar 18, 2025 | Apr 22, 2026 |
| Performance & Speed | ||
| Throughput | — | 58.8 tok/s |
| TTFT | — | 1493ms |
| Latency | — | 98032ms |
| Composite Indices | ||
| Value Score | — | 100.0 |
| Reasoning Score | 50.4 | 50.5 |
| Intelligence | 18.5 | 45.8 |
| Coding | 9.4 | 36.5 |
| Math | 54.7 | — |
| Standard Benchmarks | ||
| GPQA | 64.3% | 84.2% |
| MMLU Pro | 78.5% | — |
| HLE | 6.5% | 21.6% |
| LiveCodeBench | 27.7% | — |
| MATH 500 | 95.9% | — |
| AIME 2025 | 54.7% | — |
| AIME (Original) | 58.3% | — |
| SciCode | 28.2% | 39.8% |
| LCR | 17.0% | 68.7% |
| IFBench | 38.1% | 67.6% |
| TAU-bench v2 | 26.9% | 94.2% |
| TerminalBench Hard | 0.0% | 34.8% |
Key Takeaways
Llama 3.3 Nemotron Super 49B v1 (Reasoning) offers the best value at $0.00/1M, making it ideal for high-volume applications and cost-conscious projects.
Qwen3.6 27B (Reasoning) has the strongest reasoning profile with a 50.5 reasoning score, combining the available reasoning-heavy benchmarks.
Qwen3.6 27B (Reasoning) reaches a 36.5 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 (Reasoning)
- Cost-sensitive applications
- High-volume processing
Qwen3.6 27B (Reasoning)
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
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