Model Comparison
Llama 3.3 Instruct 70B
vs. Qwen3.5 9B (Non-reasoning)
Comparing 2 AI models · 12 benchmarks · Meta, Alibaba
Recommended Pick
Al Qwen3.5 9B (Non-reasoning) 13 metric wins Strongest on: Input price, Output price, TTFT
Lowest Price
Qwen3.5 9B (Non-reasoning)
$0.00/1M input price
Best Reasoning
Qwen3.5 9B (Non-reasoning)
38.2 reasoning score
Blends available reasoning benchmarks
Best for Coding
Qwen3.5 9B (Non-reasoning)
21.3 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
Qwen3.5 9B (Non-reasoning) is ∞x cheaper on input tokens than Llama 3.3 Instruct 70B.
Reasoning gap
Qwen3.5 9B (Non-reasoning) leads Llama 3.3 Instruct 70B by 10.9 points on reasoning.
Coding gap
Qwen3.5 9B (Non-reasoning) leads Llama 3.3 Instruct 70B by 10.6 points on coding.
Top-pick rationale
Qwen3.5 9B (Non-reasoning) wins 13 measurable categories, including Input price, Output price, TTFT, Latency.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Llama 3.3 Instruct 70B
Meta
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Qwen3.5 9B (Non-reasoning)
Alibaba
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
Full Comparison
| Metric | Me Llama 3.3 Instruct 70B | Top Pick Al Qwen3.5 9B (Non-reasoning) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.58/1M | $0.00/1M |
| Output Cost | $0.71/1M | $0.00/1M |
| Blended (3:1) | $0.61/1M | — |
| Specifications | ||
| Organization | Meta | Alibaba |
| Release Date | Dec 6, 2024 | Mar 2, 2026 |
| Performance & Speed | ||
| Throughput | 95.0 tok/s | — |
| TTFT | 628ms | — |
| Latency | 628ms | — |
| Composite Indices | ||
| Value Score | 100.0 | — |
| Reasoning Score | 27.3 | 38.2 |
| Intelligence | 14.5 | 27.3 |
| Coding | 10.7 | 21.3 |
| Math | 7.7 | — |
| Standard Benchmarks | ||
| GPQA | 49.8% | 78.6% |
| MMLU Pro | 71.3% | — |
| HLE | 4.0% | 8.6% |
| LiveCodeBench | 28.8% | — |
| MATH 500 | 77.3% | — |
| AIME 2025 | 7.7% | — |
| AIME (Original) | 30.0% | — |
| SciCode | 26.0% | 27.7% |
| LCR | 15.0% | 38.0% |
| IFBench | 47.1% | 37.8% |
| TAU-bench v2 | 26.6% | 85.1% |
| TerminalBench Hard | 3.0% | 18.2% |
Key Takeaways
Qwen3.5 9B (Non-reasoning) offers the best value at $0.00/1M, making it ideal for high-volume applications and cost-conscious projects.
Qwen3.5 9B (Non-reasoning) has the strongest reasoning profile with a 38.2 reasoning score, combining the available reasoning-heavy benchmarks.
Qwen3.5 9B (Non-reasoning) reaches a 21.3 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 Instruct 70B
- General-purpose AI
- Versatile applications
Qwen3.5 9B (Non-reasoning)
- Cost-sensitive applications
- High-volume processing
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
