Model Comparison
Llama 3.2 Instruct 11B (Vision)
vs. Qwen3.5 4B (Non-reasoning)
Comparing 2 AI models · 12 benchmarks · Meta, Alibaba
Recommended Pick
Al Qwen3.5 4B (Non-reasoning) 17 metric wins Strongest on: Value, Input price, Output price
Best Value
Qwen3.5 4B (Non-reasoning)
100.0 value score
33.8 reasoning / $0.06/1M
Lowest Price
Qwen3.5 4B (Non-reasoning)
$0.03/1M input price
Best Reasoning
Qwen3.5 4B (Non-reasoning)
33.8 reasoning score
Blends available reasoning benchmarks
Best for Coding
Qwen3.5 4B (Non-reasoning)
13.7 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 4B (Non-reasoning) has the strongest quality-to-price mix at 100.0 out of 100 value points.
Price gap
Qwen3.5 4B (Non-reasoning) is 8.2x cheaper on input tokens than Llama 3.2 Instruct 11B (Vision).
Speed gap
Qwen3.5 4B (Non-reasoning) generates about 2.4x as many tokens per second as Llama 3.2 Instruct 11B (Vision).
Reasoning gap
Qwen3.5 4B (Non-reasoning) leads Llama 3.2 Instruct 11B (Vision) by 19.4 points on reasoning.
Coding gap
Qwen3.5 4B (Non-reasoning) leads Llama 3.2 Instruct 11B (Vision) by 9.5 points on coding.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Llama 3.2 Instruct 11B (Vision)
Meta
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Qwen3.5 4B (Non-reasoning)
Alibaba
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
Full Comparison
| Metric | Me Llama 3.2 Instruct 11B (Vision) | Top Pick Al Qwen3.5 4B (Non-reasoning) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.24/1M | $0.03/1M |
| Output Cost | $0.24/1M | $0.15/1M |
| Blended (3:1) | $0.24/1M | $0.06/1M |
| Specifications | ||
| Organization | Meta | Alibaba |
| Release Date | Sep 25, 2024 | Mar 2, 2026 |
| Performance & Speed | ||
| Throughput | 86.3 tok/s | 208.9 tok/s |
| TTFT | 461ms | 229ms |
| Latency | 461ms | 229ms |
| Composite Indices | ||
| Value Score | 10.4 | 100.0 |
| Reasoning Score | 14.3 | 33.8 |
| Intelligence | 8.7 | 22.6 |
| Coding | 4.2 | 13.7 |
| Math | 1.7 | — |
| Standard Benchmarks | ||
| GPQA | 22.1% | 71.2% |
| MMLU Pro | 46.4% | — |
| HLE | 5.2% | 7.5% |
| LiveCodeBench | 11.0% | — |
| MATH 500 | 51.6% | — |
| AIME 2025 | 1.7% | — |
| AIME (Original) | 9.3% | — |
| SciCode | 11.2% | 18.3% |
| LCR | 11.7% | 28.3% |
| IFBench | 30.4% | 33.3% |
| TAU-bench v2 | 14.6% | 87.7% |
| TerminalBench Hard | 0.8% | 11.4% |
Key Takeaways
Qwen3.5 4B (Non-reasoning) offers the best value at $0.03/1M, making it ideal for high-volume applications and cost-conscious projects.
Qwen3.5 4B (Non-reasoning) has the strongest reasoning profile with a 33.8 reasoning score, combining the available reasoning-heavy benchmarks.
Qwen3.5 4B (Non-reasoning) reaches a 13.7 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 3.2 Instruct 11B (Vision)
- General-purpose AI
- Versatile applications
Qwen3.5 4B (Non-reasoning)
- Cost-sensitive applications
- High-volume processing
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
