| Signal | Llama 3.2 11B Vision Instruct | Delta | Qwen3.5-Flash |
|---|---|---|---|
Capabilities | 50 | -33 | |
Pricing | 0 | 0 | |
Context window size | 81 | -14 | |
Recency | 34 | -66 | |
Output Capacity | 70 | -10 | |
Benchmarks | 0 | -67 | |
| Overall Result | 0 wins | of 6 | 6 wins |
0
days ranked higher
0
days
30
days ranked higher
Meta
Alibaba
Llama 3.2 11B Vision Instruct saves you $12.15/month
That's $145.80/year compared to Qwen3.5-Flash at your current usage level of 100K calls/month.
| Metric | Llama 3.2 11B Vision Instruct | Qwen3.5-Flash | Winner |
|---|---|---|---|
| Overall Score | 54 | 79 | Qwen3.5-Flash |
| Rank | #249 | #80 | Qwen3.5-Flash |
| Quality Rank | #249 | #80 | Qwen3.5-Flash |
| Adoption Rank | #249 | #80 | Qwen3.5-Flash |
| Parameters | 11B | -- | -- |
| Context Window | 131K | 1000K | Qwen3.5-Flash |
| Pricing | $0.05/$0.05/M | $0.07/$0.26/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 83 | Qwen3.5-Flash |
| Pricing | 0 | 0 | Qwen3.5-Flash |
| Context window size | 81 | 95 | Qwen3.5-Flash |
| Recency | 34 | 100 | Qwen3.5-Flash |
| Output Capacity | 70 | 80 | Qwen3.5-Flash |
| Benchmarks | -- | 67 | Qwen3.5-Flash |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 54/100 (rank #249), placing it in the top 14% of all 290 models tracked.
Scores 79/100 (rank #80), placing it in the top 73% of all 290 models tracked.
Qwen3.5-Flash has a 25-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
Llama 3.2 11B Vision Instruct offers 70% better value per quality point. At 1M tokens/day, you'd spend $1.47/month with Llama 3.2 11B Vision Instruct vs $4.88/month with Qwen3.5-Flash - a $3.41 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Llama 3.2 11B Vision Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1000K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.05/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (79/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Qwen3.5-Flash clearly outperforms Llama 3.2 11B Vision Instruct with a significant 25.000000000000007-point lead. For most general use cases, Qwen3.5-Flash is the stronger choice. However, Llama 3.2 11B Vision Instruct may still excel in niche scenarios.
Best for Quality
Llama 3.2 11B Vision Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.2 11B Vision Instruct
70% lower pricing; better value at scale
Best for Reliability
Llama 3.2 11B Vision Instruct
Higher uptime and faster response speeds
Best for Prototyping
Llama 3.2 11B Vision Instruct
Stronger community support and better developer experience
Best for Production
Llama 3.2 11B Vision Instruct
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 3.2 11B Vision Instruct | Qwen3.5-Flash |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Meta
Alibaba
Llama 3.2 11B Vision Instruct saves you $0.2820/month
That's 66% cheaper than Qwen3.5-Flash at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Llama 3.2 11B Vision Instruct | Qwen3.5-Flash |
|---|---|---|
| Context Window | 131K | 1M |
| Max Output Tokens | 16,384 | 65,536 |
| Open Source | Yes | No |
| Created | Sep 25, 2024 | Feb 25, 2026 |
Qwen3.5-Flash scores 79/100 (rank #80) compared to Llama 3.2 11B Vision Instruct's 54/100 (rank #249), giving it a 25-point advantage. Qwen3.5-Flash is the stronger overall choice, though Llama 3.2 11B Vision Instruct may excel in specific areas like cost efficiency.
Llama 3.2 11B Vision Instruct is ranked #249 and Qwen3.5-Flash is ranked #80 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Llama 3.2 11B Vision Instruct is cheaper at $0.05/M output tokens vs Qwen3.5-Flash's $0.26/M output tokens - 5.3x more expensive. Input token pricing: Llama 3.2 11B Vision Instruct at $0.05/M vs Qwen3.5-Flash at $0.07/M.
Qwen3.5-Flash has a larger context window of 1,000,000 tokens compared to Llama 3.2 11B Vision Instruct's 131,072 tokens. A larger context window means the model can process longer documents and conversations.