| Signal | GPT-4 Turbo | Delta | Llama 3.2 11B Vision Instruct |
|---|---|---|---|
Capabilities | 67 | +17 | |
Benchmarks | 80 | +80 | |
Pricing | 30 | +30 | |
Context window size | 81 | 0 | |
Recency | 4 | -31 | |
Output Capacity | 60 | -10 | |
| Overall Result | 3 wins | of 6 | 3 wins |
22
days ranked higher
4
days
4
days ranked higher
OpenAI
Meta
Llama 3.2 11B Vision Instruct saves you $2492.65/month
That's $29911.80/year compared to GPT-4 Turbo at your current usage level of 100K calls/month.
| Metric | GPT-4 Turbo | Llama 3.2 11B Vision Instruct | Winner |
|---|---|---|---|
| Overall Score | 60 | 56 | GPT-4 Turbo |
| Rank | #207 | #226 | GPT-4 Turbo |
| Quality Rank | #207 | #226 | GPT-4 Turbo |
| Adoption Rank | #207 | #226 | GPT-4 Turbo |
| Parameters | -- | 11B | -- |
| Context Window | 128K | 131K | Llama 3.2 11B Vision Instruct |
| Pricing | $10.00/$30.00/M | $0.05/$0.05/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 50 | GPT-4 Turbo |
| Benchmarks | 80 | -- | GPT-4 Turbo |
| Pricing | 30 | 0 | GPT-4 Turbo |
| Context window size | 81 | 81 | Llama 3.2 11B Vision Instruct |
| Recency | 4 | 35 | Llama 3.2 11B Vision Instruct |
| Output Capacity | 60 | 70 | Llama 3.2 11B Vision Instruct |
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 60/100 (rank #207), placing it in the top 29% of all 290 models tracked.
Scores 56/100 (rank #226), placing it in the top 22% of all 290 models tracked.
With only a 3-point gap, these models are in the same performance tier. The practical difference in output quality is minimal — your choice should depend on pricing, latency requirements, and specific feature needs.
Llama 3.2 11B Vision Instruct offers 100% better value per quality point. At 1M tokens/day, you'd spend $1.47/month with Llama 3.2 11B Vision Instruct vs $600.00/month with GPT-4 Turbo — a $598.53 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 (131K 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 (60/100) 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
GPT-4 Turbo has a moderate advantage with a 3.4000000000000057-point lead in composite score. It wins on more signal dimensions, but Llama 3.2 11B Vision Instruct has specific strengths that could make it the better choice for certain workflows.
Best for Quality
GPT-4 Turbo
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.2 11B Vision Instruct
100% lower pricing; better value at scale
Best for Reliability
GPT-4 Turbo
Higher uptime and faster response speeds
Best for Prototyping
GPT-4 Turbo
Stronger community support and better developer experience
Best for Production
GPT-4 Turbo
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-4 Turbo | Llama 3.2 11B Vision Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
Meta
Llama 3.2 11B Vision Instruct saves you $53.85/month
That's 100% cheaper than GPT-4 Turbo 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 | GPT-4 Turbo | Llama 3.2 11B Vision Instruct |
|---|---|---|
| Context Window | 128K | 131K |
| Max Output Tokens | 4,096 | 16,384 |
| Open Source | Yes | Yes |
| Created | Apr 9, 2024 | Sep 25, 2024 |
GPT-4 Turbo scores 60/100 (rank #207) compared to Llama 3.2 11B Vision Instruct's 56/100 (rank #226), giving it a 3-point advantage. GPT-4 Turbo is the stronger overall choice, though Llama 3.2 11B Vision Instruct may excel in specific areas like cost efficiency.
GPT-4 Turbo is ranked #207 and Llama 3.2 11B Vision Instruct is ranked #226 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 GPT-4 Turbo's $30.00/M output tokens — 612.2x more expensive. Input token pricing: GPT-4 Turbo at $10.00/M vs Llama 3.2 11B Vision Instruct at $0.05/M.
Llama 3.2 11B Vision Instruct has a larger context window of 131,072 tokens compared to GPT-4 Turbo's 128,000 tokens. A larger context window means the model can process longer documents and conversations.