| Signal | Grok 4.20 Multi-Agent Beta | Delta | Llama 3.2 11B Vision Instruct |
|---|---|---|---|
Capabilities | 83 | +33 | |
Pricing | 6 | +6 | |
Context window size | 100 | +19 | |
Recency | 100 | +65 | |
Output Capacity | 20 | -50 | |
| Overall Result | 4 wins | of 5 | 1 wins |
30
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xAI
Meta
Llama 3.2 11B Vision Instruct saves you $492.65/month
That's $5911.80/year compared to Grok 4.20 Multi-Agent Beta at your current usage level of 100K calls/month.
| Metric | Grok 4.20 Multi-Agent Beta | Llama 3.2 11B Vision Instruct | Winner |
|---|---|---|---|
| Overall Score | 81 | 56 | Grok 4.20 Multi-Agent Beta |
| Rank | #69 | #226 | Grok 4.20 Multi-Agent Beta |
| Quality Rank | #69 | #226 | Grok 4.20 Multi-Agent Beta |
| Adoption Rank | #69 | #226 | Grok 4.20 Multi-Agent Beta |
| Parameters | -- | 11B | -- |
| Context Window | 2000K | 131K | Grok 4.20 Multi-Agent Beta |
| Pricing | $2.00/$6.00/M | $0.05/$0.05/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 50 | Grok 4.20 Multi-Agent Beta |
| Pricing | 6 | 0 | Grok 4.20 Multi-Agent Beta |
| Context window size | 100 | 81 | Grok 4.20 Multi-Agent Beta |
| Recency | 100 | 35 | Grok 4.20 Multi-Agent Beta |
| Output Capacity | 20 | 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 81/100 (rank #69), placing it in the top 77% of all 290 models tracked.
Scores 56/100 (rank #226), placing it in the top 22% of all 290 models tracked.
Grok 4.20 Multi-Agent Beta has a 24-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 99% better value per quality point. At 1M tokens/day, you'd spend $1.47/month with Llama 3.2 11B Vision Instruct vs $120.00/month with Grok 4.20 Multi-Agent Beta — a $118.53 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 (2000K 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 (81/100) 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
Grok 4.20 Multi-Agent Beta clearly outperforms Llama 3.2 11B Vision Instruct with a significant 24.200000000000003-point lead. For most general use cases, Grok 4.20 Multi-Agent Beta is the stronger choice. However, Llama 3.2 11B Vision Instruct may still excel in niche scenarios.
Best for Quality
Grok 4.20 Multi-Agent Beta
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.2 11B Vision Instruct
99% lower pricing; better value at scale
Best for Reliability
Grok 4.20 Multi-Agent Beta
Higher uptime and faster response speeds
Best for Prototyping
Grok 4.20 Multi-Agent Beta
Stronger community support and better developer experience
Best for Production
Grok 4.20 Multi-Agent Beta
Wider enterprise adoption and proven at scale
by xAI
| Capability | Grok 4.20 Multi-Agent Beta | Llama 3.2 11B Vision Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Searchdiffers | ||
| Image Output |
xAI
Meta
Llama 3.2 11B Vision Instruct saves you $10.65/month
That's 99% cheaper than Grok 4.20 Multi-Agent Beta 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 | Grok 4.20 Multi-Agent Beta | Llama 3.2 11B Vision Instruct |
|---|---|---|
| Context Window | 2M | 131K |
| Max Output Tokens | -- | 16,384 |
| Open Source | No | Yes |
| Created | Mar 12, 2026 | Sep 25, 2024 |
Grok 4.20 Multi-Agent Beta scores 81/100 (rank #69) compared to Llama 3.2 11B Vision Instruct's 56/100 (rank #226), giving it a 24-point advantage. Grok 4.20 Multi-Agent Beta is the stronger overall choice, though Llama 3.2 11B Vision Instruct may excel in specific areas like cost efficiency.
Grok 4.20 Multi-Agent Beta is ranked #69 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 Grok 4.20 Multi-Agent Beta's $6.00/M output tokens — 122.4x more expensive. Input token pricing: Grok 4.20 Multi-Agent Beta at $2.00/M vs Llama 3.2 11B Vision Instruct at $0.05/M.
Grok 4.20 Multi-Agent Beta has a larger context window of 2,000,000 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.