Claude 3.5 Haiku vs Llama 3.2 11B Vision
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Claude 3.5 Haiku and Llama 3.2 11B Vision.
Claude 3.5 Haiku
Claude 3.5 Haiku is the fast, cost-efficient member of the Claude 3.5 model family from Anthropic, built to deliver strong performance for coding, text processing, and multi-turn conversation at minimal inference cost. With a 200,000-token context window and pricing at $0.80/MTok for input, it is optimized for high-throughput, latency-sensitive production applications such as real-time chat interfaces, code completion tools, and classification systems. While smaller than its Sonnet and Opus siblings, Claude 3.5 Haiku retains Anthropic's strong alignment and safety properties, making it a reliable choice for consumer-facing AI features.
Llama 3.2 11B Vision
Meta's lightweight open weights vision model, optimized for mobile devices and local deployments. Capable of visual understanding, chart reading, and fast text generation.
Technical Specifications
| Specification | Claude 3.5 Haiku | Llama 3.2 11B Vision |
|---|---|---|
| Provider | Anthropic | Meta |
| Context Window | 200,000 tokens | 131,072 tokens |
| Agent Suitability | N/A | 72/100 |
| Time to First Token (TTFT) | N/A | 150 ms |
| Deployment Model | N/A | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-11-04 | 2024-09-25 |
API Pricing Comparison
Input Price per Million Tokens
Claude 3.5 Haiku
$0.80
Llama 3.2 11B Vision
$0.34
Output Price per Million Tokens
Claude 3.5 Haiku
$4.00
Llama 3.2 11B Vision
$0.34
Want to test both models live?
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Claude 3.5 Haiku Quirks & Gotchas
No developer gotchas reported.
Llama 3.2 11B Vision Quirks & Gotchas
- ▸Lightweight vision model for edge/on-device deployments
- ▸Limited tool calling — use Llama 4 for production agentic tasks