Claude 3.5 Haiku vs Llama 4 Scout
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 4 Scout.
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 4 Scout
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
Technical Specifications
| Specification | Claude 3.5 Haiku | Llama 4 Scout |
|---|---|---|
| Provider | Anthropic | Meta |
| Context Window | 200,000 tokens | 10,000,000 tokens |
| Agent Suitability | N/A | 82/100 |
| Time to First Token (TTFT) | N/A | 350 ms |
| Deployment Model | N/A | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2024-11-04 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Claude 3.5 Haiku
$0.80
Llama 4 Scout
$0.10
Output Price per Million Tokens
Claude 3.5 Haiku
$4.00
Llama 4 Scout
$0.30
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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 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling