Llama 3.1 8B vs Llama 4 Scout
Compare Meta (via Together AI) and Meta (via Together AI) AI models
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
Llama 3.1 8B
Llama 4 Scout
Cost Differences
Llama 4 Scout costs less than Llama 3.1 8B
Feature Comparison
| Feature | Llama 3.1 8B | Llama 4 Scout |
|---|---|---|
| Provider | Meta (via Together AI) | Meta (via Together AI) |
| Input Price | $0.18/1M tokens | $0.08/1M tokens |
| Output Price | $0.18/1M tokens | $0.30/1M tokens |
| Context Window | 128,000 tokens | 10,000,000 tokens |
| Max Output | 32,768 tokens | 32,768 tokens |
| Category | efficient | efficient |
| Capabilities | textcode | textvisioncode |
| Release Date | 7/23/2024 | 4/5/2025 |
Llama 3.1 8B vs Llama 4 Scout: Which Should You Choose?
Choosing between Llama 3.1 8B and Llama 4 Scout depends on your priorities: cost efficiency, context length, or raw capability. Llama 3.1 8B is the more affordable option at $0.18/1M input tokens. Meanwhile, Llama 4 Scout offers a significantly larger context window at 10,000,000 tokens vs 128,000 for Llama 3.1 8B.
Both models are in the efficient category, making this a direct head-to-head comparison. At scale — say 10,000 requests per day — the cost difference adds up: Llama 3.1 8B would save you roughly $12.00/month compared to Llama 4 Scout. For startups and indie developers, that difference can be significant.
Output costs matter too. Llama 3.1 8B charges $0.18/1M output tokens vs $0.30 for Llama 4 Scout. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. Llama 3.1 8B has the edge here at $0.18/1M output tokens.
Multimodal capabilities: Llama 4 Scout supports vision (image inputs) while Llama 3.1 8B is text-only. If your application needs image understanding, this narrows your choice.
Best Use Cases
Choose Llama 3.1 8B when:
- • You're already using Meta (via Together AI)'s API ecosystem
- • You're running high-volume, latency-sensitive workloads
Choose Llama 4 Scout when:
- • Budget is a primary concern
- • You need a larger context window (10,000,000 tokens)
- • You need more capabilities (vision)
- • You're already using Meta (via Together AI)'s API ecosystem
- • You're running high-volume, latency-sensitive workloads
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
Llama 3.1 8B (Meta (via Together AI))
Llama 4 Scout (Meta (via Together AI))
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