DeepSeek R1 V3.2 vs Llama 4 Scout
Compare DeepSeek and Meta (via Together AI) AI models
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
DeepSeek R1 V3.2
Llama 4 Scout
Cost Differences
Llama 4 Scout costs less than DeepSeek R1 V3.2
Feature Comparison
| Feature | DeepSeek R1 V3.2 | Llama 4 Scout |
|---|---|---|
| Provider | DeepSeek | Meta (via Together AI) |
| Input Price | $0.28/1M tokens | $0.08/1M tokens |
| Output Price | $0.42/1M tokens | $0.30/1M tokens |
| Context Window | 128,000 tokens | 10,000,000 tokens |
| Max Output | 65,536 tokens | 32,768 tokens |
| Category | reasoning | efficient |
| Capabilities | textreasoningcode | textvisioncode |
| Release Date | 1/20/2025 | 4/5/2025 |
DeepSeek R1 V3.2 vs Llama 4 Scout: Which Should You Choose?
Choosing between DeepSeek R1 V3.2 and Llama 4 Scout depends on your priorities: cost efficiency, context length, or raw capability. Llama 4 Scout is the more affordable option at $0.08/1M input tokens — 71% cheaper than DeepSeek R1 V3.2. Meanwhile, Llama 4 Scout offers a significantly larger context window at 10,000,000 tokens vs 128,000 for DeepSeek R1 V3.2.
These models come from different providers — DeepSeek and Meta (via Together AI) — which means different API ecosystems, SDKs, rate limits, and terms of service. If you're already integrated with DeepSeek, switching to Meta (via Together AI)involves migration effort beyond just pricing. Factor in your existing infrastructure when deciding.
These models target different tiers: DeepSeek R1 V3.2 is a reasoning model while Llama 4 Scout is efficient. This means they're optimized for different workloads. DeepSeek R1 V3.2 is built for complex tasks that require deeper reasoning, while Llama 4 Scout offers better value for routine operations.
Output costs matter too. DeepSeek R1 V3.2 charges $0.42/1M 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 4 Scout has the edge here at $0.30/1M output tokens.
Multimodal capabilities: Llama 4 Scout supports vision (image inputs) while DeepSeek R1 V3.2 is text-only. If your application needs image understanding, this narrows your choice.
Best Use Cases
Choose DeepSeek R1 V3.2 when:
- • You need longer outputs (up to 65,536 tokens)
- • You're already using DeepSeek's API ecosystem
Choose Llama 4 Scout when:
- • Budget is a primary concern
- • You need a larger context window (10,000,000 tokens)
- • 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
DeepSeek R1 V3.2 (DeepSeek)
Llama 4 Scout (Meta (via Together AI))
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