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DeepSeek-V2.5 vs Llama 4 Scout
Comprehensive side-by-side LLM comparison
DeepSeek-V2.5 leads with 12.6% higher average benchmark score. Llama 4 Scout offers 20.0M more tokens in context window than DeepSeek-V2.5. Both models have similar pricing. Llama 4 Scout supports multimodal inputs. Llama 4 Scout is available on 6 providers. Overall, DeepSeek-V2.5 is the stronger choice for coding tasks.
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DeepSeek
DeepSeek-V2.5 was developed as an enhanced iteration of the DeepSeek-V2 architecture, designed to incorporate improvements in model quality and efficiency. Built to advance the DeepSeek foundation model series, it provides refined capabilities for general-purpose language understanding and generation tasks.
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Meta
Llama 4 Scout was created as an exploratory variant in the Llama 4 family, designed to investigate new architectures and optimization strategies. Built as part of Meta's commitment to advancing open-source AI, it serves as a testbed for innovations that may inform future model releases.
11 months newer
DeepSeek-V2.5
DeepSeek
2024-05-08
Llama 4 Scout
Meta
2025-04-05
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Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-V2.5
Input:$0.14
Output:$0.28
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Llama 4 Scout
Input:$0.08
Output:$0.30($0.04 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 2 common benchmarks
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DeepSeek-V2.5
Average Score:77.6%(+12.6%)
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Llama 4 Scout
Average Score:65.0%
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-V2.5
3 providers
DeepInfra
Throughput: 63 tok/s
Latency: 0.5ms
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
Hyperbolic
Throughput: 100 tok/s
Latency: 0.5ms
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DeepSeek-V2.5
Avg Score:77.6%(+12.6%)
Providers:3
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Llama 4 Scout
Avg Score:65.0%
Providers:6