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DeepSeek-V2.5 vs Llama 4 Maverick
Comprehensive side-by-side LLM comparison
DeepSeek-V2.5 leads with 4.2% higher average benchmark score. Llama 4 Maverick offers 2.0M more tokens in context window than DeepSeek-V2.5. Both models have similar pricing. Llama 4 Maverick supports multimodal inputs. Llama 4 Maverick is available on 7 providers. Both models have their strengths depending on your specific coding needs.
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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 Maverick was developed as a variant in Meta's fourth-generation language model family, designed to explore specialized capabilities and training approaches. Built to push the boundaries of open-source model development, it represents experimentation with advanced techniques in the Llama lineage.
11 months newer
DeepSeek-V2.5
DeepSeek
2024-05-08
Llama 4 Maverick
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($0.35 cheaper)
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Llama 4 Maverick
Input:$0.17
Output:$0.60
Performance Metrics
Context window and performance specifications
Average performance across 2 common benchmarks
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DeepSeek-V2.5
Average Score:77.6%(+4.2%)
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Llama 4 Maverick
Average Score:73.4%
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%(+4.2%)
Providers:3
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Llama 4 Maverick
Avg Score:73.4%
Providers:7