DeepSeek V4 Pro Benchmark Test Reveals Low Performance but Significant Cost Savings with Cache-Aware Pricing
A developer's open benchmark test shows DeepSeek V4 Pro achieving only 5.3% on a coding benchmark while demonstrating 97% cost reduction through cache-aware pricing.
Practical Summary
A developer executed a reproducible benchmark test against DeepSeek's official API using the V4 Pro model with reasoning Max setting. The test consumed approximately 1 billion tokens but yielded only 5.3% success rate on the benchmark. However, the detailed cost analysis revealed that cache-aware pricing reduced the total cost from an estimated $433.76 to just $12.18, demonstrating the critical importance of cache-aware pricing for large-scale API usage. The methodology was clean with no infrastructure issues, and all code and verifiers are open for inspection.
Why It Matters
This provides concrete evidence for two key practical considerations: (1) Performance benchmarks can vary significantly across models, requiring hands-on testing rather than relying on marketing claims, and (2) Cache-aware pricing mechanisms can reduce API costs by orders of magnitude for large token consumption, making previously prohibitive workloads economically feasible. The transparent methodology allows others to replicate and verify the findings, supporting informed decision-making about model selection and cost optimization.
DeepSeek V4 Pro Benchmark Test Reveals Low Performance but Significant Cost Savings with Cache-Aware Pricing
A developer executed a reproducible benchmark test against DeepSeek's official API using the V4 Pro model with reasoning Max setting. The test consumed approximately 1 billion tokens but yielded only 5.3% success rate on the benchmark. However, the detailed cost analysis revealed that cache-aware pricing reduced the total cost from an estimated $433.76 to just $12.18, demonstrating the critical importance of cache-aware pricing for large-scale API usage. The methodology was clean with no infrastructure issues, and all code and verifiers are open for inspection.
This provides concrete evidence for two key practical considerations: (1) Performance benchmarks can vary significantly across models, requiring hands-on testing rather than relying on marketing claims, and (2) Cache-aware pricing mechanisms can reduce API costs by orders of magnitude for large token consumption, making previously prohibitive workloads economically feasible. The transparent methodology allows others to replicate and verify the findings, supporting informed decision-making about model selection and cost optimization.