DeepSeek v4 Flash Beats Opus 4.7 in Agentic Task Cost by 97%
Measure cache hit rate and read/write price ratio when benchmarking LLMs; DeepSeek v4 flash achieves 97% hit rate and 0.02 ratio, cutting cost to $0.01 per task.
Benchmark your own LLMs for cache hit rate and read/write price ratio, aiming for >90% hit rate and <0.05 ratio to approach DeepSeek's cost efficiency.
Summary
DeepSeek v4 flash costs $0.01 per task versus Opus 4.7’s $1.52, with similar token usage (~961k) and slightly more tool calls (14.8 vs 12.8). The benchmark on OpenRouter using OpenClaw shows DeepSeek’s cost is 0.0066× that of Opus, thanks to a 97% cache hit rate compared to Opus’s 87% and a 0.02 read/write price ratio versus 0.08. A 1% higher cache hit rate can reduce overall cost by ~20%, and DeepSeek’s 10% higher hit rate cuts about two‑thirds of the total cost. The low read/write ratio further halves the cost. The secret weapon is DeepSeek’s caching efficiency, which dramatically lowers per‑token expenses. The data was collected with OpenRouter as the model provider and OpenClaw for the agent loop. These findings suggest that caching strategy is a key lever for cost optimization in large‑model deployments.
Key changes
- DeepSeek v4 flash costs $0.01 per task vs Opus 4.7 $1.52.
- Tokens per task: ~961k for both models.
- Tools per task: 14.8 for DS vs 12.8 for Opus.
- Cache hit rate: 97% for DS vs 87% for Opus.
- Cache read/write price ratio: 0.02 for DS vs 0.08 for Opus.
- 1% higher hit rate reduces cost by ~20%.
- DS’s 10% higher hit rate cuts ~2/3 of total cost.
- Low read/write ratio halves cost.