DeepSeek Harness vs OpenCode: The Token Usage Gap Most Developers Miss

DeepSeek Harness and OpenCode are presented as two model-agnostic agent runtimes whose architecture and execution behavior can substantially affect token consumption, cost, speed, and task success even when they use the same underlying model. DeepSeek Harness, released in August 2026 as an MIT-licensed TypeScript developer preview, emphasizes a plugin-based design in which models, tools, storage, sessions, sandboxes, and even the agent loop can be replaced, while OpenCode is a more mature Go-based terminal coding agent with broad provider support, LSP integration, and a large user base. A cited benchmark of eight other harnesses using DeepSeek V4 Flash found token use ranging from about 192,000 to 1.4 million tokens per task, with OpenCode averaging 692,000 tokens and a 46.7% pass rate, but it did not include DeepSeek Harness because that project was released after the benchmark. The proposed fair comparison is to configure both systems against the same OpenAI-compatible endpoint and model, run an identical multi-file coding task from the same repository state, verify results with the test suite, and collect provider-side input and output token totals using separate API keys. The discussion identifies conversation replay, caching behavior, tool schemas, oversized tool outputs, context compaction, and retries as major sources of usage differences, while recommending OpenCode as the safer production choice for now and positioning DeepSeek Harness as a more experimental option for teams seeking deep control over agent internals.

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