Agent Test: Switching Models Cuts 70% of Costs
qubridInc · reddit · 2026-07-11
The author shared an internal research agent test: a single task typically requires 40–60 LLM calls, and the cost per task exceeded $1 when using GPT-4o.
After switching most "routing/extraction" calls to open-source models, they ran the same evaluation suite (about 300 runs per model). Conclusions include:
- Kimi K2.7 performed the best in tool calls, with normal parallel calls, high schema compliance, and almost no need for extra defensive parsing.
- GLM 5.2 showed stable performance in long-context scenarios stuffed with 60–80k tokens.
- Qwen 3.7 occasionally returned tool args as string JSON, which could be mitigated by retries.
- For deeply nested schemas with many optional fields, all OSS models underperformed compared to 4o; flattening the schema is recommended.
- Latency fluctuations across different providers for the same model can be larger than differences between models. Evaluations should test specific endpoints rather than just relying on leaderboards.
- DeepSeek V4-Pro had average tool-calling performance in their setup and was not selected.
The final setup is: Kimi for tool steps, GLM for synthesis/generation, and GPT-4o only for the final user-facing answers; this reduced the cost per task from roughly $1.10 to $0.35.
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