Provider: Anthropic API (via OpenClaw) Context window: 200k tokens Task: Multi-step tool calling chain (weather → Bulgarian description → markdown file → email with attachment)
| Criteria | Score | Max |
|---|---|---|
| Correct tool call sequence | 3 | 3 |
| Tool call accuracy | 2 | 3 |
| Final output quality | 2 | 2 |
| Efficiency & communication | 1.5 | 2 |
- Time: ~1 minute
- Cost: ~$0.30
- User interventions: 0
All 4 steps completed successfully, but with an engineering workaround for the attachment:
- Weather data — Retrieved via Open-Meteo API (temperature, humidity, wind, condition).
- Bulgarian description — "Малка облачност (weathercode 2)" — functional description.
- Markdown file — Created with correct structure.
- Email — Couldn't attach the file using native tools (himalaya/gog). Instead of asking the user or giving up, wrote a Python script (
send_email_with_attachment.py) to handle SMTP with attachment. Email sent successfully with the file attached.
- Engineering problem-solving — When native tools failed, Sonnet didn't ask for help or report failure. It wrote a custom script and solved the problem. This is a distinctly "Sonnet" behavior — creative, autonomous, slightly over-engineered.
- Showed reasoning process during execution — transparent about what it was doing.
- Zero user interventions — fully autonomous.
- Created a reusable script as a side effect — potentially useful for future tasks.
- The script remained in the workspace and may have been used by Opus 4.5 in the subsequent test (sessions were not filesystem-isolated).
- Cost: $0.30 for a single task — roughly 6x what Haiku costs, though Haiku failed the task.
- Approach: Writing a custom Python script is resourceful but inefficient compared to MiniMax and Kimi which used native tools directly. It also introduces a dependency (SMTP credentials, Python environment) that native tools don't have.
Classic Sonnet behavior: when the straightforward path doesn't work, engineer around it. The task was completed fully — email with attachment delivered — but the method was heavier than necessary. For a one-off task this is fine; for a repeatable agent workflow, you'd want native tool support. Still, zero user interventions and a working result puts it well ahead of models that simply failed.