Neither reasoning_effort nor Vercel's reasoning.effort object shows reliable
effort scaling for thinkingmachines/inkling through Vercel AI Gateway.
Thinking Machines documents reasoning_effort for its OpenAI-compatible Chat
Completions API and maps low to 0.2 and xhigh to 0.99. It also warns
that individual samples are stochastic, so this reproduction compares multiple
samples with enough output headroom to avoid truncation.
AI_GATEWAY_API_KEY=... node inkling_reasoning_effort_sweep.mjs
AI_GATEWAY_API_KEY=... REASONING_FORMAT=reasoning node inkling_reasoning_effort_sweep.mjsTwo independent five-sample runs per parameter format, with the same prompt and Vercel's Baseten route forced:
| Format | Run | low mean |
xhigh mean |
Direction |
|---|---|---|---|---|
reasoning_effort |
1 | 4,656.8 | 3,675.6 | reversed |
reasoning_effort |
2 | 3,545.0 | 4,723.8 | expected |
reasoning_effort |
combined | 4,100.9 | 4,199.7 | +2.4% |
reasoning.effort |
1 | 3,861.4 | 4,410.8 | expected |
reasoning.effort |
2 | 4,403.6 | 4,209.2 | reversed |
reasoning.effort |
combined | 4,132.5 | 4,310.0 | +4.3% |
The combined distributions overlap heavily:
reasoning_effortlow: 2,968–7,069; xhigh: 2,887–5,625reasoning.effortlow: 3,277–5,606; xhigh: 3,465–6,086
Both formats reverse direction between repeated batches. Their combined differences are only 2.4% and 4.3%, with heavily overlapping distributions, despite comparing near-opposite effort presets. The setting appears ignored or incorrectly mapped.
- Thinking Machines OpenAI-compatible API: https://tinker-docs.thinkingmachines.ai/tinker/compatible-apis/openai/
- Thinking Machines effort behavior: https://tinker-docs.thinkingmachines.ai/cookbook/inkling/thinking-effort/
- Vercel Chat Completions reasoning configuration: https://vercel.com/docs/ai-gateway/sdks-and-apis/openai-chat-completions/advanced#reasoning-configuration