If I want to get the most out of an AI coding assistant and I have the choice between choosing a more able model (Claude sonnet 4.6 vs 4.5) or using more words to increase the quality of response, for maximizing token efficiency, should I use less words and a more capable model?

Grok 4.3

Vote: Yes

For maximizing token efficiency specifically, a more capable model produces better results per token consumed. A stronger model needs less hand-holding, fewer clarifications, fewer retry attempts, and less verbose prompting to arrive at a correct answer. Adding more words to compensate for a weaker model increases input tokens and often output tokens (due to corrections), reducing efficiency. So using fewer words with a more capable model is the better strategy when the metric is token efficiency.

Gemini 3.5 Flash

Vote: Yes

For token efficiency specifically (output quality per token consumed), a more capable model generally produces better results with fewer input tokens. Verbose prompts inflate token usage on every turn, while a stronger model can infer intent from concise instructions and produce more accurate output on the first try, reducing costly retry loops. Since the question explicitly optimizes for token efficiency, the better model + concise prompt wins. Note: for raw quality regardless of tokens, combining both is best — but that's not what was asked.

Claude Opus 4.8

Vote: Yes

For maximizing token efficiency specifically, a more capable model gets you better results per token spent. Verbose prompting consumes input tokens on every request and often induces longer outputs, while a stronger model can extract intent from concise prompts and produce correct answers in fewer iterations (fewer retries, less back-and-forth debugging). Since token efficiency = quality of output ÷ tokens consumed, upgrading model capability typically dominates padding prompts with extra words. Clear, concise prompts on a stronger model is the efficient frontier.

GPT-5.5

Vote: Yes

To maximize token efficiency, using fewer words with a more capable model (Option A: Yes) is the superior approach. More capable models possess superior reasoning, broader general knowledge, and better instruction-following capabilities. Consequently, they can grasp complex requirements from concise prompts without requiring extensive context, verbose explanations, or multiple few-shot examples. This drastically reduces the input token count. Conversely, achieving the same level of performance from a less capable model requires highly verbose prompting, detailed specifications, and iterative corrections. This results in a massive overhead of both input and output tokens due to repeated trials, ultimately destroying token efficiency.

Final Summary

The AI Roundtable reached a unanimous consensus that upgrading to a more capable model while keeping prompts concise is the superior strategy for maximizing token efficiency. Models Claude Opus 4.8, GPT-5.5, Grok 4.3, and Gemini 3.5 Flash all independently chose Option A, highlighting that stronger reasoning capabilities eliminate the need for verbose instructions and costly debugging cycles.

No agreement after 1 rounds of discussion. Final vote: Yes: 4

Strongest Arguments

• Yes: Upgrading model capability dominates padding prompts with extra words because token efficiency is measured by output quality divided by tokens consumed; more capable models extract intent from concise prompts and achieve correct answers in fewer iterations, avoiding the massive overhead of iterative debugging.