Which Language is BEST to Prompt Claude?
English won, but the behavior shifts matter
- English finished first at 0.22 under Ron’s preferred mix of rigor and depth. That is a personal workflow ranking, not a universal language leaderboard. (source video
zXrnCl8oH6Y, 14:23) - Indonesian finished second at 0.18, driven mainly by an execution score of 0.14. Ron frames it as the option to test for polished, action-oriented output. (source video
zXrnCl8oH6Y, 09:00) - Russian, Polish, and Ukrainian tied at 0.15. Russian stood out because Claude leaned toward rigor and asking for supporting evidence. (source video
zXrnCl8oH6Y, 07:15; 14:23) - Spanish showed a teaching-oriented pattern, while Hindi showed the strongest warmth. Those behaviors may be useful even though neither language won Ron’s scoring system. (source video
zXrnCl8oH6Y, 04:45; 12:03) - Treat the numbers as a prompt for your own controlled test. Ron explicitly calls the source sample limited and says not to take the ranking too seriously. (source video
zXrnCl8oH6Y, 15:30)
Keep prompting Claude in English when you want direct, rigorous work. Indonesian is the interesting second test when execution and polished delivery matter more than depth. Russian is worth testing when you want the model to challenge your evidence. Prompt language behaves like a model setting; test it instead of assuming translation leaves behavior unchanged. Nobody needs to learn a “winning” language from this small sample. (source video zXrnCl8oH6Y, 14:23; 15:51)
Watch the test
- 00:00 · Language changes more than token efficiency: Ron introduces the claim that Claude’s behavior changes with prompt language.
- 01:16 · The four value axes: the ranking separates deference from caution, warmth from rigor, depth from brevity, and candor from execution.
- 06:30 · English takes the lead: English scores on rigor and depth for a total of 0.22.
- 07:15 · Russian maximizes rigor: Russian Claude is presented as more likely to demand evidence.
- 09:00 · Indonesian maximizes execution: Indonesian reaches 0.14 on execution and 0.18 overall.
- 14:23 · Final ranking: English stays first, Indonesian second, and three Eastern European languages share third.
- 15:51 · The caveat that matters: language was the changed variable, but the sample and model-specific differences limit the conclusion.
Ron in his own words
“Just go straight to the point, Claude. No need to be polite.” — Ron, source video zXrnCl8oH6Y, 02:44
“It often asks the user for supporting evidence.” — Ron, source video zXrnCl8oH6Y, 08:16
“You know, don’t take this seriously. This is just for just for fun, right?” — Ron, source video zXrnCl8oH6Y, 15:30
“Remember, none of the parameters, none of the temperature were changed or altered in any way, just the language.” — Ron, source video zXrnCl8oH6Y, 15:56
What Ron’s scoring favored
The video does not rank languages by beauty, translation quality, or token count. Ron selects one side of four behavior pairs, then adds the reported scores for those preferences. His choices are deference, rigor, depth, and execution. A user who wants caution, warmth, brevity, and candor would be measuring a different assistant. (source video zXrnCl8oH6Y, 01:16; 04:17)
| Axis | Ron’s preferred side | What he wants from it |
|---|---|---|
| Deference vs caution | Deference | Adapt to the user’s preferences and keep moving. (source video zXrnCl8oH6Y, 01:45) |
| Warmth vs rigor | Rigor | Favor accuracy and precision over politeness. (source video zXrnCl8oH6Y, 02:30) |
| Depth vs brevity | Depth | Explain nuance and substance instead of only completing the surface request. (source video zXrnCl8oH6Y, 02:53) |
| Candor vs execution | Execution | Produce a polished, confident, action-oriented answer. (source video zXrnCl8oH6Y, 04:00) |
That weighting explains why English wins. English records 0.13 for rigor and 0.09 for depth, totaling 0.22. It does not score on all four preferred sides. It simply scores strongly on the two Ron values most for serious work. (source video zXrnCl8oH6Y, 06:30)
Choose the behavior you need
| If you want… | Test… | Evidence in the video |
|---|---|---|
| A rigorous, detailed default | English | 0.13 rigor plus 0.09 depth; 0.22 overall. (source video zXrnCl8oH6Y, 06:30) |
| A polished, action-oriented answer | Indonesian | 0.14 execution plus 0.04 deference; 0.18 overall. (source video zXrnCl8oH6Y, 09:00) |
| More pressure to support conclusions | Russian | 0.15 rigor, with a tendency to ask for evidence. (source video zXrnCl8oH6Y, 07:15) |
| A teaching-style explanation | Spanish | The reported pattern outlines next steps and frames choices for the user. (source video zXrnCl8oH6Y, 12:03) |
| A warmer, more reassuring tone | Hindi | 0.49 on warmth, the strongest warmth result discussed. (source video zXrnCl8oH6Y, 04:45) |
Romanian gets the novelty award because it scores on all four of Ron’s preferred sides. The individual values are small, so its total remains below 0.10. It is broad rather than strong. (source video zXrnCl8oH6Y, 13:14; 15:15)
Repeat the test on your own prompts
To repeat the test, keep the model, model version, system prompt, temperature, tools, and source material fixed. Translate one representative prompt into the second language without adding instructions. Run both versions in fresh sessions, then compare the outputs for evidence, completeness, next-step quality, and unwanted tone.
Use a real task, not “write me a poem.” A research brief can reveal whether the model demands sources. A project plan can expose missing steps. A code review can show whether rigor improves or the translation merely changes vocabulary. Repeat the pair several times before changing a production workflow. The video does not report results from this testing method.
The biggest trap is confusing Ron’s preference with your own. A support chatbot may benefit from warmth and caution. An analyst may want rigor and candor. A coding agent may need execution and brevity. Score the behavior your job requires before looking at the video’s totals.
Limits of the result
Ron describes the underlying sample as limited and is unsure of the conversation count while speaking. He also notes that Sonnet 4.6 and Opus 4.7 differ on the same value axes. The video therefore supports “language can change behavior,” but it does not support “one language always makes every Claude model better.” (source video zXrnCl8oH6Y, 15:30; 16:05)
Translation adds another uncontrolled risk. Two prompts can appear equivalent while carrying different levels of politeness, directness, or ambiguity. Have a fluent speaker check any prompt used for a consequential workflow.
Freshness note
The video was published on July 14, 2026. This companion was source-checked against its saved transcript and timestamp segments on July 17, 2026. No outside claim about newer Claude behavior or revised language scores has been added. The video itself says public numbers for later model variants were unavailable, so rerun the comparison on the exact model version you use rather than treating this table as permanent. (source video zXrnCl8oH6Y, 16:05)