"Temperature" is supposed to be the simplest knob in the LLM API: 0 is deterministic, 1 is normal creativity, 2 is full random. In practice, different providers implement it differently, cap it differently, and respond to edge values with silently different behavior.

If your prompt routing targets multiple providers and you're setting the same temperature for all of them, you're shipping inconsistent outputs. Here's the map.

Empirical Test: Same Prompt, Same Provider, Different Temperatures

We ran "Write a 2-sentence product description for a new noise-cancelling headphone designed for airline travel" at four temperature values on GPT-4o-mini:

Interesting takeaway: on GPT-4o-mini, temperature 0 to 0.7 produced nearly identical outputs. The "creativity unlocked by raising temperature" pitch is less dramatic than commonly assumed below 1.0.

Running the same prompt at temperature 1.5 on Claude Haiku 4.5:

QuietFly Pro Headphones

Experience serene air travel with our advanced noise-cancelling headphones, engineered to eliminate engine roar and cabin noise so you can sleep, work, or relax at 35,000 feet...

The output is cleaner and more structured than GPT-4o-mini's at the same temperature. Why? Because Claude silently clamped temperature to 1.0.

The Per-Provider Map

From our empirical testing and provider documentation:

The two providers that cap at 1.0 — Anthropic and Together AI — don't throw errors. They accept your value and silently clamp. Your routing layer needs to know this or your "creative mode at temp 1.5" is identical to "normal mode at temp 1.0" on those providers.

The Promptster backend clamps to 1.0 for these providers explicitly with Math.min(config.temperature, 1.0) and surfaces a UI note via ResultsDisplay.temperatureNote — see our provider handlers for the source.

Reasoning Models Have No Temperature

OpenAI o-series models (o3, o3-mini, o4-mini) do not accept a temperature parameter. The API rejects requests that pass one. Instead, they use reasoning_effort: 'low' | 'medium' | 'high'.

If your router passes temperature: 0.7 to o4-mini, the call fails with a 400. The fix is to detect reasoning-model IDs (pattern match ^o[0-9]) and omit the temperature param. Promptster's backend does this automatically.

What Temperature Actually Does (Empirically)

Three patterns we've observed across the test matrix:

1. Below 0.5 — deterministic-ish. Outputs are nearly identical across runs. Use for: extraction, classification, structured data, code. Anywhere you need reproducibility.

2. 0.5 to 1.0 — mild variation. Outputs vary in word choice but not structure or stance. Use for: drafts, summaries, UX copy. Normal creative work.

3. 1.0 to 1.5 — real variation. Outputs diverge on structure, emphasis, sometimes content. Use for: brainstorming, A/B variants, multiple-attempt generation.

4. Above 1.5 — chaotic. Outputs degrade in coherence. Occasional gibberish. Mostly useful for generating diverse candidates that you then filter — not for direct use.

The Practical Recipe

For production prompts, we recommend:

• Factual / structured tasks: temperature 0.1 (not 0 — reduces edge cases with some providers while preserving reproducibility).
• Balanced copy: temperature 0.5-0.7.
• Creative variants: temperature 1.0 (and know that Anthropic clamps here).
• Never pass temperature > 1.5 for production: it's noise below the useful signal.

The Cross-Provider Gotcha

If your app routes the same prompt across multiple providers, normalize temperature at the routing layer. Don't let the per-provider API differences leak to your application code.

def normalize_temperature(provider: str, requested_temp: float) -> float:
    """Clamp temperature to each provider's accepted range."""
    if provider in ("anthropic", "together"):
        return min(requested_temp, 1.0)
    if provider.startswith("openai/o"):  # reasoning models
        return None  # omit parameter entirely
    return min(requested_temp, 2.0)

Test your routing with each provider you support. Log the effective temperature used per call. Make the silent clamp explicit.

The Summary

• Temperature 0-0.7 on most providers is visually indistinguishable. Don't expect dramatic diversity in this range.
• Anthropic and Together AI silently clamp temperature to 1.0.
• OpenAI o-series reasoning models reject temperature entirely — use reasoning_effort instead.
• Normalize temperature in your routing layer or ship inconsistent behavior.

For more on cross-provider portability issues, see why your prompts fail on different LLM providers. For the full decision framework, see the task-type decision framework.

Tests run 2026-04-19 on GPT-4o-mini and Claude Haiku 4.5. Temperature behavior may vary with model version; re-test if you're deploying to production.