Model Temperature

Temperature controls the randomness of the AI model’s output. Adjusting this setting optimizes results for different tasks—from precise code generation to creative brainstorming. Temperature is one of the most powerful parameters for shaping AI behavior. A carefully tuned temperature setting can significantly improve the quality and relevance of responses for specific tasks.

Animation showing adjustment of the temperature slider

What Is Temperature?

Temperature is a setting (typically ranging from 0.0 to 2.0) that controls the randomness or predictability of AI output. Finding the right balance is key: lower values make output more focused and consistent, while higher values encourage greater creativity and diversity. For many coding tasks, a moderate temperature (around 0.3 to 0.7) often works well, but the optimal setting depends on your specific goal.

Temperature and Code: Common Misconceptions

Temperature controls output randomness—not code quality or accuracy directly. Key points:

• Low temperature (near 0.0): Produces predictable, consistent code. Suitable for simple tasks but may become repetitive and lack creativity. It does not guarantee better code.
• High temperature: Increases randomness, which may lead to creative solutions but also more errors or nonsensical output. It does not guarantee higher-quality code.
• Accuracy: Code correctness depends on the model’s training and prompt clarity—not temperature.
• Temperature 0.0: Promotes consistency but limits the exploration needed for complex problems.

Default Values in VJSP

VJSP uses a default temperature of 0.0 for most models to optimize for maximum determinism and precision in code generation. This applies to OpenAI models, Anthropic models (non-thinking variants), LM Studio models, and most other providers.

Certain models use a higher default temperature—DeepSeek R1 models and some reasoning-focused models default to 0.6, striking a balance between determinism and creative exploration.

Models with "thinking" capabilities (where the AI shows its reasoning process) require a fixed temperature of 1.0, which cannot be changed, as this setting ensures optimal performance of the reasoning mechanism. This applies to any model with the :thinking flag enabled.

Some specialized models do not support temperature adjustment at all; in such cases, VJSP automatically respects these limitations.

When to Adjust Temperature

Here are example temperature ranges suited for different tasks:

• Code Mode (0.0–0.3): For writing precise, correct code with consistent, deterministic results
• Architect Mode (0.4–0.7): For brainstorming architectures or design solutions with balanced creativity and structure
• Ask Mode (0.7–1.0): For diverse, insightful explanations or open-ended questions
• Debug Mode (0.0–0.3): For troubleshooting with consistent precision

These are starting points—experimentation is essential to find what best fits your specific needs and preferences.

How to Adjust Temperature

1. Open the VJSP Panel: Click the VJSP icon in the VS Code sidebar
2. Open Settings: Click the ⚙️ icon in the top-right corner
3. Find Temperature Control: Navigate to the “Provider” section
4. Enable Custom Temperature: Check the “Use Custom Temperature” box
5. Set Your Value: Adjust the slider to your preferred value

*Temperature slider in the VJSP settings panel*

Setting Temperature via API Profiles

Create multiple API profiles with different temperature settings:

How to set up task-specific temperature profiles:

1. Create dedicated profiles such as “Code – Low Temp” (0.1) and “Ask – High Temp” (0.8)
2. Configure the appropriate temperature for each profile
3. Switch between profiles using the dropdown in settings or the chat interface
4. Assign different profiles as defaults for each mode so they switch automatically when you change modes

This approach optimizes model behavior for specific tasks without requiring manual adjustments.

Technical Implementation

VJSP handles temperature with the following considerations:

• User-defined settings override defaults
• Provider-specific behaviors are respected
• Model-specific constraints are enforced:
  • Thinking-enabled models require a fixed temperature of 1.0
  • Some models do not support temperature adjustment at all

Experimentation

Trying different temperature settings is the most effective way to discover what works best for your needs:

Effective Temperature Testing

1. Start with defaults – Use VJSP’s preset (usually 0.0 for most tasks) as your baseline
2. Make incremental adjustments – Change values in small steps (±0.1) to observe subtle differences
3. Test consistently – Use the same prompt across different temperature settings for valid comparison
4. Document results – Note which values yield the best outcomes for specific task types
5. Create profiles – Save effective configurations as API profiles for quick access

Remember: different models may respond differently to the same temperature value, and thinking-enabled models always use a fixed temperature of 1.0 regardless of your settings.