How to Disable Gemini Reasoning in DSPy

To disable Gemini’s reasoning capabilities in DSPy, you need to properly configure the thinking_budget parameter and ensure the correct configuration is passed to the language model. The most reliable approach is to use thinking_budget=0 when initializing your GeminiFlash language model, which should completely disable the reasoning capabilities.

Contents

• Understanding Gemini’s Reasoning System
• Correct Configuration for DSPy
• Working Code Examples
• Common Issues and Troubleshooting
• Alternative Approaches
• Performance Impact Comparison

Understanding Gemini’s Reasoning System

Gemini 2.5 Flash includes built-in reasoning capabilities that allow the model to perform internal thinking before generating responses. This can be beneficial for complex tasks but introduces additional latency and cost. The reasoning system is controlled through the thinking_budget parameter, which determines how much computational effort the model dedicates to internal reasoning.

According to the official Gemini API documentation, the thinking budget works as follows:

• thinking_budget=0: Disables thinking completely (recommended for disabling reasoning)
• thinking_budget>0: Enables thinking with the specified budget
• thinking_budget=-1: Enables dynamic thinking (model adjusts budget based on complexity)

Important: When thinking is disabled, the model generates responses directly without intermediate reasoning steps, which can significantly reduce latency and cost for simple tasks.

Correct Configuration for DSPy

The correct way to disable reasoning in DSPy with Gemini 2.5 Flash involves using the GeminiFlash class with thinking_budget=0. Here’s the proper configuration:

import dspy
from dotenv import load_dotenv

load_dotenv()

# Initialize GeminiFlash with thinking disabled
gemini_flash = dspy.GeminiFlash(
    model="gemini/gemini-2.5-flash",
    thinking_budget=0,  # This disables reasoning
    cache=False
)

# Configure DSPy to use the language model
dspy.settings.configure(lm=gemini_flash, track_usage=True)

Key Configuration Parameters:

The research shows that DSPy’s implementation specifically uses thinking_budget=0 to disable thinking, making this the most reliable approach for DSPy applications.

Working Code Examples

Here’s a complete, working example that demonstrates how to properly disable reasoning in DSPy:

Example 1: Basic Configuration

import dspy
from dotenv import load_dotenv
from google.genai.types import GenerateContentConfig, ThinkingConfig

load_dotenv()

# Method 1: Using GeminiFlash directly (recommended)
gemini_flash = dspy.GeminiFlash(
    model="gemini/gemini-2.5-flash",
    thinking_budget=0,  # Disables reasoning
    cache=False
)

dspy.settings.configure(lm=gemini_flash, track_usage=True)

# Test with a simple problem
classifier = dspy.Predict(dspy.Signature("problem -> answer: float"))
res = classifier(problem="What's 9 + 10?")

# Verify reasoning is disabled
usage = res.get_lm_usage()
if "gemini/gemini-2.5-flash" in usage:
    completion_details = usage["gemini/gemini-2.5-flash"].get("completion_tokens_details", {})
    reasoning_tokens = completion_details.get("reasoning_tokens", 0)
    assert reasoning_tokens == 0, f"Reasoning tokens found: {reasoning_tokens}"
    print("✓ Reasoning successfully disabled")

Example 2: Alternative Configuration Method

# Method 2: Using GenerateContentConfig (alternative approach)
config = GenerateContentConfig(
    thinking_config=ThinkingConfig(thinking_budget=0)
)

lm = dspy.LM(
    "gemini/gemini-2.5-flash",
    cache=False,
    config=config
)

dspy.settings.configure(lm=lm, track_usage=True)

Common Issues and Troubleshooting

Issue 1: Reasoning Still Active Despite thinking_budget=0

Some users report that even with thinking_budget=0, the model still performs reasoning. According to community discussions, this might be due to:

Solutions:

1. Use the latest DSPy version - Ensure you’re using DSPy that properly supports Gemini 2.5 Flash
2. Check model specification - Make sure you’re using the exact model name gemini/gemini-2.5-flash
3. Verify configuration priority - The thinking_budget parameter in GeminiFlash should take precedence over other configurations

Issue 2: Performance Issues

If you’re experiencing slow performance even with thinking_budget=0:

# Additional optimization options
gemini_flash = dspy.GeminiFlash(
    model="gemini/gemini-2.5-flash",
    thinking_budget=0,
    cache=False,
    temperature=0.0,  # Reduces randomness for faster responses
    max_tokens=1000   # Limit response length
)

Alternative Approaches

Method 1: Dynamic Thinking with Override

For advanced control, you can use dynamic thinking combined with system prompts to override behavior:

gemini_flash = dspy.GeminiFlash(
    model="gemini/gemini-2.5-flash",
    thinking_budget=-1,  # Dynamic thinking
    cache=False
)

# Add system prompt to override thinking
dspy.settings.configure(
    lm=gemini_flash, 
    track_usage=True,
    system_prompt="Do not perform any internal reasoning. Generate direct responses only."
)

Method 2: Model Selection Consideration

If you consistently need disabled reasoning, consider using Gemini 2.0 Flash instead, as it doesn’t have the same reasoning capabilities by default:

gemini_flash_20 = dspy.GeminiFlash(
    model="gemini/gemini-2.0-flash",
    cache=False
)

Performance Impact Comparison

Here’s how different configuration options affect performance:

Performance Benchmark Example:

import time

def benchmark_configuration(config_name, lm):
    start_time = time.time()
    dspy.settings.configure(lm=lm, track_usage=True)
    
    # Simple test
    classifier = dspy.Predict(dspy.Signature("question -> answer"))
    result = classifier(question="What is 2 + 2?")
    
    end_time = time.time()
    return end_time - start_time, result

# Test different configurations
configs = {
    "disabled reasoning": dspy.GeminiFlash("gemini/gemini-2.5-flash", thinking_budget=0),
    "dynamic reasoning": dspy.GeminiFlash("gemini/gemini-2.5-flash", thinking_budget=-1)
}

for name, lm in configs.items():
    latency, _ = benchmark_configuration(name, lm)
    print(f"{name}: {latency:.3f} seconds")

Sources

1. Building and Optimizing AI Applications with DSPy and Gemini Flash 2.5
2. Gemini thinking | Gemini API | Google AI for Developers
3. Thinking | Generative AI on Vertex AI | Google Cloud Documentation
4. How To disable Thinking using Gemini 2.5 Flash? thinkingBudget: 0 not working - Gemini API Forum
5. Gemini-2.5-flash-preview-04-17 not honoring thinking_budget=0 - Gemini API Forum

Conclusion

To disable Gemini’s reasoning capabilities in DSPy, use these key approaches:

1. Primary Method: Initialize dspy.GeminiFlash with thinking_budget=0 - this is the most reliable way to disable reasoning
2. Configuration Priority: The thinking_budget parameter in GeminiFlash takes precedence over other configuration methods
3. Performance Benefits: Disabled reasoning significantly reduces latency and cost for simple tasks
4. Verification: Use get_lm_usage() to confirm that reasoning_tokens equals 0
5. Troubleshooting: If reasoning still appears active, check your DSPy version and ensure you’re using the correct model name

For most use cases requiring disabled reasoning, the thinking_budget=0 approach will provide the best balance of performance and reliability in your DSPy applications with Gemini 2.5 Flash.