Model Comparison for Spec-Driven Development
How different LLMs perform in specification-driven workflows
The Problem
Different models have different strengths for specification-driven development. Choosing the wrong model means slower iteration, more failures, and wasted compute.
Model selection matters more than prompt engineering.
A well-prompted weak model underperforms a simply-prompted strong model. For spec-driven workflows, instruction following and reasoning matter more than raw code generation speed.
Current Options
| Option | Pros | Cons |
|---|---|---|
| Claude (Sonnet/Opus)Best for complex specifications. |
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| Gemini 2.5 Pro / 2.0 FlashBest for codebase-wide context. |
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| DeepSeek Coder V3Best for cost-sensitive iteration. |
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| Qwen 2.5 CoderBest open-weight option. |
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Future Outlook
Model specialization will drive tool selection.
Different phases, different models.
Use Claude for architecture and specification review. Use DeepSeek for high-volume implementation iteration. Use Gemini for codebase-wide refactoring. The orchestrator routes to the right model for each task.
Open models are viable for spec-driven workflows.
When tests are the judge, model capability matters less. A weaker model that passes tests is as good as a stronger one. Open models become cost-effective for well-specified tasks.
Our Decision
✓Why we chose this
- Model routingUse Claude for spec review, DeepSeek for iteration, Gemini for large context. Match model to task.
- Cost optimizationFrontier models for design, open models for implementation. Tests validate either.
- Parallel evaluationRun same spec through multiple models. Pick the one that passes tests fastest.
×Trade-offs we accept
- Orchestration complexityMulti-model routing requires infrastructure. Start simple.
- Context switchingDifferent models have different quirks. Consistency suffers.
Motivation
When tests are the judge, any model that passes is good enough. This changes the economics.
Use expensive frontier models for:
- Architecture decisions
- Specification review
- Edge case discovery
Use cheap/fast models for:
- High-volume iteration
- Well-specified implementations
- Parallel exploration
The orchestrator routes tasks to the appropriate model. The tests validate the result.
Recommendation
Task-Based Model Selection:
| Task | Model | Why |
|---|---|---|
| Spec review | Claude Opus | Deep reasoning about edge cases |
| Architecture | Claude Opus | Long-term thinking |
| Implementation | DeepSeek/Sonnet | Fast iteration, good code |
| Large codebase | Gemini 2.5 Pro | 2M context |
| Self-hosted | Qwen 2.5 Coder | No API costs |
Default recommendation:
- Claude Sonnet 4 for most spec-driven work
- Claude Opus 4 for architecture and complex reviews
- DeepSeek Coder V3 for cost-sensitive iteration
Examples
type TaskType = 'spec-review' | 'implementation' | 'architecture';
const modelForTask = (task: TaskType): string => {
switch (task) {
case 'spec-review': return 'claude-opus-4';
case 'architecture': return 'claude-opus-4';
case 'implementation': return 'deepseek-coder-v3';
default: return 'claude-sonnet-4';
}
};Simple task-based routing. Use expensive models for thinking, cheap models for doing.