If you're like me, you use AI for coding every single day. But have you actually benchmarked whether you're using the right model? I ran 100 coding tasks through 8 different models — here's what I learned.
Test Setup
I used the same 100 coding prompts across all models: 30 simple functions, 30 algorithm problems, 20 API integrations, 10 full-stack features, and 10 debugging challenges. Each model generated code with identical prompts, and I evaluated based on correctness, code quality, explanation quality, and speed.
Raw Scores
| Rank | Model | Output $/M | Correctness | Quality | Speed | Overall |
|---|---|---|---|---|---|---|
| 1 | DeepSeek V4 Pro | $0.75 | 96% | 9.2 | 8.5 | 9.1 |
| 2 | DeepSeek V4 Flash | $0.25 | 94% | 8.8 | 9.0 | 8.9 |
| 3 | Kimi K2.5 | $3.00 | 95% | 9.3 | 7.0 | 8.7 |
| 4 | Qwen3-Coder-30B | $0.35 | 91% | 8.5 | 8.8 | 8.5 |
| 5 | DeepSeek V3.2 | $0.38 | 93% | 8.7 | 7.5 | 8.5 |
Key Insight: More Expensive != Better for Coding
DeepSeek V4 Flash at $0.25/M scored 8.9/10 overall. Kimi K2.5 at $3.00/M (12x more expensive) scored 8.7/10. You're paying 12x more for 0.2 LESS score. The sweet spot for coding is clearly V4 Flash — 94% correctness, excellent code quality, and the fastest generation speed.
My daily coding workflow now uses this setup:
client = OpenAI(api_key="ga_...", base_url="https://global-apis.com/v1")
def get_coding_model(task_complexity):
if task_complexity == "simple":
return "deepseek-ai/DeepSeek-V4-Flash" # V4 Flash: fast, cheap, accurate
elif task_complexity == "hard":
return "deepseek-reasoner" # V4 Pro: slower but smarter
elif task_complexity == "refactor":
return "Qwen/Qwen3-Coder-30B" # Specialized for code
return "deepseek-ai/DeepSeek-V4-Flash"