Model Performance Benchmarks: Real-World Coding Comparisons

• Jader Correa

Model Performance Benchmarks: Real-World Coding Comparisons

Updated January 2025

Important: AI models evolve rapidly. Benchmark your models using established coding benchmarks like HumanEval1, SWE-Bench2, and LiveCodeBench3.

  1. Testing models with your specific workload
  2. Checking Groq configurations for current recommendations
  3. Exploring OpenRouter guide for latest models
  4. Using chu models search to discover available models

Speed vs Quality Trade-offs

Speed Champions (Groq)

Model Speed (TPS) Use Case
Llama 3.1 8B 840+ Router, fast classification
Qwen3 32B 650 Fast coding with good quality
GPT-OSS 120B 500 Query/research with reasoning
DeepSeek-R1-Qwen-32B 600 Code generation (83.3% AIME)

Groq’s LPU technology delivers unmatched inference speed.

Quality Leaders (OpenRouter)

Based on 2025 benchmarks and real-world testing:

Model Strength Context Cost
Claude 4.5 Sonnet Code review, debugging 200k Premium
Grok 4.1 Fast Agentic tasks, 2M context 2M Free tier
Qwen 2.5 Coder 32B Code generation (88.4% HumanEval) 131k Budget
GPT-OSS 120B Reasoning, comprehension 128k Budget

Current Recommendations (2025)

For Speed + Budget

Groq Backend:

chu profiles create groq speed
chu profiles set-agent groq speed router llama-3.1-8b-instant
chu profiles set-agent groq speed editor llama-3.3-70b-versatile
chu profiles set-agent groq speed query llama-3.3-70b-versatile
chu profiles set-agent groq speed research groq/compound
  • Router: Llama 3.1 8B Instant (840 TPS, ultra-cheap)
  • Editor: Llama 3.3 70B Versatile (strong all-around)
  • Query: Llama 3.3 70B Versatile (good reasoning)
  • Research: Groq Compound (web search + tools)

For Maximum Quality

OpenRouter Backend:

chu profiles create openrouter quality
chu profiles set-agent openrouter quality router google/gemini-2.0-flash-exp:free
chu profiles set-agent openrouter quality editor anthropic/claude-4.5-sonnet
chu profiles set-agent openrouter quality query anthropic/claude-4.5-sonnet
chu profiles set-agent openrouter quality research x-ai/grok-4.1-fast:free
  • Router: Gemini 2.0 Flash (free, fast)
  • Editor: Claude 4.5 Sonnet (premium quality)
  • Query: Claude 4.5 Sonnet (best code understanding)
  • Research: Grok 4.1 Fast (2M context, free tier)

For Zero Cost

OpenRouter Free Models:

chu profiles create openrouter free
chu profiles set-agent openrouter free router google/gemini-2.0-flash-exp:free
chu profiles set-agent openrouter free editor moonshotai/kimi-k2:free
chu profiles set-agent openrouter free query google/gemini-2.0-flash-exp:free
chu profiles set-agent openrouter free research x-ai/grok-4.1-fast:free
  • Router: Gemini 2.0 Flash (fastest TTFT)
  • Editor: Kimi K2 (good coding, free)
  • Query: Gemini 2.0 Flash or Grok 4.1 Fast (2M context)
  • Research: Grok 4.1 Fast (2M context, agentic design)

Note: Free models have rate limits. For consistent availability, consider adding your own API keys or using paid tiers.

Discovering Models

Use Chuchu’s model search to find available models:

# Search by provider
chu models search groq llama

# Search by features
chu models search free coding

# Filter by agent type
chu models search --agent editor openrouter

See our detailed configuration guides for setup instructions and cost breakdowns.

References

  1. Chen, M., Tworek, J., Jun, H., Yuan, Q., et al. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374. https://arxiv.org/abs/2107.03374 ↩

  2. Jimenez, C. E., Yang, J., Wettig, A., et al. (2024). SWE-bench: Can Language Models Resolve Real-World GitHub Issues? ICLR 2024. https://arxiv.org/abs/2310.06770 ↩

  3. Jain, N., Han, K., Gu, A., et al. (2024). LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code. arXiv preprint arXiv:2403.07974. https://arxiv.org/abs/2403.07974 ↩