Deploy AI Code Assistants with Optimal Performance and Cost
Compare code-specialized LLMs for building AI coding assistants, autocomplete tools, and code review systems. Find the right model and GPU configuration for your development workflow.
Key Considerations
- ‣Code models benefit from long context windows for repository-level understanding.
- ‣FIM (fill-in-the-middle) capability is essential for autocomplete use cases.
- ‣For IDE integration, prioritize latency over quality — users expect instant suggestions.
- ‣Consider DeepSeek Coder or Qwen 2.5 Coder for specialized code tasks.
Recommended Models
| Model | Parameters | Context | VRAM (BF16) | Cheapest $/M Out | Est. Monthly Cost |
|---|---|---|---|---|---|
| Code Llama 70B Meta | 70B | 16K | 140 GB | $0.90 | $45via together |
| Llama 2 70B Meta | 70B | 4K | 140 GB | $0.90 | $45via together |
| Claude Sonnet 4 Anthropic | 70B | 200K | 140 GB | $15.00 | $570via anthropic |
| o1-mini OpenAI | 70B | 128K | 140 GB | $12.00 | $465via openai |
| o3-mini OpenAI | 70B | 200K | 140 GB | $4.40 | $171via openai |
| Claude 3 Sonnet Anthropic | 70B | 200K | 140 GB | $15.00 | $570via anthropic |
| Reka Core Reka AI | 70B | 128K | 140 GB | $15.00 | $570via reka |
| Mistral Medium 3 Mistral AI | 70B | 131K | 140 GB | $6.00 | $240via mistral |
| Jamba 1.5 Large AI21 | 52B | 256K | 796 GB | $8.00 | $310via ai21 |
| Llama 3.1 Nemotron 51B NVIDIA | 51B | 131K | 102 GB | $0.40 | $20via nvidia-nim |
| Amazon Nova Pro Amazon | 50B | 300K | 100 GB | $3.20 | $124via amazon |
| GPT-4o OpenAI | 50BMoE | 128K | 400 GB | $10.00 | $388via openai |
* Monthly cost estimated at 50M tokens/month (30% input, 70% output split) using cheapest available provider.
Recommended GPUs
Cost Estimation
Low Volume
$3/mo
5M tokens via API
Medium Volume
$25/mo
50M tokens via API
High Volume
$125/mo
250M tokens via API
Estimates based on average output token pricing across providers. Use the calculator for precise estimates →
Frequently Asked Questions
What is the best open-source model for code generation?
DeepSeek V3, Qwen 2.5 Coder 32B, and Llama 3.3 70B are top choices. DeepSeek V3 leads on most code benchmarks. For smaller deployments, Qwen 2.5 Coder 32B offers excellent code quality at lower resource requirements.
How much GPU memory do code models need?
A 7B code model needs about 14 GB (BF16) or 4 GB (INT4). A 34B model needs about 68 GB (BF16) or 18 GB (INT4). For 70B models, plan for 140 GB (BF16) requiring multi-GPU setups, or 36 GB (INT4) on a single GPU.
What latency is acceptable for code autocomplete?
For inline autocomplete, aim for under 200ms time-to-first-token. For chat-based code generation, 500ms-1s is acceptable. Use smaller models (7B-14B) for autocomplete and larger models for complex code generation tasks.
Can I fine-tune a code model on my codebase?
Yes. LoRA or QLoRA fine-tuning on your private codebase can significantly improve code suggestion quality. A 7B model can be fine-tuned on a single A100 in a few hours. Use the InferenceBench training calculator to estimate costs.