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Best Open Source LLM for Code Generation

Deploybase · February 18, 2026 · LLM Guides

Contents

Best Open Source LLM for Coding: Code generation: what benchmark matters

Best Open Source LLM for Coding is the focus of this guide. HumanEval benchmark: LLM writes Python functions from docstrings. 164 problems. Graded on functional correctness.

As of March 2026:

  • GPT-4.1: 92% pass rate
  • Llama 3.1 70B: 88% pass rate
  • DeepSeek-Coder 33B: 85% pass rate
  • GPT-4o: 86% pass rate
  • CodeLlama 34B: 77% pass rate
  • Phi 4: 80% pass rate

Llama 3.1 70B is best open-source option overall. Fast, accurate, widely available.

HumanEval is synthetic. Real code generation requires context, multiple files, imports. Real-world performance varies.

Best models by size class

Tiny (under 10B parameters):

Phi 4 (14B): 80% HumanEval. Runs on CPU. Quantized to 4-bit fits 8GB memory. Best for local development.

Small (10-15B):

StarCoder 2 15B: 72% HumanEval. Fast inference. Good for edge deployment.

Medium (30-40B):

DeepSeek-Coder 33B: 85% HumanEval. Multi-language support. Fast inference on single GPU. Price/performance leader.

Large (70B+):

Llama 3.1 70B: 88% HumanEval. Best accuracy. Standard choice. Available via API.

Speed and cost: inference comparison

Running locally on single RTX 4090:

ModelSizeTime/tokenCost (if rented)
Phi 412B5ms$0.34/hr (RunPod)
StarCoder 2 15B15B8ms$0.34/hr
DeepSeek-Coder 33B33B18ms$0.34/hr
Llama 3.1 70B70B45ms$0.79/hr (RunPod L40S)

Phi 4 fastest, lowest latency. Good for IDE autocomplete.

Llama 70B slower but best quality. Good for code review, architecture decisions.

DeepSeek 33B sweet spot: 33ms per token, 33B parameters. Cost-effective.

Local vs API: when to use each

Use local (RunPod/Lambda):

  • Generating code for proprietary systems (security)
  • High volume (100+ generations daily)
  • Fine-tuning on private codebase
  • Code review automation
  • Batch code generation

Use API:

  • Development tooling (IDE integration)
  • Single queries
  • No private data
  • Low volume

API pricing comparison for code generation:

Assume 2,000 input tokens (code context), 300 output tokens (generated code).

DeepSeek cheapest for code generation. Llama 3.1 on Anyscale similar: $0.04 per generation.

Local generation on RunPod RTX 4090 at $0.34/hr: ~30 seconds per generation = $0.003 cost (if running continuously). Efficient at scale.

Fine-tuning for the codebase

Fine-tune Llama 3.1 7B or 13B on the codebase.

Prepare training data:

  • Recent commits (functions + tests)
  • PR diffs (before/after)
  • Documentation + code examples
  • Capture the style, patterns, naming conventions

Cost:

  • 10K code examples: $200-500 on RunPod 7B training
  • One-time setup
  • Inference on fine-tuned model: same speed as base

Result: model generates code in the style. Understands the architecture. Generates names matching conventions. Cuts code review time.

Real-world performance: beyond HumanEval

HumanEval tests isolated functions. Real code needs:

  • Multi-file context
  • Dependency understanding
  • Error handling
  • Comments and documentation

Llama 3.1 70B handles multi-file better than smaller models. More context. Better reasoning.

DeepSeek-Coder trained on more code diversity. Good at uncommon languages (Go, Rust, Kotlin).

Phi 4 struggles with complex scenarios. Excellent for simple patterns.

For production code generation, pick Llama 3.1 70B or DeepSeek-Coder 33B.

FAQ

Q: Should I use GitHub Copilot or open-source models? Copilot uses GPT-4, better quality, lower latency. Open-source models cheaper, private. For commercial products, start with Copilot. For internal tools, open-source saves cost.

Q: Can Phi 4 run on MacBook? Yes. 4-bit quantized version fits M1 8GB memory. Inference at 2-3 tokens/second. Acceptable for autocomplete. Not for batch generation.

Q: Does fine-tuning actually improve code generation? Yes, measurably. Domain-specific code generation improves 20-40%. Standard benchmarks like HumanEval improve less. The improvement is in your codebase accuracy, not generic code.

Q: Which model is best for Rust code? DeepSeek-Coder trained on more Rust. Llama 3.1 70B also good. Both 80%+ accuracy on Rust HumanEval-equivalent. Phi 4 struggles with Rust (lower accuracy).

Q: Can I generate tests alongside code? Yes. Include test examples in fine-tuning data. Prompt model to generate both implementation and tests. Works well at 33B+ scale. Smaller models less reliable.

Q: How does quantization affect code generation quality? Minimal impact. 8-bit quantization: no measurable quality loss. 4-bit quantization: very small accuracy drop (<2%). 2-bit: notable drop (5-10%). Use 4-bit for local inference.

Sources