Table of Contents
Quick Verdict
DeepSeek Coder hits 60.7% on HumanEval pass@1 while CodeLlama manages 49.1%. That 11.6-point gap is not subtle — it means DeepSeek Coder produces a working function nearly 1 in 4 times when CodeLlama would have failed. For code generation on a dedicated GPU server, DeepSeek Coder is the definitive winner on correctness.
It also generates faster: 43 completions per minute at 235 ms average latency versus CodeLlama’s 32 completions at 301 ms. DeepSeek Coder wins on every code-generation metric that matters.
Full data below. More at the GPU comparisons hub.
Specs Comparison
Both models are purpose-built for code. DeepSeek Coder’s training emphasised a broader programming language corpus, which likely explains its HumanEval advantage.
| Specification | CodeLlama | DeepSeek Coder |
|---|---|---|
| Parameters | 34B | 33B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 16K | 16K |
| VRAM (FP16) | 68 GB | 66 GB |
| VRAM (INT4) | 20 GB | 19 GB |
| Licence | Meta Community | MIT |
Guides: CodeLlama VRAM requirements and DeepSeek Coder VRAM requirements.
Code Generation Benchmark
Tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. Tasks included function completions, class stubs, and algorithm implementations. Live data at our tokens-per-second benchmark.
| Model (INT4) | HumanEval pass@1 | Completions/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| CodeLlama | 49.1% | 32 | 301 | 20 GB |
| DeepSeek Coder | 60.7% | 43 | 235 | 19 GB |
DeepSeek Coder’s 34% higher completion rate compounds with its 23% higher accuracy: you finish faster and more outputs are correct. For any pipeline measuring cost per working completion, DeepSeek Coder is unambiguously better. See our best GPU for LLM inference guide.
See also: CodeLlama vs DeepSeek Coder for Chatbot / Conversational AI for a related comparison.
See also: CodeLlama vs DeepSeek Coder for Cost-Optimised Batch Processing for a related comparison.
Cost Analysis
When you factor in correctness, DeepSeek Coder’s cost per working completion is substantially lower. A model that generates broken code cheaper is not cheaper — it just wastes developer time.
| Cost Factor | CodeLlama | DeepSeek Coder |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 20 GB | 19 GB |
| Est. Monthly Server Cost | £144 | £174 |
| Throughput Advantage | 8% faster | 12% cheaper/tok |
Calculate at our cost-per-million-tokens calculator.
Recommendation
Choose DeepSeek Coder for code generation. It dominates on accuracy, throughput, latency, and VRAM efficiency. Its MIT licence also gives maximum commercial flexibility. There is no metric where CodeLlama wins for this workload.
Choose CodeLlama only if you require Meta ecosystem compatibility (shared fine-tunes, LoRA adapters) or if your organisation has policy constraints around model provenance.
Deploy on dedicated GPU servers for reliable code generation at scale.
Deploy the Winner
Run CodeLlama or DeepSeek Coder on bare-metal GPU servers with full root access, no shared resources, and no token limits.
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