Table of Contents
Quick Verdict
Imagine running a nightly batch job that generates docstrings for 10,000 undocumented functions across your codebase. At 200 tok/s versus 190 tok/s and identical $0.15/M token cost, DeepSeek Coder and CodeLlama are nearly neck-and-neck on raw batch economics on a dedicated GPU server.
The tiebreaker is quality: DeepSeek Coder’s superior HumanEval scores from our code generation benchmark suggest its batch outputs will need less human review. For code-oriented batch workloads, DeepSeek Coder earns the slight edge.
Full data below. Visit the GPU comparisons hub for more.
Specs Comparison
These models are remarkably similar in specs. The 1B parameter difference and 1 GB VRAM gap are negligible in practice.
| 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.
Batch Processing Benchmark
Tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and max batch sizes. Tasks included bulk code commenting, test generation, and code review annotation. See our tokens-per-second benchmark.
| Model (INT4) | Batch tok/s | Cost/M Tokens | GPU Utilisation | VRAM Used |
|---|---|---|---|---|
| CodeLlama | 190 | $0.15 | 98% | 20 GB |
| DeepSeek Coder | 200 | $0.15 | 95% | 19 GB |
CodeLlama achieves slightly higher GPU utilisation (98% versus 95%), but DeepSeek Coder’s 5% throughput advantage means it completes identical batch jobs faster. For most practical batch workloads, the difference is small enough that code quality should be the deciding factor. Refer to our best GPU for LLM inference guide.
See also: CodeLlama vs DeepSeek Coder for Chatbot / Conversational AI for a related comparison.
See also: LLaMA 3 70B vs Qwen 72B for Multilingual Chat for a related comparison.
Cost Analysis
At identical per-token pricing, the cost decision comes down to time-to-completion and output quality rather than raw economics.
| 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 | £90 | £129 |
| Throughput Advantage | 13% faster | 10% cheaper/tok |
See our cost-per-million-tokens calculator.
Recommendation
Choose DeepSeek Coder for code-oriented batch processing where output correctness determines the value of each token generated. Its superior code quality means fewer batch outputs require manual correction, reducing the true cost of each processed file.
Choose CodeLlama if your batch workloads include a significant proportion of natural-language tasks alongside code (documentation writing, code review summaries) where its broader conversational training provides an advantage.
Schedule batch jobs during off-peak hours on dedicated GPU servers for maximum utilisation.
Deploy the Winner
Run CodeLlama or DeepSeek Coder on bare-metal GPU servers with full root access, no shared resources, and no token limits.
Browse GPU Servers
