Table of Contents
Quick Verdict
Neither CodeLlama nor DeepSeek Coder was designed as a general chatbot — both are code-specialised models. But development teams increasingly want a single model that handles both code generation and natural-language conversation. On a dedicated GPU server, CodeLlama scores 8.7 on multi-turn conversation quality versus DeepSeek Coder’s 7.0, a 1.7-point gap that reflects meaningfully better dialogue coherence outside of pure coding contexts.
DeepSeek Coder generates tokens slightly faster (47 versus 45 tok/s), but that 4% speed advantage is negligible compared to the quality difference for conversational use. When your developers want to chat with the model about architecture decisions or debug strategies, CodeLlama holds up better.
Full results below. See the GPU comparisons hub for more.
Specs Comparison
These models are architectural twins — both dense transformers with 16K context windows and nearly identical parameter counts. The differences are in training data composition, not architecture.
| 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.
Chatbot Performance Benchmark
Tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. Conversations mixed technical questions with explanatory dialogue. See our tokens-per-second benchmark.
| Model (INT4) | TTFT (ms) | Generation tok/s | Multi-turn Score | VRAM Used |
|---|---|---|---|---|
| CodeLlama | 37 | 45 | 8.7 | 20 GB |
| DeepSeek Coder | 36 | 47 | 7.0 | 19 GB |
Both models fit comfortably on a single 24 GB GPU at INT4 with headroom to spare. The TTFT difference is just 1 ms — effectively identical. The multi-turn score is the differentiator. See our best GPU for LLM inference guide.
See also: CodeLlama vs DeepSeek Coder for Code Generation for a related comparison.
See also: Mixtral 8x7B vs Qwen 72B for Document Processing / RAG for a related comparison.
Cost Analysis
With nearly identical VRAM and throughput, the cost story is a wash. Pick based on quality, not price.
| 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 | £116 | £110 |
| Throughput Advantage | 0% faster | 9% cheaper/tok |
See our cost-per-million-tokens calculator.
Recommendation
Choose CodeLlama if your team needs a single model that handles both code and conversation. Its 1.7-point multi-turn quality lead makes it substantially better at explaining code, discussing architecture, and maintaining coherent technical dialogue.
Choose DeepSeek Coder if your chatbot is purely code-focused and conversational quality is secondary. Its MIT licence also offers more commercial flexibility than Meta’s community licence.
Both fit on a single GPU on dedicated GPU hosting with room for co-located services.
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
