A 56.4% HumanEval pass@1 versus 48.1%. On paper, LLaMA 3 8B looks like the obvious winner for code generation. But raw accuracy scores hide an important trade-off: DeepSeek 7B pushes 41 completions per minute against LLaMA’s 28. If you are building an autocomplete backend that serves a whole engineering team, throughput might matter more than any single benchmark number.
Accuracy vs Speed: The Core Trade-Off
We benchmarked both models on an RTX 3090 running vLLM with INT4 quantisation and continuous batching. The prompt set covered Python function completion, TypeScript interface generation, and SQL query writing. See live speed data for current numbers.
| Model (INT4) | HumanEval pass@1 | Completions/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 56.4% | 28 | 203 | 6.5 GB |
| DeepSeek 7B | 48.1% | 41 | 334 | 5.8 GB |
LLaMA posts an 8-point lead on HumanEval and returns each completion in 203 ms on average. DeepSeek takes longer per request at 334 ms but compensates with higher throughput when batching is factored in. The reason is architectural: DeepSeek’s 32K context window means it processes larger code blocks in a single pass without chunking, which amortises the per-request overhead when you are processing many requests simultaneously.
Under the Hood
| Specification | LLaMA 3 8B | DeepSeek 7B |
|---|---|---|
| Parameters | 8B | 7B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 8K | 32K |
| VRAM (FP16) | 16 GB | 14 GB |
| VRAM (INT4) | 6.5 GB | 5.8 GB |
| Licence | Meta Community | MIT |
DeepSeek’s MIT licence gives it an edge in commercial deployments where legal teams get nervous about Meta’s community licence restrictions. If you are embedding code generation into a SaaS product, that distinction is worth considering. See our LLaMA 3 VRAM guide and DeepSeek VRAM guide for deployment sizing.
What It Costs to Run
| Cost Factor | LLaMA 3 8B | DeepSeek 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 5.8 GB |
| Est. Monthly Server Cost | £88 | £156 |
| Throughput Advantage | 6% faster | 11% cheaper/tok |
Both models fit comfortably on a single RTX 3090 at INT4. The per-token economics favour DeepSeek by 11% thanks to its higher batch throughput, though the monthly server cost varies depending on your provider and configuration. Run the numbers for your expected volume with the cost-per-million-tokens calculator.
Which One to Pick
Go with LLaMA 3 8B if you are building an IDE plugin or pair-programming assistant where each suggestion needs to be correct on the first attempt. The 8-point accuracy advantage translates into fewer broken suggestions cluttering a developer’s flow. For background on hardware choices, see best GPU for LLM inference.
Go with DeepSeek 7B if you are running a batch code review service or generating test suites at scale. The higher throughput means your CI pipeline spends less time waiting, and the MIT licence keeps legal simple. Check our full comparison index for related matchups.
See also: LLaMA 3 vs DeepSeek for Chatbots | LLaMA 3 vs Mistral for Code Generation
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