Compare

Code Llama 7B Instruct vs Llama 3.1 8B Instruct

Same hardware, same arithmetic, side by side. Every memory figure is calculated from published model geometry rather than quoted from a marketing page.

Estimated

Model AModel B

Hardware

Side by side

On GeForce RTX 4090

Property	Code Llama 7B Instruct	Llama 3.1 8B Instruct
Organization	Meta	Meta
Parameters	6.7B	8B
Architecture	Llama · dense	Llama · dense
Context	16k	128k
Layers	32	32
Hidden size	4,096	4,096
KV heads	32 of 32	8 of 32
Licence	Llama 2 Community License	Llama 3.1 Community License
Commercial use	Yes	Yes
Modalities	text	text
Released	2023-08-24	2024-07-23
Recommended quantization	Q8_0	Q8_0
Memory needed	11 GB	9.5 GB
Estimated tok/s	109	92
Fit	Excellent fit	Excellent fit
Fine-tune here	Yes	Yes
MMLU (reported)	—	69.4%
HUMANEVAL (reported)	—	72.6%

Benchmark rows are figures the model's authors published, not ModelLM measurements, and the two models may not have been evaluated under identical conditions.

Trade-offs

Code Llama 7B Instruct

Now superseded by Qwen2.5-Coder, but still the reference point for a lot of editor integrations.

Strengths

Ubiquitous editor integration
Fill-in-the-middle

Limitations

Clearly behind modern code models
Full multi-head attention: heavy KV cache

Llama 3.1 8B Instruct

The most widely supported open model there is. If a tool, adapter or tutorial exists, it was written for this one first.

Strengths

Unmatched ecosystem support
128k context
Very stable fine-tuning behaviour

Limitations

Benchmarks now behind newer 7–9B models
Community licence with an acceptable-use policy

Common comparisons

Qwen2.5 14B Instruct vs Llama 3.1 8B Instruct Qwen2.5 Coder 32B Instruct vs DeepSeek-R1-Distill-Qwen-32B Gemma 3 12B Instruct vs Mistral Nemo 12B Instruct Phi-4 14B vs Qwen3 14B

Property

Code Llama 7B Instruct

Llama 3.1 8B Instruct

Organization