Comparison
Llama 3.1 8B Instruct vs Qwen2.5 7B Instruct
Side-by-side specs, benchmarks and hosted-inference pricing.
The workhorse 8B instruction-tuned model. Excellent quality-to-cost ratio and the broadest ecosystem support of any open-weights model — every major inference engine, fine-tuning library, and quantization toolchain has a 3.1 8B preset. Fits in 24 GB of VRAM at fp16, ~6 GB at Q4. Strong default for production chat where 70B is overkill, for fine-tuning on a specialist task, and for any workload where you want a known-good baseline.
Apache-2.0-licensed 7B model with surprisingly strong reasoning and multilingual chops. Qwen 2.5 trains on a larger and more carefully filtered corpus than the original Qwen series, and the 7B variant punches well above its weight on coding and math benchmarks. A strong default for cost-sensitive chat workloads and for fine-tuning experiments where the Apache licence simplifies downstream redistribution.
Specs
| Parameters | 8B | 7B |
| Context length | 128K | 128K |
| Modality | text | text |
| Released | 2024-07-23 | 2024-09-18 |
| License | Llama 3 Community License | Apache 2.0 |
| Commercial use | Yes | Yes |
| VRAM fp16 | 16 GB | 14 GB |
| VRAM Q4 | 4.8 GB | 4.2 GB |
Benchmarks
| HumanEval | 72.6 | 84.8 |
| MATH | 51.9 | 75.5 |
| MMLU | 69.4 | 74.2 |