OSAIM
Open Source AI Models

Comparison

Llama 3.1 8B Instruct vs Qwen2.5 7B Instruct

Side-by-side specs, benchmarks and hosted-inference pricing.

Side A
Llama 3.1 8B Instruct
Meta · Llama

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.

Side B
Qwen2.5 7B Instruct
Alibaba · Qwen

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

Parameters8B7B
Context length128K128K
Modalitytexttext
Released2024-07-232024-09-18
LicenseLlama 3 Community LicenseApache 2.0
Commercial useYesYes
VRAM fp1616 GB14 GB
VRAM Q44.8 GB4.2 GB

Benchmarks

HumanEval72.684.8
MATH51.975.5
MMLU69.474.2

Cheapest hosted pricing

Llama 3.1 8B Instruct
groq: $0.05 in / $0.08 out per 1M tokens
Qwen2.5 7B Instruct
deepinfra: $0.08 in / $0.30 out per 1M tokens
Highlighted cells indicate the better value for that row (higher score, larger context, lower VRAM).