[Llama 3.1 70B Instruct](/models/meta--llama-3.1-70b-instruct) and [Llama 3.3 70B Instruct](/models/meta--llama-3.3-70b-instruct) share the same 70B dense architecture, but Llama 3.3 is a subsequent fine-tune with improved instruction alignment and stronger reasoning. Pricing across providers is nearly identical — both land in the $0.25–$0.90/M input token range — so this is a quality-per-dollar question, not a cost question. Throughput is also equivalent: 60–100 tok/s on A100/H100 hardware for both. The material differences are in benchmark performance. Llama 3.3 70B closes much of the gap to Llama 3.1 405B on instruction-following and coding benchmarks. Meta reported MATH scores improving roughly 5 points over 3.1 70B, and IFEval accuracy ticks up by 3–4%. That's a real improvement baked in at the same serving cost. **Where Llama 3.1 70B wins:** Provider availability. Llama 3.1 70B has been in production longer and is supported by a wider set of providers — including some that haven't yet onboarded 3.3. If your infrastructure is pinned to a specific endpoint and migration is costly, staying on 3.1 is defensible. **Where Llama 3.3 70B wins:** Any new deployment. The improved instruction-following alignment reduces prompt engineering overhead, and the coding improvements are measurable on HumanEval-style tasks. At identical pricing, there's no cost reason to choose 3.1 over 3.3. **Bottom line:** Pick Llama 3.1 70B Instruct only if your provider hasn't yet listed 3.3. For all new deployments, Llama 3.3 70B Instruct delivers meaningfully better quality at the same price.