[Llama 3.1 8B Instruct](/models/meta--llama-3.1-8b-instruct) and [Mistral 7B Instruct v0.3](/models/mistralai--mistral-7b-instruct-v0.3) are both sub-10B models targeting cost-sensitive, high-throughput deployments. Pricing is in the same ballpark — $0.05–$0.20/M input tokens — making both among the cheapest hosted options available. Throughput is where they diverge: Mistral 7B typically achieves 120–200 tok/s on A10G or A100 hardware; Llama 3.1 8B runs slightly slower at 100–170 tok/s due to its larger vocabulary and attention implementation, though the difference is rarely decisive at application level. Mistral 7B v0.3 uses sliding window attention (SWA) with a 32K context window and ships native function-calling support. Llama 3.1 8B also supports tool use and offers a 128K context window — a major structural advantage for long-document workloads. **Where Llama 3.1 8B wins:** Long-context tasks — document Q&A, summarization over transcripts, multi-turn conversations with deep history. The 128K window is 4× larger than Mistral 7B v0.3's and enables use cases that simply don't fit the smaller context. Benchmark quality on MMLU and instruction following is also higher. **Where Mistral 7B v0.3 wins:** Ultra-low-latency, token-constrained APIs. Its lighter architecture and widespread provider support (including many edge and on-device deployments) make it the practical pick when you're optimizing for p95 latency under 500ms at high concurrency. **Bottom line:** Pick Llama 3.1 8B Instruct for general-purpose production workloads and anything requiring long context. Pick Mistral 7B Instruct v0.3 when you're optimizing for the lowest latency ceiling at the lowest cost tier.