Mixtral 8x22B v0.1 vs Phi-3 Medium 128K

On shared benchmark coverage, HumanEval has Mixtral 8x22B v0.1 at 86.2 and Phi-3 Medium 128K at 52.2, with Mixtral 8x22B v0.1 ahead by 34 points; Massive Multitask Language Understanding has Mixtral 8x22B v0.1 at 84.5 and Phi-3 Medium 128K at 75.3, with Mixtral 8x22B v0.1 ahead by 9.2 points. The largest visible gap is 34 points on HumanEval, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.

For cost, Mixtral 8x22B v0.1 lists $0.3/1M input and $0.9/1M output tokens, while Phi-3 Medium 128K lists $0.5/1M input and $1.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x22B v0.1 lower by about $0.32 per million blended tokens. Availability is 8 providers versus 2, so concentration risk also matters.

Choose Mixtral 8x22B v0.1 when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Phi-3 Medium 128K when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.