DeepSeek R1 vs Mistral: Mixtral 8x22B Instruct benchmark

Interactive Comparison Diagram

Recommended Workload Routing

Frequently Asked Questions

Which is better overall, DeepSeek R1 or Mistral: Mixtral 8x22B Instruct?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, DeepSeek R1 or Mistral: Mixtral 8x22B Instruct?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

DeepSeek R1 output tokens are $2.5/1M versus $6/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek R1 or Mistral: Mixtral 8x22B Instruct?

We do not have TTFT for both models.

Which has the larger context window?

Mistral: Mixtral 8x22B Instruct accepts 66k tokens versus 64k.

Can I self-host either model?

DeepSeek R1 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. DeepSeek R1 and Mistral: Mixtral 8x22B Instruct Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.