Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
DeepSeek-R1 is clearly ahead on the aggregate, 43 to 28. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Mixtral 8x22B Instruct v0.1. That is roughly Infinityx on output cost alone. DeepSeek-R1 is the reasoning model in the pair, while Mixtral 8x22B Instruct v0.1 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. DeepSeek-R1 gives you the larger context window at 128K, compared with 64K for Mixtral 8x22B Instruct v0.1.
Pick DeepSeek-R1 if you want the stronger benchmark profile. Mixtral 8x22B Instruct v0.1 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
DeepSeek-R1
38.8
Mixtral 8x22B Instruct v0.1
71.4
DeepSeek-R1
24
Mixtral 8x22B Instruct v0.1
54.8
DeepSeek-R1 is ahead overall, 43 to 28. The biggest single separator in this matchup is MMLU, where the scores are 44 and 71.4.
Mixtral 8x22B Instruct v0.1 has the edge for knowledge tasks in this comparison, averaging 71.4 versus 38.8. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Mixtral 8x22B Instruct v0.1 has the edge for coding in this comparison, averaging 54.8 versus 24. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
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