Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 nano is clearly ahead on the aggregate, 49 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1 nano's sharpest advantage is in reasoning, where it averages 74.1 against 38.6. The single biggest benchmark swing on the page is LongBench v2, 75 to 39. Mixtral 8x22B Instruct v0.1 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 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. GPT-4.1 nano gives you the larger context window at 1M, compared with 64K for Mixtral 8x22B Instruct v0.1.
Pick GPT-4.1 nano if you want the stronger benchmark profile. Mixtral 8x22B Instruct v0.1 only becomes the better choice if coding is the priority or you want the cheaper token bill.
GPT-4.1 nano
47.4
Mixtral 8x22B Instruct v0.1
31.8
GPT-4.1 nano
18
Mixtral 8x22B Instruct v0.1
40
GPT-4.1 nano
59.3
Mixtral 8x22B Instruct v0.1
35.5
GPT-4.1 nano
74.1
Mixtral 8x22B Instruct v0.1
38.6
GPT-4.1 nano
50.7
Mixtral 8x22B Instruct v0.1
53
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GPT-4.1 nano
59
Mixtral 8x22B Instruct v0.1
42
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GPT-4.1 nano is ahead overall, 49 to 35. The biggest single separator in this matchup is LongBench v2, where the scores are 75 and 39.
Mixtral 8x22B Instruct v0.1 has the edge for knowledge tasks in this comparison, averaging 53 versus 50.7. 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 40 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for reasoning in this comparison, averaging 74.1 versus 38.6. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for agentic tasks in this comparison, averaging 47.4 versus 31.8. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for multimodal and grounded tasks in this comparison, averaging 59.3 versus 35.5. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for multilingual tasks in this comparison, averaging 59 versus 42. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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