Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 nano is clearly ahead on the aggregate, 45 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5 nano's sharpest advantage is in multimodal & grounded, where it averages 56.7 against 35.5. The single biggest benchmark swing on the page is MMMU-Pro, 58 to 35. 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-5 nano is also the more expensive model on tokens at $0.05 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-5 nano 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. GPT-5 nano gives you the larger context window at 400K, compared with 64K for Mixtral 8x22B Instruct v0.1.
Pick GPT-5 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-5 nano
37.7
Mixtral 8x22B Instruct v0.1
31.8
GPT-5 nano
22
Mixtral 8x22B Instruct v0.1
40
GPT-5 nano
56.7
Mixtral 8x22B Instruct v0.1
35.5
GPT-5 nano
58.8
Mixtral 8x22B Instruct v0.1
38.6
GPT-5 nano
63.7
Mixtral 8x22B Instruct v0.1
53
Benchmark data for this category is coming soon.
GPT-5 nano
48
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-5 nano is ahead overall, 45 to 35. The biggest single separator in this matchup is MMMU-Pro, where the scores are 58 and 35.
GPT-5 nano has the edge for knowledge tasks in this comparison, averaging 63.7 versus 53. Inside this category, FrontierScience 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 22. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for reasoning in this comparison, averaging 58.8 versus 38.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for agentic tasks in this comparison, averaging 37.7 versus 31.8. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for multimodal and grounded tasks in this comparison, averaging 56.7 versus 35.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for multilingual tasks in this comparison, averaging 48 versus 42. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.