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 37. 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 reasoning, where it averages 58.8 against 36.4. The single biggest benchmark swing on the page is SWE-bench Pro, 22 to 48. DBRX Instruct 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 DBRX Instruct. That is roughly Infinityx on output cost alone. GPT-5 nano is the reasoning model in the pair, while DBRX Instruct 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 32K for DBRX Instruct.
Pick GPT-5 nano if you want the stronger benchmark profile. DBRX Instruct only becomes the better choice if coding is the priority or you want the cheaper token bill.
GPT-5 nano
37.7
DBRX Instruct
34.3
GPT-5 nano
22
DBRX Instruct
48
GPT-5 nano
56.7
DBRX Instruct
35.6
GPT-5 nano
58.8
DBRX Instruct
36.4
GPT-5 nano
63.7
DBRX Instruct
52
Benchmark data for this category is coming soon.
GPT-5 nano
48
DBRX Instruct
46
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 37. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 22 and 48.
GPT-5 nano has the edge for knowledge tasks in this comparison, averaging 63.7 versus 52. Inside this category, FrontierScience is the benchmark that creates the most daylight between them.
DBRX Instruct has the edge for coding in this comparison, averaging 48 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 36.4. 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 34.3. 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 35.6. 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 46. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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