Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mercury 2's sharpest advantage is in reasoning, where it averages 80.1 against 38.6. The single biggest benchmark swing on the page is LongBench v2, 77 to 39.
Mercury 2 is also the more expensive model on tokens at $0.25 input / $0.75 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. Mercury 2 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. Mercury 2 gives you the larger context window at 128K, compared with 64K for Mixtral 8x22B Instruct v0.1.
Pick Mercury 2 if you want the stronger benchmark profile. Mixtral 8x22B Instruct v0.1 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Mercury 2
63.7
Mixtral 8x22B Instruct v0.1
31.8
Mercury 2
41.1
Mixtral 8x22B Instruct v0.1
40
Mercury 2
68.3
Mixtral 8x22B Instruct v0.1
35.5
Mercury 2
80.1
Mixtral 8x22B Instruct v0.1
38.6
Mercury 2
57.2
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.
Mercury 2
79.7
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.
Mercury 2 is ahead overall, 65 to 35. The biggest single separator in this matchup is LongBench v2, where the scores are 77 and 39.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 53. Inside this category, FrontierScience is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for coding in this comparison, averaging 41.1 versus 40. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for reasoning in this comparison, averaging 80.1 versus 38.6. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for agentic tasks in this comparison, averaging 63.7 versus 31.8. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for multimodal and grounded tasks in this comparison, averaging 68.3 versus 35.5. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for multilingual tasks in this comparison, averaging 79.7 versus 42. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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