Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3-Codex-Spark is clearly ahead on the aggregate, 87 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3-Codex-Spark's sharpest advantage is in reasoning, where it averages 92.7 against 38.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 90 to 35.
GPT-5.3-Codex-Spark is also the more expensive model on tokens at $2.00 input / $8.00 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-5.3-Codex-Spark 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-5.3-Codex-Spark gives you the larger context window at 256K, compared with 64K for Mixtral 8x22B Instruct v0.1.
Pick GPT-5.3-Codex-Spark 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.
GPT-5.3-Codex-Spark
85.6
Mixtral 8x22B Instruct v0.1
31.8
GPT-5.3-Codex-Spark
82.3
Mixtral 8x22B Instruct v0.1
40
GPT-5.3-Codex-Spark
88.3
Mixtral 8x22B Instruct v0.1
35.5
GPT-5.3-Codex-Spark
92.7
Mixtral 8x22B Instruct v0.1
38.6
GPT-5.3-Codex-Spark
78.3
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-5.3-Codex-Spark
90.8
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-5.3-Codex-Spark is ahead overall, 87 to 35. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 90 and 35.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 53. Inside this category, FrontierScience is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for coding in this comparison, averaging 82.3 versus 40. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for reasoning in this comparison, averaging 92.7 versus 38.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for agentic tasks in this comparison, averaging 85.6 versus 31.8. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for multimodal and grounded tasks in this comparison, averaging 88.3 versus 35.5. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for multilingual tasks in this comparison, averaging 90.8 versus 42. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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