GPT-5.2-Codex
BenchLM is tracking GPT-5.2-Codex, but sourced benchmark results are not published on the site yet. This page currently shows the model metadata we can verify now, and score-level benchmark coverage will appear once public evaluations land.
GPT-5.2-Codex is a proprietary model with a 400K token context window. It uses explicit chain-of-thought reasoning, which typically improves performance on math and complex reasoning tasks at the cost of higher latency and token usage.
This profile currently has 0 sourced benchmarks on BenchLM, so the benchmark sections below are intentionally marked as coming soon.
Its strongest category is Mathematics (#2), while its weakest is Knowledge (#20). This performance profile makes it particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
Ranking Distribution
Category rank across 8 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
Coding
#13Reasoning
#9Knowledge
#20Math
#2Multilingual
#10Multimodal
#13Inst. Following
#9Chatbot Arena Performance
Benchmark Details
Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.
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See how GPT-5.2-Codex stacks up against similar models
Frequently Asked Questions
How does GPT-5.2-Codex perform overall in AI benchmarks?
BenchLM is tracking GPT-5.2-Codex, but sourced benchmark coverage is still coming soon. We currently list its creator, model type, and context window while we wait for public benchmark results.
Does GPT-5.2-Codex have full benchmark coverage on BenchLM?
Not yet. GPT-5.2-Codex currently has 0 published benchmark scores out of the 152 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
What is the context window size of GPT-5.2-Codex?
GPT-5.2-Codex has a context window of 400K, which determines how much text it can process in a single interaction.
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