GPT-5.3 Codex
According to BenchLM.ai, GPT-5.3 Codex ranks #13 out of 119 models on the provisional leaderboard with an overall score of 86/100. It does not yet have enough sourced coverage for BenchLM's verified leaderboard. This places it in the upper tier of AI models, with competitive scores across most benchmark categories.
GPT-5.3 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.
GPT-5.3 Codex sits inside the GPT-5.3 Codex family alongside GPT-5.3-Codex-Spark. This profile currently has 24 of 225 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Mathematics (#1), while its weakest is Agentic (#15). 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
#15Coding
#9Reasoning
#4Knowledge
#6Math
#1Multilingual
#6Multimodal
#5Inst. Following
#13Chatbot 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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Frequently Asked Questions
How does GPT-5.3 Codex perform overall in AI benchmarks?
GPT-5.3 Codex currently ranks #13 out of 119 models on BenchLM's provisional leaderboard with an overall score of 86 (estimated). It is created by OpenAI and features a 400K context window.
Is GPT-5.3 Codex good for knowledge and understanding?
GPT-5.3 Codex ranks #6 out of 119 models in knowledge and understanding benchmarks with an average score of 92.8. It is among the top performers in this category.
Is GPT-5.3 Codex good for coding and programming?
GPT-5.3 Codex ranks #9 out of 119 models in coding and programming benchmarks with an average score of 87.5. It is among the top performers in this category.
Is GPT-5.3 Codex good for reasoning and logic?
GPT-5.3 Codex ranks #4 out of 119 models in reasoning and logic benchmarks with an average score of 93.4. It is among the top performers in this category.
Is GPT-5.3 Codex good for agentic tool use and computer tasks?
GPT-5.3 Codex ranks #15 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 79.2. There are stronger options in this category.
Is GPT-5.3 Codex good for multimodal and grounded tasks?
GPT-5.3 Codex ranks #5 out of 119 models in multimodal and grounded tasks benchmarks with an average score of 94.8. It is among the top performers in this category.
Is GPT-5.3 Codex good for instruction following?
GPT-5.3 Codex ranks #13 out of 119 models in instruction following benchmarks with an average score of 91.4. There are stronger options in this category.
Which sibling models are related to GPT-5.3 Codex?
GPT-5.3 Codex belongs to the GPT-5.3 Codex family. Related variants on BenchLM include GPT-5.3-Codex-Spark.
Does GPT-5.3 Codex have full benchmark coverage on BenchLM?
Not yet. GPT-5.3 Codex currently has 24 published benchmark scores out of the 225 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-5.3 Codex?
GPT-5.3 Codex has a context window of 400K, which determines how much text it can process in a single interaction.
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