GPT-5.2 vs GPT-5.3-Codex
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | GPT-5.2 | GPT-5.3-Codex |
|---|---|---|
| Developer | OpenAI | OpenAI |
| Family | Gpt 5 2 | Gpt 5 3 |
| Model | GPT-5.2 | GPT-5.3-Codex |
| Version | GPT-5.2 | GPT-5.3-Codex |
| Lifecycle | active | active |
| Released | 2025-12-11 | Unknown |
| Knowledge cutoff | 2025-08-31 | 2025-08-31 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 400,000 | 400,000 |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Openai (Standard), Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, reasoning, structured_outputs, tools |
14 comparable fields · 3 material differences · Pair passes the primary-source comparison gate
GPT-5.2 Capabilities
GPT-5.3-Codex Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
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GPT-5.1OpenAI | vs | GPT-5.2OpenAI | family variantsimage, text |
GPT-5.3-CodexOpenAI | vs | cross-developer peersimage, text | |
GPT-5 ProOpenAI | vs | GPT-5.2OpenAI | family variantsimage, text |
GPT-5 NanoOpenAI | vs | GPT-5.3-CodexOpenAI | family variantsimage, text |
GPT-5.3-CodexOpenAI | vs | GPT-5.4 NanoOpenAI | family variantsimage, text |
GPT-5.2OpenAI | vs | Grok 4.5xAI | cross-developer peersimage, text |
 Gemma 4 31BGoogle DeepMind | vs | GPT-5.3-CodexOpenAI | cross-developer peersimage, text |
GPT-5.2OpenAI | vs |  Qwen3 VL PlusQwen | cross-developer peersimage, text |
Claude Sonnet 4.5Anthropic | vs | GPT-5.3-CodexOpenAI | cross-developer peersimage, text |
Claude Sonnet 4.6Anthropic | vs | GPT-5.2OpenAI | cross-developer peersimage, text |
GPT-4oOpenAI | vs | GPT-5.2OpenAI | family variantsimage, text |
Primary Evidence
Sources and Freshness
Questions
GPT-5.2 vs GPT-5.3-Codex FAQs
Is GPT-5.2 or GPT-5.3-Codex better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.2 and GPT-5.3-Codex, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-5.2 or GPT-5.3-Codex?+
GPT-5.2 is $1.75 and GPT-5.3-Codex is $1.75 per million tokens, so they are tied on this metric. GPT-5.2 is $14.00 and GPT-5.3-Codex is $14.00 per million tokens, so they are tied on this metric.
Which has a larger context window, GPT-5.2 or GPT-5.3-Codex?+
Neither model has a larger sourced context window in this comparison. GPT-5.2 is 400,000 and GPT-5.3-Codex is 400,000.
Which performs better in benchmarks, GPT-5.2 or GPT-5.3-Codex?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can GPT-5.2 or GPT-5.3-Codex be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. GPT-5.2 is not marked open weight; GPT-5.3-Codex is not marked open weight.
Can GPT-5.2 and GPT-5.3-Codex understand images?+
GPT-5.2 is documented with image input; GPT-5.3-Codex is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GPT-5.2 or GPT-5.3-Codex?+
Neither has a larger sourced maximum output. GPT-5.2 is 128,000 and GPT-5.3-Codex is 128,000.
Do GPT-5.2 and GPT-5.3-Codex support reasoning and tool use?+
GPT-5.2: reasoning, tool calling, and image input. GPT-5.3-Codex: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, GPT-5.2 or GPT-5.3-Codex?+
GPT-5.2 has 2 sourced provider routes; GPT-5.3-Codex has 2, a tie.
Which offers better value, GPT-5.2 or GPT-5.3-Codex?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.