GPT-5.3-Codex vs K-EXAONE 236B-A23B
GPT-5.3-Codex (2026) and K-EXAONE 236B-A23B (2025) are agentic coding models from OpenAI and LG Research. GPT-5.3-Codex ships a 400K-token context window, while K-EXAONE 236B-A23B ships a 256k-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
GPT-5.3-Codex is safer overall; choose K-EXAONE 236B-A23B when provider fit matters.
Decision scorecard
Local evidence first| Signal | GPT-5.3-Codex | K-EXAONE 236B-A23B |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | Long context |
| Context window | 400K | 256k |
| Cheapest output | $14/1M tokens | - |
| Provider routes | 2 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GPT-5.3-Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.3-Codex has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-5.3-Codex uniquely exposes Vision, Reasoning, and Function calling in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
- Local decision data tags K-EXAONE 236B-A23B for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
GPT-5.3-Codex
$4,900
Cheapest tracked route: OpenRouter
K-EXAONE 236B-A23B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for GPT-5.3-Codex and K-EXAONE 236B-A23B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Reasoning, and Function calling before moving production traffic.
- No overlapping tracked provider route is sourced for K-EXAONE 236B-A23B and GPT-5.3-Codex; plan for SDK, billing, or endpoint changes.
- GPT-5.3-Codex adds Vision, Reasoning, and Function calling in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-05 | 2025-12-31 |
| Context window | 400K | 256k |
| Parameters | — | 236B |
| Architecture | decoder only | MoE |
| License | Proprietary | Open Source |
| Knowledge cutoff | 2025-08 | - |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex | K-EXAONE 236B-A23B |
|---|---|---|
| Input price | $1.75/1M tokens | - |
| Output price | $14/1M tokens | - |
| Providers | - |
Capabilities
| Capability | GPT-5.3-Codex | K-EXAONE 236B-A23B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | No |
| Code execution | Yes | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: GPT-5.3-Codex, reasoning mode: GPT-5.3-Codex, function calling: GPT-5.3-Codex, tool use: GPT-5.3-Codex, structured outputs: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: GPT-5.3-Codex has $1.75/1M input tokens and K-EXAONE 236B-A23B has no token price sourced yet. Provider availability is 2 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GPT-5.3-Codex when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose K-EXAONE 236B-A23B when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, GPT-5.3-Codex or K-EXAONE 236B-A23B?
GPT-5.3-Codex supports 400K tokens, while K-EXAONE 236B-A23B supports 256k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Is GPT-5.3-Codex or K-EXAONE 236B-A23B open source?
GPT-5.3-Codex is listed under Proprietary. K-EXAONE 236B-A23B is listed under Open Source. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, GPT-5.3-Codex or K-EXAONE 236B-A23B?
GPT-5.3-Codex has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for reasoning mode, GPT-5.3-Codex or K-EXAONE 236B-A23B?
GPT-5.3-Codex has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, GPT-5.3-Codex or K-EXAONE 236B-A23B?
GPT-5.3-Codex has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GPT-5.3-Codex and K-EXAONE 236B-A23B?
GPT-5.3-Codex is available on OpenRouter and OpenAI API. K-EXAONE 236B-A23B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.