Codex 1 vs GPT-4
Codex 1 (2025) and GPT-4 (2023) compare a coding-specialized model against a standalone API model. Codex 1 ships a 192K-token context window, while GPT-4 ships a 8K-token context window. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: Codex 1 is coding-specialized model, while GPT-4 is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | Codex 1 | GPT-4 |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents and code generation | multimodal apps, tool-calling agents, and provider-routed production |
| Decision fit | Coding, Agents, and Long context | Coding, Agents, and Vision |
| Context window | 192K | 8K |
| Cheapest output | - | $60/1M tokens |
| Provider routes | 0 tracked | 4 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Codex 1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Codex 1 uniquely exposes Reasoning in local model data.
- Local decision data tags Codex 1 for Coding, Agents, and Long context.
- GPT-4 has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-4 uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags GPT-4 for Coding, Agents, and Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Codex 1
Unavailable
No complete token price in local provider data
GPT-4
$39,000
Cheapest tracked route/tier: OpenAI API
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Codex 1 and GPT-4; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning before moving production traffic.
- GPT-4 adds Vision, Multimodal, and Function calling in local capability data.
- No overlapping tracked provider route is sourced for GPT-4 and Codex 1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
- Codex 1 adds Reasoning in local capability data.
Specs
Pricing and availability
| Pricing attribute | Codex 1 | GPT-4 |
|---|---|---|
| Input price | - | $30/1M tokens |
| Output price | - | $60/1M tokens |
| Providers | - |
Capabilities
| Capability | Codex 1 | GPT-4 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | No |
| Function calling | No | Yes |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | Yes | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: GPT-4, multimodal input: GPT-4, reasoning mode: Codex 1, function calling: GPT-4, and structured outputs: GPT-4. Both models share code execution, 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: Codex 1 has no token price sourced yet and GPT-4 has $30/1M input tokens. Provider availability is 0 tracked routes versus 4. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Codex 1 when coding workflow support and larger context windows are central to the workload. Choose GPT-4 when coding workflow support and broader provider choice 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Codex 1 or GPT-4?
Codex 1 supports 192K tokens, while GPT-4 supports 8K 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 Codex 1 or GPT-4 open source?
Codex 1 is listed under Proprietary. GPT-4 is listed under Proprietary. 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, Codex 1 or GPT-4?
GPT-4 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 multimodal input, Codex 1 or GPT-4?
GPT-4 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for reasoning mode, Codex 1 or GPT-4?
Codex 1 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.
Where can I run Codex 1 and GPT-4?
Codex 1 is available on the tracked providers still being sourced. GPT-4 is available on OpenAI API, Azure OpenAI, Salesforce Einstein Generative AI, and OpenRouter. 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.