GPT-5.2 Codex vs GPT-4.1
Head-to-head specifications
| Metric | GPT-5.2 Codex | GPT-4.1 | Difference |
|---|---|---|---|
| Intelligence Index | 40.0 | 24.0 | +66.7% |
| Context window | 922K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $1.05 | $0.90 | +16.7% |
| Output speed (tokens/s) | 164 | 103 | +59.2% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.2 Codex leads overall capability (Intelligence Index 40.0 vs 24.0).
- GPT-4.1 is the cheaper model to run at $0.90/1M blended tokens — about 1.2× cheaper.
- GPT-4.1 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5.2 Codex or GPT-4.1?
GPT-5.2 Codex advantages
- General intelligence (+40%)
- Output speed (+37%)
GPT-4.1 advantages
- Context window (+8%)
- Affordability (+14%)
Which should you choose?
- Choose the GPT-5.2 Codex if you need the strongest overall reasoning and accuracy.
- Choose the GPT-4.1 if you work with long documents or large codebases.
- Choose the GPT-5.2 Codex if low latency and fast generation matter for your application.
Value for money
GPT-5.2 Codex offers more intelligence per dollar (1.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.2 Codex vs GPT-4.1: which should you choose?
GPT-5.2 Codex — OpenAI multimodal model with an Intelligence Index of 40, a 922K-token context window and a blended price of $1.05/1M tokens.
GPT-4.1 — OpenAI multimodal model with an Intelligence Index of 24, a 1M-token context window and a blended price of $0.9/1M tokens.
GPT-5.2 Codex vs GPT-4.1: GPT-5.2 Codex scores higher on the Intelligence Index. GPT-5.2 Codex leads overall capability (Intelligence Index 40.0 vs 24.0). GPT-4.1 is the cheaper model to run at $0.90/1M blended tokens — about 1.2× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.2 Codex scores 40.0 versus 24.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-4.1 accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5.2 Codex generates faster (164 vs 103 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GPT-4.1 is the cheaper model to run ($0.90 vs $1.05 per 1M tokens). GPT-5.2 Codex is proprietary api and GPT-4.1 is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the GPT-5.2 Codex better than the GPT-4.1?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. GPT-5.2 Codex leads overall capability (Intelligence Index 40.0 vs 24.0).
What is the main difference between the GPT-5.2 Codex and the GPT-4.1?
GPT-5.2 Codex leads overall capability (Intelligence Index 40.0 vs 24.0). GPT-4.1 is the cheaper model to run at $0.90/1M blended tokens — about 1.2× cheaper.
Which is better value?
GPT-5.2 Codex offers more intelligence per dollar (1.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GPT-5.2 Codex if you need the strongest overall reasoning and accuracy. Choose the GPT-4.1 if you work with long documents or large codebases.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.