GPT-5.3-Codex
OpenAI · released Feb 24, 2026
GPT-5.3-Codex is OpenAI’s most advanced agentic coding model, combining the frontier software engineering performance of GPT-5.2-Codex with the broader reasoning and professional knowledge capabilities of GPT-5.2. It achieves state-of-the-art results...
Specification
- Context window
- 400K
- Max output
- 128K
- Knowledge cutoff
- Not stated
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 2
- Moderated
- Yes
- Uptime
- 100.0%
Intelligence
45.5
93th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
61 t/s
Median across providers
Latency
2.84s
Time to first token
Cost per task
$1.21
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- GPQA Diamond91.5%
- τ²-bench (Telecom)86.0%
- AA-LCR (long context)78.3%
- IFBench75.4%
- SciCode53.2%
- Terminal-Bench Hard53.0%
- Humanity's Last Exam42.5%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| GPQA Diamond | 91.5% |
| τ²-bench (Telecom) | 86.0% |
| AA-LCR (long context) | 78.3% |
| IFBench | 75.4% |
| SciCode | 53.2% |
| Terminal-Bench Hard | 53.0% |
| Humanity's Last Exam | 42.5% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Gemini 3.1 Pro PreviewGoogle47.7
- Qwen3.7 MaxQwen46.7
- GPT-5.3-CodexOpenAI45.5
- MiniMax M3MiniMax45.4
- V4 ProDeepSeek45.3
- Kimi K2.6Moonshot AI45.1
- GPT-5.2OpenAI43.3
- Kimi K2.7 CodeMoonshot AI43.0
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Gemini 3.1 Pro Preview | 47.7 |
| Qwen3.7 Max | 46.7 |
| GPT-5.3-Codex | 45.5 |
| MiniMax M3 | 45.4 |
| V4 Pro | 45.3 |
| Kimi K2.6 | 45.1 |
| GPT-5.2 | 43.3 |
| Kimi K2.7 Code | 43.0 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $1.75
- Output / 1M tokens
- $14
- Cached input / 1M
- $0.175
- Blended 3:1
- $4.81
One task, estimated
$1.21
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Arena
Head-to-head generation quality
Elo from pairwise judgements, broken out by the kind of thing the model was asked to build.
- Overall Elo
- 1,203
- Win rate
- 51.3%
- Strongest at
- Game development
- Tournaments
- 2,963
Elo by category
Dot position on a 1,000–1,225 scale · Elo has no meaningful zero
- 1,203
- 1,191
- 1,185
- 1,176
- 1,172
- 1,063
- 1,114
- 1,024
Agent categories (full-stack apps, mobile apps) are only scored for models tested in agent harnesses.
View as table
| Category | Elo | Win rate |
|---|---|---|
| Game development | 1203 | 51.3% |
| Data visualisation | 1191 | 50.5% |
| Websites | 1185 | 48.6% |
| SVG | 1176 | 54.0% |
| UI components | 1172 | 47.3% |
| 3D scenes | 1063 | 35.3% |
| Mobile apps | 1114 | 41.4% |
| Full-stack apps | 1024 | 36.4% |