GPT-5.3-Codex vs o3 Pro
GPT-5.3-Codex leads o3 Pro on the Artificial Analysis Intelligence Index, 32.5 to 21.9, and GPT-5.3-Codex costs 5.7x less per output token ($14.00 vs $80.00 per million). Updated October 21, 2026; 4 benchmarks have a result for both.
GPT-5.3-Codex or o3 Pro: which to pick
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; o3 Pro has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 4
- GPT-5.3-Codex only
- 30
- o3 Pro only
- 8
- Like-for-like categories
- 3 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GPT-5.3-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 against 21.9.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.3-Codex has a Design Arena: websites result, 1163.
Directional only
Long documents
Listed context window
GPT-5.3-Codex
GPT-5.3-Codex takes 400K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.3-Codex
GPT-5.3-Codex $0.0088, o3 Pro $0.060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 benchmarks, o3 Pro not benchmarked.
- Widest category
- Coding, 21.1 points apart on average over 1 shared benchmark.
- Widest single gap
- WeirdML: GPT-5.3-Codex 79.3% against o3 Pro 58.2%, 21.1 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against o3 Pro $80.00 per million output tokens, 5.7x apart.
- Context window
- GPT-5.3-Codex takes 400K tokens, o3 Pro 200K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.3-Codex | Bars | o3 Pro | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 1 | 0 | ||
| All shared boards | 4 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.3-Codex | o3 Pro | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 84.9% | Only o3 Pro measured |
| LMArena WebDev | 1407 | Not measured | Only GPT-5.3-Codex measured |
| LiveCodeBench (Vals AI) | 87.3% | Not measured | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | 78.0% | Not measured | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | 61.8% | Not measured | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| WeirdML | 79.3% | 58.2% | GPT-5.3-Codex |
| ALE-Bench | 1655.2 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: React | 1395 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: HTML | 1422 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | o3 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 21.9 | GPT-5.3-Codex |
| Epoch Capabilities Index | 157.1 | 147.3 | GPT-5.3-Codex |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.3-Codex | o3 Pro | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1032 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 983 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1054 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | 86.0% | Not measured | Only GPT-5.3-Codex measured |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | $5,940 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | o3 Pro | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 91.5% | 84.5% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | Not measured | Only GPT-5.3-Codex measured |
| ARC-AGI | Not measured | 59.3% | Only o3 Pro measured |
| ARC-AGI-2 | Not measured | 4.9% | Only o3 Pro measured |
| Kagi LLM Benchmark | Not measured | 72.1% | Only o3 Pro measured |
| Neither model has a result on the other 17 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | o3 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | o3 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.3-Codex | o3 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: UI components | 1137 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: game dev | 1161 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: data viz | 1164 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: 3D | 1008 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | 1145 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: SVG | 1129 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | 1152 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.3-Codex | o3 Pro | Leader |
|---|---|---|---|
| IFBench | 75.4% | Not measured | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | 83.3% | Not measured | Only GPT-5.3-Codex measured |
| DTBench | Not measured | 86.9% | Only o3 Pro measured |
| LMCA | Not measured | 38.5% | Only o3 Pro measured |
| Fiction.LiveBench | Not measured | 97.2% | Only o3 Pro measured |
| Creative writing (Lech Mazur) | Not measured | 84.4% | Only o3 Pro measured |
| Neither model has a result on the other 40 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | o3 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-5.3-Codex costs less
Repository review
50K input + 3K output tokens
GPT-5.3-Codex costs less
Agent loop
200K input + 10K output tokens
GPT-5.3-Codex costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.3-Codex | OpenAI o3 Pro |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | February 24, 2026 | June 10, 2025 |
| Context window | 400K | 200K |
| Input $/Mtok | $1.75 | $20.00 |
| Output $/Mtok | $14.00 | $80.00 |
| Coding index | 60.0 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than o3 Pro for coding?
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; o3 Pro has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-5.3-Codex or o3 Pro?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; o3 Pro costs $20.00 and $80.00. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or o3 Pro?
GPT-5.3-Codex, with 400K tokens against 200K.
How many benchmarks is the GPT-5.3-Codex vs o3 Pro comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 3 independent benchmarks and o3 Pro's on 0. Where only one model has a result, the page says so instead of naming a winner.