GPT-5.3-Codex vs GPT-5 Nano
GPT-5.3-Codex leads GPT-5 Nano on the Artificial Analysis Intelligence Index, 32.5 to 13.0, and GPT-5 Nano costs 35.0x less per output token ($0.40 vs $14.00 per million). Updated October 21, 2026; 17 benchmarks have a result for both.
GPT-5.3-Codex or GPT-5 Nano: which to pick
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding.
Pick GPT-5 Nano for output tokens 35.0x cheaper ($0.40 vs $14.00 per million) and a lower cost per solved task ($0.109 against $0.59).
- Shared results
- 17
- GPT-5.3-Codex only
- 17
- GPT-5 Nano only
- 70
- Like-for-like categories
- 6 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 13.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5 Nano has a GPQA Diamond result, 63.4%.
Directional only
Math
AIME
No call
Only GPT-5 Nano has a AIME result, 81.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 Nano has a MMLU Pro result, 76.1%.
Directional only
Web design
Design Arena: websites
GPT-5.3-Codex
GPT-5.3-Codex leads, 1163 against 1101.
Same board, both measured
Long documents
Listed context window
No call
Both take 400K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
GPT-5.3-Codex $0.0088, GPT-5 Nano $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Nano
GPT-5.3-Codex $0.59, GPT-5 Nano $0.109.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 benchmarks, GPT-5 Nano not benchmarked.
- Widest category
- Agentic work, 49.4 points apart on average over 1 shared benchmark.
- Widest single gap
- Terminal-Bench: GPT-5.3-Codex 78.4% against GPT-5 Nano 21.8%, 56.6 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against GPT-5 Nano $0.40 per million output tokens, 35.0x apart.
- Context window
- Both take 400K tokens.
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 | GPT-5 Nano | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Web design | 6 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 17 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GPT-5.3-Codex | GPT-5 Nano | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 34.8% | Only GPT-5 Nano measured |
| LMArena WebDev | 1407 | Not measured | Only GPT-5.3-Codex measured |
| LiveCodeBench (Vals AI) | 87.3% | 70.2% | GPT-5.3-Codex |
| 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 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.9% | Only GPT-5 Nano measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | 17.4% | GPT-5.3-Codex |
| Terminal-Bench | 78.4% | 21.8% | GPT-5.3-Codex |
| WeirdML | 79.3% | 38.1% | GPT-5.3-Codex |
| ALE-Bench | 1655.2 | Not measured | Only GPT-5.3-Codex measured |
| LMArena Text: coding | Not measured | 1351 | Only GPT-5 Nano measured |
| LMArena Text: software and IT | Not measured | 1345 | Only GPT-5 Nano 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 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 13.0 | GPT-5.3-Codex |
| Epoch Capabilities Index | 157.1 | 139.2 | GPT-5.3-Codex |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | GPT-5 Nano | 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% | 36.5% | GPT-5.3-Codex |
| 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 |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 51.4% | Only GPT-5 Nano measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | GPT-5 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 63.4% | Only GPT-5 Nano measured |
| MMMU Pro (Vals AI) | Not measured | 70.9% | Only GPT-5 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 91.5% | 67.6% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | 9.5% | GPT-5.3-Codex |
| GPQA Diamond (Epoch AI run) | Not measured | 69.4% | Only GPT-5 Nano measured |
| ARC-AGI | Not measured | 20.7% | Only GPT-5 Nano measured |
| ARC-AGI-2 | Not measured | 2.6% | Only GPT-5 Nano measured |
| Chess puzzles | Not measured | 22.0% | Only GPT-5 Nano measured |
| Mystery game puzzles | Not measured | 9.0% | Only GPT-5 Nano measured |
| VPCT | Not measured | 37.2% | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | Not measured | 1328 | Only GPT-5 Nano measured |
| LMArena Text: expert | Not measured | 1321 | Only GPT-5 Nano measured |
| LMArena Text: hard prompts in English | Not measured | 1338 | Only GPT-5 Nano measured |
| LMArena Vision: diagrams | Not measured | 1141 | Only GPT-5 Nano measured |
| LMArena Vision: homework | Not measured | 1157 | Only GPT-5 Nano measured |
| Kagi LLM Benchmark | Not measured | 62.2% | Only GPT-5 Nano measured |
| Neither model has a result on the other 6 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5 Nano | Leader |
|---|---|---|---|
| AIME | Not measured | 81.2% | Only GPT-5 Nano measured |
| MATH 500 (Vals AI) | Not measured | 93.8% | Only GPT-5 Nano measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only GPT-5 Nano measured |
| AA Math Index | Not measured | 83.7 | Only GPT-5 Nano measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 83.7% | Only GPT-5 Nano measured |
| OTIS Mock AIME | Not measured | 81.1% | Only GPT-5 Nano measured |
| FrontierMath, tiers 1 to 3 | Not measured | 20.0% | Only GPT-5 Nano measured |
| FrontierMath, tier 4 | Not measured | 2.4% | Only GPT-5 Nano measured |
| ProofBench | Not measured | 12.0% | Only GPT-5 Nano measured |
| MATH level 5 | Not measured | 95.2% | Only GPT-5 Nano measured |
| LMArena Text: math | Not measured | 1317 | Only GPT-5 Nano measured |
| LMArena Text: mathematical work | Not measured | 1326 | Only GPT-5 Nano measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5 Nano | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 76.1% | Only GPT-5 Nano measured |
| LegalBench (Vals AI) | Not measured | 50.1% | Only GPT-5 Nano measured |
| CaseLaw (Vals AI) | Not measured | 52.6% | Only GPT-5 Nano measured |
| TaxEval (Vals AI) | Not measured | 67.4% | Only GPT-5 Nano measured |
| MortgageTax (Vals AI) | Not measured | 53.6% | Only GPT-5 Nano measured |
| MedQA (Vals AI) | Not measured | 93.3% | Only GPT-5 Nano measured |
| MedCode (Vals AI) | Not measured | 30.4% | Only GPT-5 Nano measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 78.0% | Only GPT-5 Nano measured |
| SimpleQA Verified | Not measured | 11.7% | Only GPT-5 Nano measured |
| LMArena Text: science | Not measured | 1318 | Only GPT-5 Nano measured |
| LMArena Text: medicine and healthcare | Not measured | 1335 | Only GPT-5 Nano measured |
| LMArena Text: legal and government | Not measured | 1298 | Only GPT-5 Nano measured |
| LMArena Text: business and finance | Not measured | 1311 | Only GPT-5 Nano measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-5.3-Codex | GPT-5 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | 1101 | GPT-5.3-Codex |
| Design Arena: UI components | 1137 | 1067 | GPT-5.3-Codex |
| Design Arena: game dev | 1161 | 1045 | GPT-5.3-Codex |
| Design Arena: data viz | 1164 | 1056 | GPT-5.3-Codex |
| Design Arena: 3D | 1008 | 966 | GPT-5.3-Codex |
| Design Arena: all code categories | 1145 | 1084 | GPT-5.3-Codex |
| 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 | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | GPT-5 Nano | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 30.4% | Only GPT-5 Nano measured |
| MedScribe (Vals AI) | Not measured | 72.9% | Only GPT-5 Nano measured |
| IFBench | 75.4% | 67.6% | GPT-5.3-Codex |
| Long context reasoning (AA-LCR) | 83.3% | 45.0% | GPT-5.3-Codex |
| DTBench | Not measured | 62.7% | Only GPT-5 Nano measured |
| LMCA | Not measured | 7.9% | Only GPT-5 Nano measured |
| Fiction.LiveBench | Not measured | 44.4% | Only GPT-5 Nano measured |
| LMArena Text | Not measured | 1320 | Only GPT-5 Nano measured |
| LMArena Text: instruction following | Not measured | 1306 | Only GPT-5 Nano measured |
| LMArena Text: creative writing | Not measured | 1249 | Only GPT-5 Nano measured |
| LMArena Vision | Not measured | 1159 | Only GPT-5 Nano measured |
| LMArena Text: English | Not measured | 1321 | Only GPT-5 Nano measured |
| LMArena Text: non-English | Not measured | 1313 | Only GPT-5 Nano measured |
| LMArena Text: Chinese | Not measured | 1356 | Only GPT-5 Nano measured |
| LMArena Text: German | Not measured | 1327 | Only GPT-5 Nano measured |
| LMArena Text: Japanese | Not measured | 1227 | Only GPT-5 Nano measured |
| LMArena Text: Korean | Not measured | 1269 | Only GPT-5 Nano measured |
| LMArena Text: Spanish | Not measured | 1360 | Only GPT-5 Nano measured |
| LMArena Text: Russian | Not measured | 1296 | Only GPT-5 Nano measured |
| LMArena Text: Polish | Not measured | 1314 | Only GPT-5 Nano measured |
| LMArena Text: multi-turn | Not measured | 1311 | Only GPT-5 Nano measured |
| LMArena Text: longer queries | Not measured | 1312 | Only GPT-5 Nano measured |
| LMArena Text: writing and language | Not measured | 1283 | Only GPT-5 Nano measured |
| LMArena Text: entertainment and media | Not measured | 1268 | Only GPT-5 Nano measured |
| LMArena Vision: English | Not measured | 1170 | Only GPT-5 Nano measured |
| LMArena Vision: Chinese | Not measured | 1176 | Only GPT-5 Nano measured |
| LMArena Vision: reading text | Not measured | 1152 | Only GPT-5 Nano measured |
| LMArena Vision: humor | Not measured | 1110 | Only GPT-5 Nano measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5 Nano | 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 Nano costs less
Repository review
50K input + 3K output tokens
GPT-5 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-5 Nano 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 GPT-5 Nano |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | February 24, 2026 | August 7, 2025 |
| Context window | 400K | 400K |
| Input $/Mtok | $1.75 | $0.05 |
| Output $/Mtok | $14.00 | $0.40 |
| Coding index | 60.0 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.59 | $0.109 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than GPT-5 Nano for coding?
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 35.0x cheaper ($0.40 vs $14.00 per million) and a lower cost per solved task ($0.109 against $0.59).
Which is cheaper, GPT-5.3-Codex or GPT-5 Nano?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; GPT-5 Nano costs $0.05 and $0.40. Per solved SWE-bench Verified task, GPT-5.3-Codex costs $0.59 and GPT-5 Nano $0.109.
Which has the bigger context window, GPT-5.3-Codex or GPT-5 Nano?
Neither: both take 400K tokens.
How many benchmarks is the GPT-5.3-Codex vs GPT-5 Nano comparison based on?
17 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 GPT-5 Nano's on 0. Where only one model has a result, the page says so instead of naming a winner.