GPT-5 vs GPT-5.1-Codex
GPT-5 leads GPT-5.1-Codex on coding, 38.8 to 15.3 on the Vibe Coding Index, at about the same output price ($10.00 vs $10.00 per million). Updated October 21, 2026; 23 benchmarks have a result for both.
GPT-5 or GPT-5.1-Codex: which to pick
Pick GPT-5 for coding: it scores 38.8 on the Vibe Coding Index against 15.3 for GPT-5.1-Codex.
Pick GPT-5.1-Codex for work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench, where it leads.
- Shared results
- 23
- GPT-5 only
- 90
- GPT-5.1-Codex only
- 3
- Like-for-like categories
- 8 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
GPT-5
GPT-5 scores 38.8 against 15.3.
3 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.1-Codex
GPT-5.1-Codex leads, 23.7 against 23.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5 has a GPQA Diamond result, 85.6%.
Directional only
Math
AIME
No call
Only GPT-5 has a AIME result, 93.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 has a MMLU Pro result, 86.5%.
Directional only
Web design
Design Arena: websites
GPT-5
GPT-5 leads, 1184 against 1162.
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
No call
GPT-5 $0.0063, GPT-5.1-Codex $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5
GPT-5 $0.39, GPT-5.1-Codex $0.892.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5 38.8 from 3 benchmarks, GPT-5.1-Codex 15.3 from 2 benchmarks.
- Widest category
- Coding, 7.4 points apart on average over 9 shared benchmarks.
- Widest single gap
- ALE-Bench: GPT-5 807.6 against GPT-5.1-Codex 1244.9, 14.6 points apart on a 0 to 100 scale.
- Output price
- Both cost $10.00 per million output tokens.
- 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 | Bars | GPT-5.1-Codex | Ties |
|---|---|---|---|---|
| Coding | 6 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 1 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 0 | ||
| Web design | 4 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 15 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index63
| Benchmark | GPT-5 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Aider polyglot | 88.0% | Not measured | Only GPT-5 measured |
| SWE-bench Verified | 74.4% | 66.0% | GPT-5 |
| LMArena WebDev | 1418 | 1337 | GPT-5 |
| AA Coding Index | 37.8 | Not measured | Only GPT-5 measured |
| LiveCodeBench (Vals AI) | 85.9% | 85.5% | GPT-5 |
| SWE-bench Verified (Vals AI) | 69.0% | Not measured | Only GPT-5 measured |
| Vibe Code Bench (Vals AI) | 20.1% | 13.1% | GPT-5 |
| LiveCodeBench (Artificial Analysis run) | 84.6% | 84.9% | GPT-5.1-Codex |
| Terminal-Bench Hard (Artificial Analysis run) | 37.9% | 34.8% | GPT-5 |
| Terminal-Bench | 49.6% | 57.8% | GPT-5.1-Codex |
| GSO-Bench | 6.9% | Not measured | Only GPT-5 measured |
| WeirdML | 60.7% | Not measured | Only GPT-5 measured |
| ALE-Bench | 807.6 | 1244.9 | GPT-5.1-Codex |
| LMArena Text: coding | 1436 | Not measured | Only GPT-5 measured |
| LMArena Text: software and IT | 1420 | Not measured | Only GPT-5 measured |
| LMArena WebDev: HTML | 1427 | 1346 | GPT-5 |
| SciCode | 0.0% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.0 | 23.7 | GPT-5.1-Codex |
| Epoch Capabilities Index | 150.0 | Not measured | Only GPT-5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1018 | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 1086 | Only GPT-5.1-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1191 | Only GPT-5.1-Codex measured |
| τ²-Bench Telecom | 86.5% | 83.0% | GPT-5 |
| METR time horizons | 69.6% | Not measured | Only GPT-5 measured |
| Remote Labor Index | 1.7% | Not measured | Only GPT-5 measured |
| GDPval | 34.8% | Not measured | Only GPT-5 measured |
| BALROG | 32.8% | Not measured | Only GPT-5 measured |
| DeepResearch Bench | 49.6% | Not measured | Only GPT-5 measured |
| BrowseComp-Plus | 80.0% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | Not measured | Only GPT-5 measured |
| MMMU Pro (Vals AI) | 81.5% | Not measured | Only GPT-5 measured |
| GPQA Diamond (Artificial Analysis run) | 85.4% | 86.0% | GPT-5.1-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 28.5% | 25.7% | GPT-5 |
| GPQA Diamond (Epoch AI run) | 86.2% | Not measured | Only GPT-5 measured |
| ARC-AGI | 65.7% | Not measured | Only GPT-5 measured |
| ARC-AGI-2 | 9.9% | Not measured | Only GPT-5 measured |
| SimpleBench | 56.7% | Not measured | Only GPT-5 measured |
| Chess puzzles | 36.0% | Not measured | Only GPT-5 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5 measured |
| EBR-bench | 12.7% | Not measured | Only GPT-5 measured |
| VPCT | 66.0% | Not measured | Only GPT-5 measured |
| LMArena Text: hard prompts | 1415 | Not measured | Only GPT-5 measured |
| LMArena Text: expert | 1419 | Not measured | Only GPT-5 measured |
| LMArena Text: hard prompts in English | 1419 | Not measured | Only GPT-5 measured |
| LMArena Vision: diagrams | 1252 | Not measured | Only GPT-5 measured |
| LMArena Vision: homework | 1278 | Not measured | Only GPT-5 measured |
| Kagi LLM Benchmark | 72.7% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AIME | 93.4% | Not measured | Only GPT-5 measured |
| MATH 500 (Vals AI) | 96.0% | Not measured | Only GPT-5 measured |
| MGSM (Vals AI) | 92.8% | Not measured | Only GPT-5 measured |
| AA Math Index | 94.3 | 95.7 | GPT-5.1-Codex |
| AIME 2025 (Artificial Analysis run) | 94.3% | 95.7% | GPT-5.1-Codex |
| OTIS Mock AIME | 91.4% | Not measured | Only GPT-5 measured |
| FrontierMath, tiers 1 to 3 | 55.4% | Not measured | Only GPT-5 measured |
| FrontierMath, tier 4 | 22.0% | Not measured | Only GPT-5 measured |
| ProofBench | 18.0% | Not measured | Only GPT-5 measured |
| MATH level 5 | 98.1% | Not measured | Only GPT-5 measured |
| LMArena Text: math | 1407 | Not measured | Only GPT-5 measured |
| LMArena Text: mathematical work | 1405 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| MMLU Pro | 86.5% | Not measured | Only GPT-5 measured |
| LegalBench (Vals AI) | 86.0% | Not measured | Only GPT-5 measured |
| CaseLaw (Vals AI) | 66.5% | Not measured | Only GPT-5 measured |
| CorpFin (Vals AI) | 61.1% | Not measured | Only GPT-5 measured |
| TaxEval (Vals AI) | 73.4% | Not measured | Only GPT-5 measured |
| MortgageTax (Vals AI) | 65.5% | Not measured | Only GPT-5 measured |
| MedQA (Vals AI) | 96.3% | Not measured | Only GPT-5 measured |
| MedCode (Vals AI) | 49.6% | Not measured | Only GPT-5 measured |
| MMLU Pro (Artificial Analysis run) | 87.1% | 86.0% | GPT-5 |
| SimpleQA Verified | 50.1% | Not measured | Only GPT-5 measured |
| GeoBench | 81.0% | Not measured | Only GPT-5 measured |
| LMArena Text: science | 1416 | Not measured | Only GPT-5 measured |
| LMArena Text: medicine and healthcare | 1413 | Not measured | Only GPT-5 measured |
| LMArena Text: legal and government | 1431 | Not measured | Only GPT-5 measured |
| LMArena Text: business and finance | 1400 | Not measured | Only GPT-5 measured |
| FACTS Benchmark Suite | 56.5% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GPT-5 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1184 | 1162 | GPT-5 |
| Design Arena: UI components | 1180 | 1237 | GPT-5.1-Codex |
| Design Arena: game dev | 1185 | 1138 | GPT-5 |
| Design Arena: data viz | 1221 | 1213 | GPT-5 |
| Design Arena: 3D | 1056 | Not measured | Only GPT-5 measured |
| Design Arena: all code categories | 1168 | 1152 | GPT-5 |
| Design Arena: SVG | 1189 | Not measured | Only GPT-5 measured |
| Design Arena: ASCII art | 1141 | Not measured | Only GPT-5 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 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 43.7% | Not measured | Only GPT-5 measured |
| MedScribe (Vals AI) | 83.7% | Not measured | Only GPT-5 measured |
| IFBench | 73.1% | 70.0% | GPT-5 |
| Long context reasoning (AA-LCR) | 78.2% | 69.3% | GPT-5 |
| DTBench | 90.7% | Not measured | Only GPT-5 measured |
| LMCA | 40.0% | Not measured | Only GPT-5 measured |
| Fiction.LiveBench | 97.2% | Not measured | Only GPT-5 measured |
| Creative writing (Lech Mazur) | 86.0% | Not measured | Only GPT-5 measured |
| LMArena Text | 1406 | Not measured | Only GPT-5 measured |
| LMArena Text: instruction following | 1388 | Not measured | Only GPT-5 measured |
| LMArena Text: creative writing | 1365 | Not measured | Only GPT-5 measured |
| LMArena Vision | 1232 | Not measured | Only GPT-5 measured |
| LMArena Text: English | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: non-English | 1397 | Not measured | Only GPT-5 measured |
| LMArena Text: Chinese | 1422 | Not measured | Only GPT-5 measured |
| LMArena Text: German | 1416 | Not measured | Only GPT-5 measured |
| LMArena Text: Japanese | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: Korean | 1360 | Not measured | Only GPT-5 measured |
| LMArena Text: French | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: Spanish | 1399 | Not measured | Only GPT-5 measured |
| LMArena Text: Russian | 1407 | Not measured | Only GPT-5 measured |
| LMArena Text: Polish | 1431 | Not measured | Only GPT-5 measured |
| LMArena Text: multi-turn | 1426 | Not measured | Only GPT-5 measured |
| LMArena Text: longer queries | 1399 | Not measured | Only GPT-5 measured |
| LMArena Text: writing and language | 1384 | Not measured | Only GPT-5 measured |
| LMArena Text: entertainment and media | 1379 | Not measured | Only GPT-5 measured |
| LMArena Vision: English | 1231 | Not measured | Only GPT-5 measured |
| LMArena Vision: Chinese | 1267 | Not measured | Only GPT-5 measured |
| LMArena Vision: reading text | 1246 | Not measured | Only GPT-5 measured |
| LMArena Vision: humor | 1219 | Not measured | Only GPT-5 measured |
| LMArena Vision: captioning | 1212 | Not measured | Only GPT-5 measured |
| LMArena Vision: recognizing things | 1263 | Not measured | Only GPT-5 measured |
| LMArena Vision: creative writing | 1210 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 | GPT-5.1-Codex | 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
Same cost
Repository review
50K input + 3K output tokens
Same cost
Agent loop
200K input + 10K output tokens
Same cost
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 | OpenAI GPT-5.1-Codex |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 7, 2025 | November 13, 2025 |
| Context window | 400K | 400K |
| Input $/Mtok | $1.25 | $1.25 |
| Output $/Mtok | $10.00 | $10.00 |
| Coding index | 38.8 | 15.3 |
| Independent measurements behind it | 3 | 2 |
| Cost per solved task | $0.39 | $0.892 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 better than GPT-5.1-Codex for coding?
Pick GPT-5 for coding: it scores 38.8 on the Vibe Coding Index against 15.3 for GPT-5.1-Codex. Pick GPT-5.1-Codex for work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench, where it leads.
Which is cheaper, GPT-5 or GPT-5.1-Codex?
GPT-5 costs $1.25 per million input tokens and $10.00 per million output tokens; GPT-5.1-Codex costs $1.25 and $10.00. Per solved SWE-bench Verified task, GPT-5 costs $0.39 and GPT-5.1-Codex $0.892.
Which has the bigger context window, GPT-5 or GPT-5.1-Codex?
Neither: both take 400K tokens.
How many benchmarks is the GPT-5 vs GPT-5.1-Codex comparison based on?
23 benchmarks have a result for both models as of October 21, 2026. GPT-5's coding index rests on 3 independent benchmarks and GPT-5.1-Codex's on 2. Where only one model has a result, the page says so instead of naming a winner.