GPT-5.1-Codex vs o1
GPT-5.1-Codex leads o1 on the Artificial Analysis Intelligence Index, 23.7 to 15.2, and GPT-5.1-Codex costs 6.0x less per output token ($10.00 vs $60.00 per million). Updated October 22, 2026; 10 benchmarks have a result for both.
GPT-5.1-Codex or o1: which to pick
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding.
Pick o1 for work measured by IFBench, where it leads.
- Shared results
- 10
- GPT-5.1-Codex only
- 16
- o1 only
- 59
- 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-5.1-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.1-Codex
GPT-5.1-Codex leads, 23.7 against 15.2.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only o1 has a GPQA Diamond result, 73.2%.
Directional only
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
No call
Only o1 has a MMLU Pro result, 83.5%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex has a Design Arena: websites result, 1161.
Directional only
Long documents
Listed context window
GPT-5.1-Codex
GPT-5.1-Codex takes 400K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.1-Codex
GPT-5.1-Codex $0.0063, o1 $0.045 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.1-Codex has the number, $0.892.
Directional only
At a glance
- Coding index
- GPT-5.1-Codex 15.3 from 2 benchmarks, o1 not benchmarked.
- Widest category
- Coding, 24.8 points apart on average over 3 shared benchmarks.
- Widest single gap
- LiveCodeBench (Vals AI): GPT-5.1-Codex 85.5% against o1 50.3%, 35.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex $10.00 against o1 $60.00 per million output tokens, 6.0x apart.
- Context window
- GPT-5.1-Codex takes 400K tokens, o1 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-5.1-Codex | Bars | o1 | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 1 | 1 | ||
| All shared boards | 9 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GPT-5.1-Codex | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 measured |
| SWE-bench Verified | 66.0% | Not measured | Only GPT-5.1-Codex measured |
| LMArena WebDev | 1337 | Not measured | Only GPT-5.1-Codex measured |
| AA Coding Index | Not measured | 39.7 | Only o1 measured |
| LiveCodeBench (Vals AI) | 85.5% | 50.3% | GPT-5.1-Codex |
| Vibe Code Bench (Vals AI) | 13.1% | Not measured | Only GPT-5.1-Codex measured |
| LiveCodeBench (Artificial Analysis run) | 84.9% | 67.9% | GPT-5.1-Codex |
| Terminal-Bench Hard (Artificial Analysis run) | 34.8% | 12.9% | GPT-5.1-Codex |
| Terminal-Bench | 57.8% | Not measured | Only GPT-5.1-Codex measured |
| WeirdML | Not measured | 46.1% | Only o1 measured |
| ALE-Bench | 1244.9 | Not measured | Only GPT-5.1-Codex measured |
| LMArena Text: coding | Not measured | 1367 | Only o1 measured |
| LMArena Text: software and IT | Not measured | 1366 | Only o1 measured |
| LMArena WebDev: HTML | 1346 | Not measured | Only GPT-5.1-Codex measured |
| SciCode | Not measured | 4.6% | Only o1 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.7 | 15.2 | GPT-5.1-Codex |
| Epoch Capabilities Index | Not measured | 141.7 | Only o1 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex | o1 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1018 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | 1086 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building mobile apps | 1191 | Not measured | Only GPT-5.1-Codex measured |
| τ²-Bench Telecom | 83.0% | 62.6% | GPT-5.1-Codex |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 73.2% | Only o1 measured |
| MMMU Pro (Vals AI) | Not measured | 77.4% | Only o1 measured |
| GPQA Diamond (Artificial Analysis run) | 86.0% | 74.7% | GPT-5.1-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 25.7% | 7.0% | GPT-5.1-Codex |
| GPQA Diamond (Epoch AI run) | Not measured | 76.8% | Only o1 measured |
| ARC-AGI | Not measured | 30.7% | Only o1 measured |
| SimpleBench | Not measured | 40.1% | Only o1 measured |
| Chess puzzles | Not measured | 10.0% | Only o1 measured |
| VPCT | Not measured | 37.0% | Only o1 measured |
| LMArena Text: hard prompts | Not measured | 1371 | Only o1 measured |
| LMArena Text: expert | Not measured | 1361 | Only o1 measured |
| LMArena Text: hard prompts in English | Not measured | 1372 | Only o1 measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.1-Codex | o1 | Leader |
|---|---|---|---|
| AIME | Not measured | 71.5% | Only o1 measured |
| MATH 500 (Vals AI) | Not measured | 90.4% | Only o1 measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only o1 measured |
| AA Math Index | 95.7 | Not measured | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | 95.7% | Not measured | Only GPT-5.1-Codex measured |
| OTIS Mock AIME | Not measured | 73.3% | Only o1 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 14.7% | Only o1 measured |
| MATH level 5 | Not measured | 94.7% | Only o1 measured |
| LMArena Text: math | Not measured | 1388 | Only o1 measured |
| LMArena Text: mathematical work | Not measured | 1390 | Only o1 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex | o1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 83.5% | Only o1 measured |
| LegalBench (Vals AI) | Not measured | 80.4% | Only o1 measured |
| TaxEval (Vals AI) | Not measured | 74.3% | Only o1 measured |
| MedQA (Vals AI) | Not measured | 96.5% | Only o1 measured |
| MMLU Pro (Artificial Analysis run) | 86.0% | 84.1% | GPT-5.1-Codex |
| SimpleQA Verified | Not measured | 41.1% | Only o1 measured |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | Not measured | 1369 | Only o1 measured |
| LMArena Text: medicine and healthcare | Not measured | 1355 | Only o1 measured |
| LMArena Text: legal and government | Not measured | 1368 | Only o1 measured |
| LMArena Text: business and finance | Not measured | 1328 | Only o1 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.1-Codex | o1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1161 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: UI components | 1234 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: game dev | 1137 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: data viz | 1213 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: all code categories | 1152 | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.1-Codex | o1 | Leader |
|---|---|---|---|
| IFBench | 70.0% | 70.3% | o1 |
| Long context reasoning (AA-LCR) | 69.3% | 65.0% | GPT-5.1-Codex |
| DTBench | Not measured | 74.7% | Only o1 measured |
| LMCA | Not measured | 22.3% | Only o1 measured |
| Fiction.LiveBench | Not measured | 83.3% | Only o1 measured |
| Creative writing (Lech Mazur) | Not measured | 70.2% | Only o1 measured |
| LMArena Text | Not measured | 1366 | Only o1 measured |
| LMArena Text: instruction following | Not measured | 1367 | Only o1 measured |
| LMArena Text: creative writing | Not measured | 1347 | Only o1 measured |
| LMArena Vision | Not measured | 1168 | Only o1 measured |
| LMArena Text: English | Not measured | 1372 | Only o1 measured |
| LMArena Text: non-English | Not measured | 1358 | Only o1 measured |
| LMArena Text: Chinese | Not measured | 1394 | Only o1 measured |
| LMArena Text: German | Not measured | 1337 | Only o1 measured |
| LMArena Text: Japanese | Not measured | 1346 | Only o1 measured |
| LMArena Text: Korean | Not measured | 1396 | Only o1 measured |
| LMArena Text: French | Not measured | 1344 | Only o1 measured |
| LMArena Text: Spanish | Not measured | 1345 | Only o1 measured |
| LMArena Text: Russian | Not measured | 1356 | Only o1 measured |
| LMArena Text: multi-turn | Not measured | 1355 | Only o1 measured |
| LMArena Text: longer queries | Not measured | 1378 | Only o1 measured |
| LMArena Text: writing and language | Not measured | 1360 | Only o1 measured |
| LMArena Text: entertainment and media | Not measured | 1358 | Only o1 measured |
| LMArena Vision: English | Not measured | 1162 | Only o1 measured |
| LMArena Vision: Chinese | Not measured | 1235 | Only o1 measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex | o1 | 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-5.1-Codex costs less
Repository review
50K input + 3K output tokens
GPT-5.1-Codex costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-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-5.1-Codex | OpenAI o1 |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | December 17, 2024 |
| Context window | 400K | 200K |
| Input $/Mtok | $1.25 | $15.00 |
| Output $/Mtok | $10.00 | $60.00 |
| Coding index | 15.3 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.892 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex better than o1 for coding?
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding. Pick o1 for work measured by IFBench, where it leads.
Which is cheaper, GPT-5.1-Codex or o1?
GPT-5.1-Codex costs $1.25 per million input tokens and $10.00 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for GPT-5.1-Codex only.
Which has the bigger context window, GPT-5.1-Codex or o1?
GPT-5.1-Codex, with 400K tokens against 200K.
How many benchmarks is the GPT-5.1-Codex vs o1 comparison based on?
10 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex's coding index rests on 2 independent benchmarks and o1's on 0. Where only one model has a result, the page says so instead of naming a winner.