GPT-4.1 Nano vs o1
o1 leads GPT-4.1 Nano on the Artificial Analysis Intelligence Index, 15.2 to 7.8, and GPT-4.1 Nano costs 150.0x less per output token ($0.40 vs $60.00 per million). Updated October 21, 2026; 58 benchmarks have a result for both.
GPT-4.1 Nano or o1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4.1 Nano for output tokens 150.0x cheaper ($0.40 vs $60.00 per million) and a larger context window (1.05M against 200K tokens).
- Shared results
- 58
- GPT-4.1 Nano only
- 13
- o1 only
- 11
- Like-for-like categories
- 7 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
o1
o1 leads, 15.2 against 7.8.
Same board, both measured
Reasoning
GPQA Diamond
o1
o1 leads, 73.2% against 50.8%.
Same board, both measured
Math
AIME
o1
o1 leads, 71.5% against 26.5%.
Same board, both measured
Knowledge
MMLU Pro
o1
o1 leads, 83.5% against 63.5%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-4.1 Nano has a Design Arena: websites result, 973.
Directional only
Long documents
Listed context window
GPT-4.1 Nano
GPT-4.1 Nano takes 1.05M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4.1 Nano
GPT-4.1 Nano $0.0003, o1 $0.045 per turn.
List prices
At a glance
- Coding index
- GPT-4.1 Nano not benchmarked, o1 not benchmarked.
- Widest category
- Agentic work, 45.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Fiction.LiveBench: GPT-4.1 Nano 25.0% against o1 83.3%, 58.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 Nano $0.40 against o1 $60.00 per million output tokens, 150.0x apart.
- Context window
- GPT-4.1 Nano takes 1.05M 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-4.1 Nano | Bars | o1 | Ties |
|---|---|---|---|---|
| Coding | 0 | 8 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 10 | ||
| Math | 0 | 7 | ||
| Knowledge | 0 | 10 | ||
| General | 0 | 20 | ||
| All shared boards | 0 | 58 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index08
| Benchmark | GPT-4.1 Nano | o1 | Leader |
|---|---|---|---|
| Aider polyglot | 8.9% | 61.7% | o1 |
| AA Coding Index | 11.1 | 39.7 | o1 |
| LiveCodeBench (Vals AI) | 42.7% | 50.3% | o1 |
| LiveCodeBench (Artificial Analysis run) | 32.6% | 67.9% | o1 |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | 12.9% | o1 |
| WeirdML | 19.0% | 46.1% | o1 |
| LMArena Text: coding | 1306 | 1367 | o1 |
| LMArena Text: software and IT | 1305 | 1366 | o1 |
| SciCode | Not measured | 4.6% | Only o1 measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4.1 Nano | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.8 | 15.2 | o1 |
| Epoch Capabilities Index | 129.5 | 141.7 | o1 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4.1 Nano | o1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 17.3% | 62.6% | o1 |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 33.1% | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index010
| Benchmark | GPT-4.1 Nano | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | 50.8% | 73.2% | o1 |
| MMMU Pro (Vals AI) | 55.0% | 77.4% | o1 |
| GPQA Diamond (Artificial Analysis run) | 51.2% | 74.7% | o1 |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 7.0% | o1 |
| GPQA Diamond (Epoch AI run) | 48.9% | 76.8% | o1 |
| ARC-AGI | 0.0% | 30.7% | o1 |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| SimpleBench | Not measured | 40.1% | Only o1 measured |
| Chess puzzles | 0.0% | 10.0% | o1 |
| VPCT | Not measured | 37.0% | Only o1 measured |
| LMArena Text: hard prompts | 1286 | 1371 | o1 |
| LMArena Text: expert | 1271 | 1361 | o1 |
| LMArena Text: hard prompts in English | 1293 | 1372 | o1 |
| Kagi LLM Benchmark | 33.3% | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GPT-4.1 Nano | o1 | Leader |
|---|---|---|---|
| AIME | 26.5% | 71.5% | o1 |
| MATH 500 (Vals AI) | 80.2% | 90.4% | o1 |
| MGSM (Vals AI) | 69.3% | 89.3% | o1 |
| AA Math Index | 24.0 | Not measured | Only GPT-4.1 Nano measured |
| AIME 2025 (Artificial Analysis run) | 24.0% | Not measured | Only GPT-4.1 Nano measured |
| OTIS Mock AIME | 28.9% | 73.3% | o1 |
| FrontierMath, tiers 1 to 3 | Not measured | 14.7% | Only o1 measured |
| MATH level 5 | 70.0% | 94.7% | o1 |
| LMArena Text: math | 1274 | 1388 | o1 |
| LMArena Text: mathematical work | 1274 | 1390 | o1 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index010
| Benchmark | GPT-4.1 Nano | o1 | Leader |
|---|---|---|---|
| MMLU Pro | 63.5% | 83.5% | o1 |
| LegalBench (Vals AI) | 61.1% | 80.4% | o1 |
| CorpFin (Vals AI) | 42.1% | Not measured | Only GPT-4.1 Nano measured |
| TaxEval (Vals AI) | 60.8% | 74.3% | o1 |
| MortgageTax (Vals AI) | 52.8% | Not measured | Only GPT-4.1 Nano measured |
| MedQA (Vals AI) | 68.2% | 96.5% | o1 |
| MMLU Pro (Artificial Analysis run) | 65.7% | 84.1% | o1 |
| SimpleQA Verified | 6.0% | 41.1% | o1 |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | 1297 | 1369 | o1 |
| LMArena Text: medicine and healthcare | 1276 | 1355 | o1 |
| LMArena Text: legal and government | 1300 | 1368 | o1 |
| LMArena Text: business and finance | 1267 | 1328 | o1 |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4.1 Nano | o1 | Leader |
|---|---|---|---|
| Design Arena: websites | 973 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: UI components | 916 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: game dev | 971 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: data viz | 890 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: 3D | 925 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: all code categories | 963 | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 Nano | 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-4.1 Nano costs less
Repository review
50K input + 3K output tokens
GPT-4.1 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-4.1 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-4.1 Nano | OpenAI o1 |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | April 14, 2025 | December 17, 2024 |
| Context window | 1.05M | 200K |
| Input $/Mtok | $0.10 | $15.00 |
| Output $/Mtok | $0.40 | $60.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4.1 Nano better than o1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4.1 Nano for output tokens 150.0x cheaper ($0.40 vs $60.00 per million) and a larger context window (1.05M against 200K tokens).
Which is cheaper, GPT-4.1 Nano or o1?
GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4.1 Nano or o1?
GPT-4.1 Nano, with 1.05M tokens against 200K.
How many benchmarks is the GPT-4.1 Nano vs o1 comparison based on?
58 benchmarks have a result for both models as of October 21, 2026. GPT-4.1 Nano's coding index rests on 0 independent benchmarks and o1's on 0. Where only one model has a result, the page says so instead of naming a winner.