84.1%
MMLU-Pro (AA run)
Modelle / OpenAI
OpenAI o · Release 2024-09-12
OpenAIs Reasoning-Modell September 2024. AIME 2024 83,3 % mit cons@64-Sampling. Vorgänger von o3 und GPT-5-Reasoning-Tiers.
Legacy-Reasoning-Tier — für neue Mathe- und Code-Arbeit o3 oder GPT-5.5 bevorzugen.
74average across 6 tested families
Wo OpenAI o1 auf jeder öffentlichen Benchmark-Quelle mit Zeile steht. Rang zählt jedes Modell mit neuester Zeile im selben Test — kein universeller Qualitätsscore.
| Benchmark | Family | Rang | Score | Quelle | Datum |
|---|---|---|---|---|---|
| MATH | Math | #2/ 20 | 97 | Artificial Analysis | 2026-07-21 |
| LiveCodeBench | Coding | #8/ 13 | 68 | Artificial Analysis | 2026-07-21 |
| MMLU-Pro | Knowledge | #8/ 21 | 84 | Artificial Analysis | 2026-07-21 |
| AIME | Math | #12/ 19 | 83 | OpenAI: Learning to Reason | 2024-09-12 |
| τ³-Bench Banking | Agentic | #14/ 64 | 63 | Artificial Analysis | 2026-07-21 |
| IFBench | Reasoning | #34/ 57 | 70 | Artificial Analysis | 2026-07-21 |
| AA-LCR | Long context | #46/ 64 | 59 | Artificial Analysis | 2026-07-21 |
| Artificial Analysis Intelligence Index | Reasoning | #53/ 67 | 23 | Artificial Analysis | 2026-07-21 |
| SciCode | Coding | #56/ 65 | 36 | Artificial Analysis | 2026-07-21 |
| Humanity's Last Exam | Reasoning | #57/ 65 | 8 | Artificial Analysis | 2026-07-21 |
| GPQA Diamond | Reasoning | #58/ 69 | 75 | Artificial Analysis | 2026-07-21 |
| Terminal-Bench | Agentic | #63/ 69 | 13 | Artificial Analysis | 2026-07-21 |
| AA output speed | Performance | #69/ 69 | Artificial Analysis | 2026-07-21 | |
| AA time to first token | Performance | #69/ 69 | Artificial Analysis | 2026-07-21 |
Die drei höchstscorierenden kartierten Modelle pro Capability-Family. Wo OpenAI o1 auftaucht, ist es hervorgehoben.
Benchmark-Zeilen im öffentlichen Ledger für OpenAI o1 in den letzten 120 Tagen. Ältere Zeilen stehen in der vollen Tabelle unten.
| Benchmark | Family | Score | Quelle | Tage her |
|---|---|---|---|---|
| AA-LCR | Long context | 59.33% | Artificial Analysis | 2 |
| AA output speed | Performance | 0 t/s | Artificial Analysis | 2 |
| AA time to first token | Performance | 0.00s | Artificial Analysis | 2 |
| Artificial Analysis Intelligence Index | Reasoning | 23.4% | Artificial Analysis | 2 |
| GPQA Diamond | Reasoning | 74.7% | Artificial Analysis | 2 |
| Humanity's Last Exam | Reasoning | 7.7% | Artificial Analysis | 2 |
| IFBench | Reasoning | 70.34% | Artificial Analysis | 2 |
| LiveCodeBench | Coding | 67.9% | Artificial Analysis | 2 |
| MATH | Math | 97% | Artificial Analysis | 2 |
| MMLU-Pro | Knowledge | 84.1% | Artificial Analysis | 2 |
| SciCode | Coding | 35.8% | Artificial Analysis | 2 |
| τ³-Bench Banking | Agentic | 62.57% | Artificial Analysis | 2 |
| Terminal-Bench | Agentic | 12.88% | Artificial Analysis | 2 |
Ein hoher Composite, der eine schwache Family versteckt, ist eine Falle. Diese Balken zeigen Families ohne öffentlichen Test — und wo das Modell führt.
Hover or tab any dot for name, score, and input price.
Jeder Katalog-Benchmark für OpenAI o1. Scores verlinken zur Originalquelle; Lücken heißen: noch keine öffentliche Zeile.
14 of 32 catalog benchmarks have a sourced row for OpenAI o1.
84.1%
MMLU-Pro (AA run)
23.4%
AA Intelligence Index v4.1
74.7%
GPQA Diamond (AA run)
7.7%
HLE (AA run)
70.34%
IFBench (AA run)
67.9%
LiveCodeBench (AA run)
35.8%
SciCode (AA run)
12.88%
Terminal-Bench Hard (AA run)
62.57%
τ²-Bench Banking (AA run; maps to τ³ explainer)
59.33%
AA-LCR (AA run)
0 t/s
Median output tokens/s (1k prompt, default provider)
0.00s
Median time to first token (1k prompt, default provider)