# GPT-5.1-Codex vs o3

> o3 is the stronger model overall, scoring 47.4 to 38.2 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gpt-5-1-codex-vs-o3
- Last updated: 2026-10-10
- Shared benchmarks: 3

## Summary

- They share 3 benchmarks with published results for both. GPT-5.1-Codex scores higher in 1 category and o3 in 2 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where o3 leads 48.9 to 29.3.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 66% for GPT-5.1-Codex and 58.4% for o3.
- Both cost about the same: $1.25 input and $10 output per million tokens.
- GPT-5.1-Codex accepts more context: 400K tokens versus 200K.

## Snapshot

| | GPT-5.1-Codex | o3 |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 38.2 | 47.4 |
| Rank | 178 | 53 |
| Context | 400K | 200K |
| Input $/M | $1.25 | $2 |
| Output $/M | $10 | $8 |
| Weights | Proprietary | Proprietary |

## Coding

- GPT-5.1-Codex: 41.7 (#107)
- o3: 47.3 (#57)

| Benchmark | GPT-5.1-Codex | o3 |
|---|---|---|
| SWE-bench Verified (bash only) | 66% | 58.4% |
| ALE-Bench | 1,245 | 933.55 |
| SWE-bench Verified | — | 62.3% |
| Aider Polyglot | — | 81.3% |
| LMArena WebDev | 1337 | — |
| GSO | — | 8.8% |
| WeirdML | — | 52.4% |
| LMArena Coding | — | 1408 |
| CadEval | — | 74% |

## Agentic & Tool Use

- GPT-5.1-Codex: 37.8 (#32)
- o3: 34.3 (#42)

| Benchmark | GPT-5.1-Codex | o3 |
|---|---|---|
| METR Time Horizons | 70.8% | 65.4% |
| Terminal-Bench | 60.4% | — |
| Berkeley Function Calling Leaderboard | — | 63% |
| GDPval | — | 30.8% |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| LMArena Search | — | 1144 |

## Reasoning

- GPT-5.1-Codex: —
- o3: 32.1 (#66)

| Benchmark | GPT-5.1-Codex | o3 |
|---|---|---|
| ARC-AGI-2 | — | 6.5% |
| SimpleBench | — | 53.1% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 60.8% |
| CritPt | — | 1.4% |
| Chess Puzzles | — | 38% |
| EnigmaEval | — | 13.1% |
| LMArena Hard Prompts | — | 1402 |
| Mystery Game Puzzles | — | 29% |
| DTBench | — | 84.8% |
| LMCA | — | 39.7% |
| Epoch Capabilities Index | — | 146.88 |
| ForecastBench | — | 62.5 |

## Math

- GPT-5.1-Codex: 29.3 (#218)
- o3: 48.9 (#49)

| Benchmark | GPT-5.1-Codex | o3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 33.3% |
| OTIS Mock AIME 2024-2025 | — | 84.4% |
| ProofBench | 9% | — |
| Omni-MATH | — | 71.4% |
| LMArena Math | — | 1426 |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Tier 4 (v1) | — | 2.1% |

## Knowledge

- GPT-5.1-Codex: —
- o3: 54.5 (#47)

| Benchmark | GPT-5.1-Codex | o3 |
|---|---|---|
| GPQA Diamond | — | 81.8% |
| Humanity's Last Exam | — | 20.3% |
| SimpleQA Verified | — | 49.4% |
| MMLU-Pro | — | 85.9% |
| Confabulations | — | 14.4% |
| GPQA (HELM) | — | 75.3% |
| LMArena Expert | — | 1403 |

## Multimodal

- GPT-5.1-Codex: —
- o3: 40.7 (#35)

| Benchmark | GPT-5.1-Codex | o3 |
|---|---|---|
| LMArena Vision | — | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |

## Multilingual

- GPT-5.1-Codex: —
- o3: 51.9 (#99)

| Benchmark | GPT-5.1-Codex | o3 |
|---|---|---|
| LMArena Non-English | — | 1402 |
| LMArena Chinese | — | 1438 |
| LMArena French | — | 1430 |
| LMArena German | — | 1420 |
| LMArena Japanese | — | 1404 |
| LMArena Korean | — | 1371 |
| LMArena Russian | — | 1407 |
| LMArena Spanish | — | 1395 |

## Instruction Following

- GPT-5.1-Codex: —
- o3: 73.0 (#122)

| Benchmark | GPT-5.1-Codex | o3 |
|---|---|---|
| IFEval | — | 86.9% |
| LMArena Instruction Following | — | 1368 |

## Long Context

- GPT-5.1-Codex: —
- o3: 53.2 (#6)

| Benchmark | GPT-5.1-Codex | o3 |
|---|---|---|
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |
| LMArena Longer Query | — | 1372 |

## Writing & Preference

- GPT-5.1-Codex: —
- o3: 63.7 (#62)

| Benchmark | GPT-5.1-Codex | o3 |
|---|---|---|
| LMArena Text | — | 1410 |
| LMArena Creative Writing | — | 1359 |
| Short-Story Creative Writing | — | 83.9% |
| EQ-Bench Creative Writing | — | 1676 |
| WildBench | — | 86.1% |
| LMArena Multi-Turn | — | 1405 |

## FAQ

### Is GPT-5.1-Codex better than o3?

o3 is the stronger model overall, scoring 47.4 to 38.2 on the Noometry Index.

### Which is cheaper, GPT-5.1-Codex or o3?

GPT-5.1-Codex is cheaper. It lists at $1.25 per million input tokens and $10 per million output tokens; o3 lists at $2 and $8.

### Is GPT-5.1-Codex or o3 better for coding?

o3 scores higher on coding benchmarks: 47.3 versus 41.7 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.1-Codex does, with 400K tokens against 200K.

### How many benchmarks do GPT-5.1-Codex and o3 share?

3 benchmarks have published results for both models. GPT-5.1-Codex has 6 scored results on Noometry and o3 has 63.