Model comparison
GPT-5-Codex vs o3-mini
GPT-5-Codex is the stronger model overall, scoring 37.9 to 36.7 on the Noometry Index. o3-mini costs 1.8× less per token, which makes it the better buy when GPT-5-Codex's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
Summary
- They share 1 benchmark with published results for both. GPT-5-Codex scores higher in 3 categories and o3-mini in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5-Codex leads 30.9 to 16.3.
- The biggest single-benchmark swing is WeirdML: 54.5% for GPT-5-Codex and 43.7% for o3-mini.
- o3-mini is cheaper at $1.10 / $4.40 per million input/output tokens, against $1.25 / $10 for GPT-5-Codex.
- GPT-5-Codex accepts more context: 400K tokens versus 200K.
Side by side
| GPT-5-Codex | o3-mini | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 37.9 | 36.7 |
| Released | 2025-09-15 | 2024-12-20 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 200K |
| Max output | 128K | 100K |
| Input $ / M tokens | $1.25 | $1.10 |
| Output $ / M tokens | $10 | $4.40 |
| Results tracked | 3 | 51 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5-Codex leads
GPT-5-Codex: 42.4 (#103), o3-mini: 40.8 (#132)
| Benchmark | GPT-5-Codex | o3-mini |
|---|---|---|
| WeirdML | 54.5% | 43.7% |
| Aider Polyglot | — | 60.4% |
| SciCode | — | 39.8% |
| GSO | — | 1.3% |
| LiveBench Coding | — | 82.7% |
| LMArena Coding | — | 1378 |
| CadEval | — | 54% |
Agentic & Tool Use GPT-5-Codex leads
GPT-5-Codex: 31.0 (#72), o3-mini: 29.6 (#84)
| Benchmark | GPT-5-Codex | o3-mini |
|---|---|---|
| Terminal-Bench | 44.3% | — |
| Cybench | — | 22.5% |
Reasoning GPT-5-Codex leads
GPT-5-Codex: 30.9 (#83), o3-mini: 16.3 (#305)
| Benchmark | GPT-5-Codex | o3-mini |
|---|---|---|
| ARC-AGI-2 | — | 3% |
| SimpleBench | — | 22.8% |
| Kagi LLM Benchmark | 70.3% | — |
| ARC-AGI-1 | — | 34.5% |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 17% |
| LiveBench Reasoning | — | 89.6% |
| LMArena Hard Prompts | — | 1366 |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 68.8% |
| LiveBench Data Analysis | — | 70.6% |
| LMCA | — | 19% |
| Epoch Capabilities Index | — | 140.34 |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math Not comparable
GPT-5-Codex: —, o3-mini: 28.1 (#244)
| Benchmark | GPT-5-Codex | o3-mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| OTIS Mock AIME 2024-2025 | — | 76.9% |
| LiveBench Math | — | 77.3% |
| LMArena Math | — | 1396 |
| MATH Level 5 | — | 96.5% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge Not comparable
GPT-5-Codex: —, o3-mini: 38.3 (#146)
| Benchmark | GPT-5-Codex | o3-mini |
|---|---|---|
| GPQA Diamond | — | 77% |
| SimpleQA Verified | — | 15.3% |
| Confabulations | — | 17.9% |
| LMArena Expert | — | 1364 |
Multilingual Not comparable
GPT-5-Codex: —, o3-mini: 45.7 (#164)
| Benchmark | GPT-5-Codex | o3-mini |
|---|---|---|
| LMArena Non-English | — | 1319 |
| LMArena Chinese | — | 1379 |
| LMArena French | — | 1334 |
| LMArena German | — | 1303 |
| LMArena Japanese | — | 1286 |
| LMArena Korean | — | 1314 |
| LMArena Russian | — | 1304 |
| LMArena Spanish | — | 1321 |
Instruction Following Not comparable
GPT-5-Codex: —, o3-mini: 75.1 (#72)
| Benchmark | GPT-5-Codex | o3-mini |
|---|---|---|
| LiveBench Instruction Following | — | 84.4% |
| LMArena Instruction Following | — | 1337 |
Long Context Not comparable
GPT-5-Codex: —, o3-mini: 33.8 (#256)
| Benchmark | GPT-5-Codex | o3-mini |
|---|---|---|
| Fiction.LiveBench | — | 50% |
| LMArena Longer Query | — | 1343 |
Writing & Preference Not comparable
GPT-5-Codex: —, o3-mini: 50.3 (#182)
| Benchmark | GPT-5-Codex | o3-mini |
|---|---|---|
| LMArena Text | — | 1337 |
| LMArena Creative Writing | — | 1286 |
| Short-Story Creative Writing | — | 61.7% |
| LMArena Multi-Turn | — | 1320 |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is GPT-5-Codex better than o3-mini?
GPT-5-Codex is the stronger model overall, scoring 37.9 to 36.7 on the Noometry Index. o3-mini costs 1.8× less per token, which makes it the better buy when GPT-5-Codex's lead doesn't matter for your workload.
Which is cheaper, GPT-5-Codex or o3-mini?
o3-mini is cheaper. It lists at $1.10 per million input tokens and $4.40 per million output tokens; GPT-5-Codex lists at $1.25 and $10.
Is GPT-5-Codex or o3-mini better for coding?
GPT-5-Codex scores higher on coding benchmarks: 42.4 versus 40.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5-Codex does, with 400K tokens against 200K.
How many benchmarks do GPT-5-Codex and o3-mini share?
1 benchmark has published results for both models. GPT-5-Codex has 3 scored results on Noometry and o3-mini has 51.