Model comparison
GPT-3.5-turbo vs o3
o3 is the stronger model overall, scoring 47.5 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 4.7× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Last verified . 30 shared benchmarks.
Summary
- They share 30 benchmarks with published results for both. GPT-3.5-turbo scores higher in 0 categories and o3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o3 leads 54.6 to 10.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 2.2% for GPT-3.5-turbo and 84.4% for o3.
- GPT-3.5-turbo is cheaper at $0.50 / $1.50 per million input/output tokens, against $2 / $8 for o3.
- o3 accepts more context: 200K tokens versus 16K.
Side by side
| GPT-3.5-turbo | o3 | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 23.2 | 47.5 |
| Released | 2023-03-01 | 2025-04-16 |
| Weights | Proprietary | Proprietary |
| Context window | 16K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.50 | $2 |
| Output $ / M tokens | $1.50 | $8 |
| Results tracked | 44 | 63 |
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Category by category
Coding o3 leads
GPT-3.5-turbo: 23.9 (#331), o3: 46.8 (#64)
| Benchmark | GPT-3.5-turbo | o3 |
|---|---|---|
| WeirdML | 3.5% | 52.4% |
| LMArena Coding | 1136 | 1408 |
| SWE-bench Verified | — | 62.3% |
| SWE-bench Verified (bash only) | — | 58.4% |
| Aider Polyglot | — | 81.3% |
| GSO | — | 8.8% |
| BigCodeBench Instruct | 39.1% | — |
| BigCodeBench Complete | 50.6% | — |
| CadEval | — | 74% |
| ALE-Bench | — | 933.55 |
| HumanEval+ | 70.7% | — |
| MBPP+ | 69.7% | — |
Agentic & Tool Use Not comparable
GPT-3.5-turbo: —, o3: 34.5 (#44)
| Benchmark | GPT-3.5-turbo | o3 |
|---|---|---|
| METR Time Horizons | 21.5% | 65.4% |
| Berkeley Function Calling Leaderboard | — | 63% |
| GDPval | — | 30.8% |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| LMArena Search | — | 1144 |
Reasoning o3 leads
GPT-3.5-turbo: 13.8 (#332), o3: 32.0 (#78)
| Benchmark | GPT-3.5-turbo | o3 |
|---|---|---|
| Chess Puzzles | 0% | 38% |
| LMArena Hard Prompts | 1108 | 1402 |
| Mystery Game Puzzles | 3% | 29% |
| DTBench | 48.5% | 84.8% |
| LMCA | 9.7% | 39.7% |
| Epoch Capabilities Index | 118.55 | 146.86 |
| ForecastBench | 50.4 | 62.5 |
| ARC-AGI-2 | — | 6.5% |
| SimpleBench | — | 53.1% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 60.8% |
| CritPt | — | 1.4% |
| EnigmaEval | — | 13.1% |
| Adversarial NLI | 58.1% | — |
| BIG-Bench Hard | 61.6% | — |
| CommonsenseQA 2.0 | 57% | — |
| WinoGrande | 81.6% | — |
Math o3 leads
GPT-3.5-turbo: 6.3 (#327), o3: 50.2 (#58)
| Benchmark | GPT-3.5-turbo | o3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0% | 33.3% |
| OTIS Mock AIME 2024-2025 | 2.2% | 84.4% |
| LMArena Math | 1142 | 1426 |
| MATH Level 5 | 15.9% | 97.8% |
| Omni-MATH | — | 71.4% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
| GSM8K | 57.8% | — |
Knowledge o3 leads
GPT-3.5-turbo: 10.0 (#303), o3: 54.6 (#52)
| Benchmark | GPT-3.5-turbo | o3 |
|---|---|---|
| GPQA Diamond | 28% | 81.8% |
| LMArena Expert | 1070 | 1402 |
| Humanity's Last Exam | — | 20.3% |
| SimpleQA Verified | — | 49.4% |
| MMLU-Pro | — | 85.9% |
| Confabulations | — | 14.4% |
| GPQA (HELM) | — | 75.3% |
| ARC (AI2) Challenge | 87.4% | — |
| BoolQ | 87% | — |
| MMLU | 71.4% | — |
| OpenBookQA | 86% | — |
| TriviaQA | 85.8% | — |
Multimodal Not comparable
GPT-3.5-turbo: —, o3: 41.4 (#36)
| Benchmark | GPT-3.5-turbo | o3 |
|---|---|---|
| LMArena Vision | — | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |
Multilingual o3 leads
GPT-3.5-turbo: 31.5 (#258), o3: 51.7 (#105)
| Benchmark | GPT-3.5-turbo | o3 |
|---|---|---|
| LMArena Non-English | 1108 | 1401 |
| LMArena Chinese | 1075 | 1437 |
| LMArena French | 1118 | 1430 |
| LMArena German | 1090 | 1420 |
| LMArena Japanese | 1043 | 1403 |
| LMArena Korean | 1019 | 1370 |
| LMArena Russian | 1123 | 1406 |
| LMArena Spanish | 1121 | 1395 |
Instruction Following o3 leads
GPT-3.5-turbo: 57.9 (#262), o3: 72.8 (#127)
| Benchmark | GPT-3.5-turbo | o3 |
|---|---|---|
| LMArena Instruction Following | 1119 | 1368 |
| IFEval | — | 86.9% |
Long Context o3 leads
GPT-3.5-turbo: 34.0 (#254), o3: 53.3 (#6)
| Benchmark | GPT-3.5-turbo | o3 |
|---|---|---|
| LMArena Longer Query | 1121 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |
Writing & Preference o3 leads
GPT-3.5-turbo: 25.3 (#305), o3: 63.5 (#64)
| Benchmark | GPT-3.5-turbo | o3 |
|---|---|---|
| LMArena Text | 1125 | 1410 |
| LMArena Creative Writing | 1092 | 1359 |
| EQ-Bench Creative Writing | 451 | 1676 |
| LMArena Multi-Turn | 1117 | 1405 |
| Short-Story Creative Writing | — | 83.9% |
| WildBench | — | 86.1% |
Frequently asked questions
Is GPT-3.5-turbo better than o3?
o3 is the stronger model overall, scoring 47.5 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 4.7× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Which is cheaper, GPT-3.5-turbo or o3?
GPT-3.5-turbo is cheaper. It lists at $0.50 per million input tokens and $1.50 per million output tokens; o3 lists at $2 and $8.
Is GPT-3.5-turbo or o3 better for coding?
o3 scores higher on coding benchmarks: 46.8 versus 23.9 in the Noometry coding category.
Which has the bigger context window?
o3 does, with 200K tokens against 16K.
How many benchmarks do GPT-3.5-turbo and o3 share?
30 benchmarks have published results for both models. GPT-3.5-turbo has 44 scored results on Noometry and o3 has 63.