Model comparison
GPT-3.5-turbo vs GPT-5
GPT-5 is the stronger model overall, scoring 50.9 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 4.6× less per token, which makes it the better buy when GPT-5'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 GPT-5 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 leads 55.0 to 6.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 2.2% for GPT-3.5-turbo and 91.4% for GPT-5.
- GPT-3.5-turbo is cheaper at $0.50 / $1.50 per million input/output tokens, against $1.25 / $10 for GPT-5.
- GPT-5 accepts more context: 400K tokens versus 16K.
Side by side
| GPT-3.5-turbo | GPT-5 | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 23.2 | 50.9 |
| Released | 2023-03-01 | 2025-08-07 |
| Weights | Proprietary | Proprietary |
| Context window | 16K | 400K |
| Max output | 4K | 128K |
| Input $ / M tokens | $0.50 | $1.25 |
| Output $ / M tokens | $1.50 | $10 |
| Results tracked | 44 | 69 |
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Category by category
Coding GPT-5 leads
GPT-3.5-turbo: 23.9 (#331), GPT-5: 50.3 (#47)
| Benchmark | GPT-3.5-turbo | GPT-5 |
|---|---|---|
| WeirdML | 3.5% | 60.7% |
| LMArena Coding | 1136 | 1436 |
| SWE-bench Verified | — | 73.6% |
| SWE-bench Verified (bash only) | — | 65% |
| Aider Polyglot | — | 88% |
| LMArena WebDev | — | 1418 |
| SciCode | — | 42.9% |
| GSO | — | 6.9% |
| BigCodeBench Instruct | 39.1% | — |
| BigCodeBench Complete | 50.6% | — |
| ALE-Bench | — | 1,162 |
| AlgoTune | — | 1.67 |
| HumanEval+ | 70.7% | — |
| MBPP+ | 69.7% | — |
Agentic & Tool Use Not comparable
GPT-3.5-turbo: —, GPT-5: 33.1 (#56)
| Benchmark | GPT-3.5-turbo | GPT-5 |
|---|---|---|
| METR Time Horizons | 21.5% | 69.6% |
| Terminal-Bench | — | 49.6% |
| GDPval | — | 34.8% |
| Remote Labor Index | — | 1.7% |
| DeepResearch Bench | — | 49.6% |
| BALROG | — | 32.8% |
| LMArena Search | — | 1133 |
Reasoning GPT-5 leads
GPT-3.5-turbo: 13.8 (#332), GPT-5: 38.3 (#64)
| Benchmark | GPT-3.5-turbo | GPT-5 |
|---|---|---|
| Chess Puzzles | 0% | 37% |
| LMArena Hard Prompts | 1108 | 1416 |
| Mystery Game Puzzles | 3% | 23% |
| DTBench | 48.5% | 90.7% |
| LMCA | 9.7% | 40% |
| Epoch Capabilities Index | 118.55 | 150 |
| ForecastBench | 50.4 | 61.4 |
| ARC-AGI-2 | — | 9.9% |
| SimpleBench | — | 56.7% |
| Kagi LLM Benchmark | — | 72.7% |
| ARC-AGI-1 | — | 65.7% |
| CritPt | — | 12.6% |
| EnigmaEval | — | 10.5% |
| EBR-Bench | — | 12.7% |
| Adversarial NLI | 58.1% | — |
| BIG-Bench Hard | 61.6% | — |
| CommonsenseQA 2.0 | 57% | — |
| WinoGrande | 81.6% | — |
Math GPT-5 leads
GPT-3.5-turbo: 6.3 (#327), GPT-5: 55.0 (#44)
| Benchmark | GPT-3.5-turbo | GPT-5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0% | 55.4% |
| OTIS Mock AIME 2024-2025 | 2.2% | 91.4% |
| LMArena Math | 1142 | 1407 |
| MATH Level 5 | 15.9% | 98.1% |
| FrontierMath Tier 4 | — | 22% |
| ProofBench | — | 18% |
| Omni-MATH | — | 64.7% |
| FrontierMath (Feb 2025 set) | — | 32.4% |
| FrontierMath Tier 4 (v1) | — | 12.5% |
| GSM8K | 57.8% | — |
Knowledge GPT-5 leads
GPT-3.5-turbo: 10.0 (#303), GPT-5: 56.6 (#43)
| Benchmark | GPT-3.5-turbo | GPT-5 |
|---|---|---|
| GPQA Diamond | 28% | 86.2% |
| LMArena Expert | 1070 | 1419 |
| Humanity's Last Exam | — | 25.3% |
| SimpleQA Verified | — | 50.1% |
| MMLU-Pro | — | 86.3% |
| Confabulations | — | 10.3% |
| Vectara Hallucination Rate | — | 14.7% |
| GPQA (HELM) | — | 79.2% |
| ARC (AI2) Challenge | 87.4% | — |
| BoolQ | 87% | — |
| MMLU | 71.4% | — |
| OpenBookQA | 86% | — |
| TriviaQA | 85.8% | — |
Multimodal Not comparable
GPT-3.5-turbo: —, GPT-5: 46.8 (#13)
| Benchmark | GPT-3.5-turbo | GPT-5 |
|---|---|---|
| LMArena Vision | — | 1232 |
| GeoBench | — | 81% |
| VPCT | — | 66% |
Multilingual GPT-5 leads
GPT-3.5-turbo: 31.5 (#258), GPT-5: 51.4 (#110)
| Benchmark | GPT-3.5-turbo | GPT-5 |
|---|---|---|
| LMArena Non-English | 1108 | 1397 |
| LMArena Chinese | 1075 | 1422 |
| LMArena French | 1118 | 1410 |
| LMArena German | 1090 | 1416 |
| LMArena Japanese | 1043 | 1409 |
| LMArena Korean | 1019 | 1360 |
| LMArena Russian | 1123 | 1406 |
| LMArena Spanish | 1121 | 1399 |
Instruction Following GPT-5 leads
GPT-3.5-turbo: 57.9 (#262), GPT-5: 73.8 (#113)
| Benchmark | GPT-3.5-turbo | GPT-5 |
|---|---|---|
| LMArena Instruction Following | 1119 | 1388 |
| IFEval | — | 87.5% |
Long Context GPT-5 leads
GPT-3.5-turbo: 34.0 (#254), GPT-5: 69.5 (#2)
| Benchmark | GPT-3.5-turbo | GPT-5 |
|---|---|---|
| LMArena Longer Query | 1121 | 1399 |
| Fiction.LiveBench | — | 97.2% |
Writing & Preference GPT-5 leads
GPT-3.5-turbo: 25.3 (#305), GPT-5: 63.4 (#65)
| Benchmark | GPT-3.5-turbo | GPT-5 |
|---|---|---|
| LMArena Text | 1125 | 1406 |
| LMArena Creative Writing | 1092 | 1365 |
| EQ-Bench Creative Writing | 451 | 1627 |
| LMArena Multi-Turn | 1117 | 1426 |
| Short-Story Creative Writing | — | 86% |
| WildBench | — | 85.7% |
Frequently asked questions
Is GPT-3.5-turbo better than GPT-5?
GPT-5 is the stronger model overall, scoring 50.9 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 4.6× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Which is cheaper, GPT-3.5-turbo or GPT-5?
GPT-3.5-turbo is cheaper. It lists at $0.50 per million input tokens and $1.50 per million output tokens; GPT-5 lists at $1.25 and $10.
Is GPT-3.5-turbo or GPT-5 better for coding?
GPT-5 scores higher on coding benchmarks: 50.3 versus 23.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5 does, with 400K tokens against 16K.
How many benchmarks do GPT-3.5-turbo and GPT-5 share?
30 benchmarks have published results for both models. GPT-3.5-turbo has 44 scored results on Noometry and GPT-5 has 69.