Model comparison
GPT-3.5-turbo vs GPT-6 Sol
GPT-6 Sol is the stronger model overall, scoring 61.8 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 5.3× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
Summary
- They share 25 benchmarks with published results for both. GPT-3.5-turbo scores higher in 0 categories and GPT-6 Sol in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 6.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 2.2% for GPT-3.5-turbo and 100% for GPT-6 Sol.
- GPT-3.5-turbo is cheaper at $0.50 / $1.50 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 16K.
Side by side
| GPT-3.5-turbo | GPT-6 Sol | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 23.2 | 61.8 |
| Released | 2023-03-01 | 2026-09-22 |
| Weights | Proprietary | Proprietary |
| Context window | 16K | 1.05M |
| Max output | 4K | 128K |
| Input $ / M tokens | $0.50 | $2 |
| Output $ / M tokens | $1.50 | $10 |
| Results tracked | 44 | 45 |
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Category by category
Coding GPT-6 Sol leads
GPT-3.5-turbo: 23.9 (#331), GPT-6 Sol: 60.1 (#11)
| Benchmark | GPT-3.5-turbo | GPT-6 Sol |
|---|---|---|
| LMArena Coding | 1136 | 1447 |
| DeepSWE | — | 68.8% |
| FrontierCode | — | 49.3% |
| LMArena WebDev | — | 1688 |
| SciCode | — | 57.6% |
| WeirdML | 3.5% | — |
| BigCodeBench Instruct | 39.1% | — |
| BigCodeBench Complete | 50.6% | — |
| ALE-Bench | — | 2,462 |
| HumanEval+ | 70.7% | — |
| MBPP+ | 69.7% | — |
Agentic & Tool Use Not comparable
GPT-3.5-turbo: —, GPT-6 Sol: 37.2 (#36)
| Benchmark | GPT-3.5-turbo | GPT-6 Sol |
|---|---|---|
| APEX-Agents | — | 54.3% |
| GDP.pdf | — | 26.4% |
| METR Time Horizons | 21.5% | — |
| Vending-Bench 2 | — | 14,428 |
Reasoning GPT-6 Sol leads
GPT-3.5-turbo: 13.8 (#332), GPT-6 Sol: 74.0 (#9)
| Benchmark | GPT-3.5-turbo | GPT-6 Sol |
|---|---|---|
| LMArena Hard Prompts | 1108 | 1418 |
| Mystery Game Puzzles | 3% | 56% |
| DTBench | 48.5% | 97.3% |
| LMCA | 9.7% | 59.1% |
| Epoch Capabilities Index | 118.55 | 162.72 |
| ARC-AGI-2 | — | 89.6% |
| NYT Connections (extended) | — | 90.1% |
| ARC-AGI-1 | — | 95.5% |
| CritPt | — | 30.9% |
| Chess Puzzles | 0% | — |
| EBR-Bench | — | 53.3% |
| Adversarial NLI | 58.1% | — |
| BIG-Bench Hard | 61.6% | — |
| CommonsenseQA 2.0 | 57% | — |
| ForecastBench | 50.4 | — |
| WinoGrande | 81.6% | — |
Math GPT-6 Sol leads
GPT-3.5-turbo: 6.3 (#327), GPT-6 Sol: 87.2 (#7)
| Benchmark | GPT-3.5-turbo | GPT-6 Sol |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0% | 89.8% |
| OTIS Mock AIME 2024-2025 | 2.2% | 100% |
| LMArena Math | 1142 | 1402 |
| FrontierMath Tier 4 | — | 90% |
| ProofBench | — | 83% |
| MATH Level 5 | 15.9% | — |
| GSM8K | 57.8% | — |
Knowledge GPT-6 Sol leads
GPT-3.5-turbo: 10.0 (#303), GPT-6 Sol: 64.8 (#15)
| Benchmark | GPT-3.5-turbo | GPT-6 Sol |
|---|---|---|
| GPQA Diamond | 28% | 94.3% |
| LMArena Expert | 1070 | 1439 |
| SimpleQA Verified | — | 60.7% |
| Vectara Hallucination Rate | — | 6.5% |
| ARC (AI2) Challenge | 87.4% | — |
| BoolQ | 87% | — |
| MMLU | 71.4% | — |
| OpenBookQA | 86% | — |
| TriviaQA | 85.8% | — |
Multimodal Not comparable
GPT-3.5-turbo: —, GPT-6 Sol: 47.6 (#10)
| Benchmark | GPT-3.5-turbo | GPT-6 Sol |
|---|---|---|
| LMArena Vision | — | 1245 |
| Blueprint-Bench 2 | — | 36.9% |
| Furniture Assembly | — | 58.3% |
Multilingual GPT-6 Sol leads
GPT-3.5-turbo: 31.5 (#258), GPT-6 Sol: 50.5 (#118)
| Benchmark | GPT-3.5-turbo | GPT-6 Sol |
|---|---|---|
| LMArena Non-English | 1108 | 1385 |
| LMArena Chinese | 1075 | 1405 |
| LMArena French | 1118 | 1410 |
| LMArena German | 1090 | 1390 |
| LMArena Japanese | 1043 | 1385 |
| LMArena Korean | 1019 | 1341 |
| LMArena Russian | 1123 | 1401 |
| LMArena Spanish | 1121 | 1384 |
Instruction Following GPT-6 Sol leads
GPT-3.5-turbo: 57.9 (#262), GPT-6 Sol: 74.5 (#94)
| Benchmark | GPT-3.5-turbo | GPT-6 Sol |
|---|---|---|
| LMArena Instruction Following | 1119 | 1412 |
Long Context GPT-6 Sol leads
GPT-3.5-turbo: 34.0 (#254), GPT-6 Sol: 43.1 (#108)
| Benchmark | GPT-3.5-turbo | GPT-6 Sol |
|---|---|---|
| LMArena Longer Query | 1121 | 1411 |
Writing & Preference GPT-6 Sol leads
GPT-3.5-turbo: 25.3 (#305), GPT-6 Sol: 71.9 (#18)
| Benchmark | GPT-3.5-turbo | GPT-6 Sol |
|---|---|---|
| LMArena Text | 1125 | 1395 |
| LMArena Creative Writing | 1092 | 1378 |
| EQ-Bench Creative Writing | 451 | 2125 |
| LMArena Multi-Turn | 1117 | 1412 |
Frequently asked questions
Is GPT-3.5-turbo better than GPT-6 Sol?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 5.3× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-3.5-turbo or GPT-6 Sol?
GPT-3.5-turbo is cheaper. It lists at $0.50 per million input tokens and $1.50 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-3.5-turbo or GPT-6 Sol better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 23.9 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 16K.
How many benchmarks do GPT-3.5-turbo and GPT-6 Sol share?
25 benchmarks have published results for both models. GPT-3.5-turbo has 44 scored results on Noometry and GPT-6 Sol has 45.