Model comparison
GPT-3.5-turbo vs Grok 4.5
Grok 4.5 is the stronger model overall, scoring 55.0 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 4.0× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
Summary
- They share 26 benchmarks with published results for both. GPT-3.5-turbo scores higher in 0 categories and Grok 4.5 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Grok 4.5 leads 60.9 to 6.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 2.2% for GPT-3.5-turbo and 97.8% for Grok 4.5.
- GPT-3.5-turbo is cheaper at $0.50 / $1.50 per million input/output tokens, against $2 / $6 for Grok 4.5.
- Grok 4.5 accepts more context: 500K tokens versus 16K.
Side by side
| GPT-3.5-turbo | Grok 4.5 | |
|---|---|---|
| Provider | OpenAI | xAI |
| Noometry Index | 23.2 | 55.0 |
| Released | 2023-03-01 | 2026-07-08 |
| Weights | Proprietary | Proprietary |
| Context window | 16K | 500K |
| Max output | 4K | 500K |
| Input $ / M tokens | $0.50 | $2 |
| Output $ / M tokens | $1.50 | $6 |
| Results tracked | 44 | 52 |
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Category by category
Coding Grok 4.5 leads
GPT-3.5-turbo: 23.9 (#331), Grok 4.5: 52.2 (#35)
| Benchmark | GPT-3.5-turbo | Grok 4.5 |
|---|---|---|
| WeirdML | 3.5% | 46.4% |
| LMArena Coding | 1136 | 1474 |
| DeepSWE | — | 53.8% |
| FrontierCode | — | 42.4% |
| LMArena WebDev | — | 1553 |
| SciCode | — | 54.1% |
| BigCodeBench Instruct | 39.1% | — |
| BigCodeBench Complete | 50.6% | — |
| ALE-Bench | — | 1,309 |
| HumanEval+ | 70.7% | — |
| MBPP+ | 69.7% | — |
Agentic & Tool Use Not comparable
GPT-3.5-turbo: —, Grok 4.5: 44.4 (#17)
| Benchmark | GPT-3.5-turbo | Grok 4.5 |
|---|---|---|
| APEX-Agents | — | 56.2% |
| τ²-bench Banking | — | 47.9% |
| PostTrainBench | — | 23.4% |
| GBAEval | — | 65.4% |
| GDP.pdf | — | 14% |
| LMArena Search | — | 1213 |
| METR Time Horizons | 21.5% | — |
| Vending-Bench 2 | — | 3,887 |
Reasoning Grok 4.5 leads
GPT-3.5-turbo: 13.8 (#332), Grok 4.5: 56.1 (#25)
| Benchmark | GPT-3.5-turbo | Grok 4.5 |
|---|---|---|
| Chess Puzzles | 0% | 36% |
| LMArena Hard Prompts | 1108 | 1462 |
| DTBench | 48.5% | 96.5% |
| LMCA | 9.7% | 45.2% |
| Epoch Capabilities Index | 118.55 | 153.92 |
| ARC-AGI-2 | — | 52.6% |
| SimpleBench | — | 70% |
| Kagi LLM Benchmark | — | 83.5% |
| NYT Connections (extended) | — | 79.9% |
| ARC-AGI-1 | — | 87.2% |
| CritPt | — | 15.4% |
| Mystery Game Puzzles | 3% | — |
| Surface Evolver Bench | — | 74.4% |
| Adversarial NLI | 58.1% | — |
| BIG-Bench Hard | 61.6% | — |
| CommonsenseQA 2.0 | 57% | — |
| ForecastBench | 50.4 | — |
| WinoGrande | 81.6% | — |
Math Grok 4.5 leads
GPT-3.5-turbo: 6.3 (#327), Grok 4.5: 60.9 (#35)
| Benchmark | GPT-3.5-turbo | Grok 4.5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0% | 57.2% |
| OTIS Mock AIME 2024-2025 | 2.2% | 97.8% |
| LMArena Math | 1142 | 1459 |
| FrontierMath Tier 4 | — | 24.4% |
| ProofBench | — | 31% |
| MATH Level 5 | 15.9% | — |
| GSM8K | 57.8% | — |
Knowledge Grok 4.5 leads
GPT-3.5-turbo: 10.0 (#303), Grok 4.5: 62.3 (#24)
| Benchmark | GPT-3.5-turbo | Grok 4.5 |
|---|---|---|
| GPQA Diamond | 28% | 93.4% |
| LMArena Expert | 1070 | 1466 |
| SimpleQA Verified | — | 48.3% |
| ARC (AI2) Challenge | 87.4% | — |
| BoolQ | 87% | — |
| MMLU | 71.4% | — |
| OpenBookQA | 86% | — |
| TriviaQA | 85.8% | — |
Multimodal Not comparable
GPT-3.5-turbo: —, Grok 4.5: 37.6 (#72)
| Benchmark | GPT-3.5-turbo | Grok 4.5 |
|---|---|---|
| LMArena Vision | — | 1288 |
| Blueprint-Bench 2 | — | 27.3% |
| Furniture Assembly | — | 22.5% |
| LMArena Document | — | 1452 |
Multilingual Grok 4.5 leads
GPT-3.5-turbo: 31.5 (#258), Grok 4.5: 54.4 (#42)
| Benchmark | GPT-3.5-turbo | Grok 4.5 |
|---|---|---|
| LMArena Non-English | 1108 | 1440 |
| LMArena Chinese | 1075 | 1496 |
| LMArena French | 1118 | 1456 |
| LMArena German | 1090 | 1446 |
| LMArena Japanese | 1043 | 1428 |
| LMArena Korean | 1019 | 1404 |
| LMArena Russian | 1123 | 1448 |
| LMArena Spanish | 1121 | 1450 |
Instruction Following Grok 4.5 leads
GPT-3.5-turbo: 57.9 (#262), Grok 4.5: 76.0 (#48)
| Benchmark | GPT-3.5-turbo | Grok 4.5 |
|---|---|---|
| LMArena Instruction Following | 1119 | 1446 |
Long Context Grok 4.5 leads
GPT-3.5-turbo: 34.0 (#254), Grok 4.5: 44.8 (#56)
| Benchmark | GPT-3.5-turbo | Grok 4.5 |
|---|---|---|
| LMArena Longer Query | 1121 | 1463 |
Writing & Preference Grok 4.5 leads
GPT-3.5-turbo: 25.3 (#305), Grok 4.5: 65.8 (#42)
| Benchmark | GPT-3.5-turbo | Grok 4.5 |
|---|---|---|
| LMArena Text | 1125 | 1448 |
| LMArena Creative Writing | 1092 | 1442 |
| EQ-Bench Creative Writing | 451 | 1579 |
| LMArena Multi-Turn | 1117 | 1456 |
Frequently asked questions
Is GPT-3.5-turbo better than Grok 4.5?
Grok 4.5 is the stronger model overall, scoring 55.0 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 4.0× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Which is cheaper, GPT-3.5-turbo or Grok 4.5?
GPT-3.5-turbo is cheaper. It lists at $0.50 per million input tokens and $1.50 per million output tokens; Grok 4.5 lists at $2 and $6.
Is GPT-3.5-turbo or Grok 4.5 better for coding?
Grok 4.5 scores higher on coding benchmarks: 52.2 versus 23.9 in the Noometry coding category.
Which has the bigger context window?
Grok 4.5 does, with 500K tokens against 16K.
How many benchmarks do GPT-3.5-turbo and Grok 4.5 share?
26 benchmarks have published results for both models. GPT-3.5-turbo has 44 scored results on Noometry and Grok 4.5 has 52.