Model comparison
GPT-3.5-turbo vs Grok 4.20 Multi-Agent
Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 2.1× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. GPT-3.5-turbo scores higher in 0 categories and Grok 4.20 Multi-Agent in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Grok 4.20 Multi-Agent leads 64.0 to 25.3.
- GPT-3.5-turbo is cheaper at $0.50 / $1.50 per million input/output tokens, against $1.25 / $2.50 for Grok 4.20 Multi-Agent.
- Grok 4.20 Multi-Agent accepts more context: 1M tokens versus 16K.
Side by side
| GPT-3.5-turbo | Grok 4.20 Multi-Agent | |
|---|---|---|
| Provider | OpenAI | xAI |
| Noometry Index | 23.2 | 46.2 |
| Released | 2023-03-01 | 2026-03-09 |
| Weights | Proprietary | Proprietary |
| Context window | 16K | 1M |
| Max output | 4K | 30K |
| Input $ / M tokens | $0.50 | $1.25 |
| Output $ / M tokens | $1.50 | $2.50 |
| Results tracked | 44 | 20 |
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Category by category
Coding Grok 4.20 Multi-Agent leads
GPT-3.5-turbo: 23.9 (#331), Grok 4.20 Multi-Agent: 43.0 (#92)
| Benchmark | GPT-3.5-turbo | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Coding | 1136 | 1457 |
| WeirdML | 3.5% | — |
| BigCodeBench Instruct | 39.1% | — |
| BigCodeBench Complete | 50.6% | — |
| HumanEval+ | 70.7% | — |
| MBPP+ | 69.7% | — |
Agentic & Tool Use Not comparable
GPT-3.5-turbo: —, Grok 4.20 Multi-Agent: —
| Benchmark | GPT-3.5-turbo | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Search | — | 1204 |
| METR Time Horizons | 21.5% | — |
Reasoning Grok 4.20 Multi-Agent leads
GPT-3.5-turbo: 13.8 (#332), Grok 4.20 Multi-Agent: 43.9 (#48)
| Benchmark | GPT-3.5-turbo | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Hard Prompts | 1108 | 1448 |
| NYT Connections (extended) | — | 89.6% |
| Chess Puzzles | 0% | — |
| Mystery Game Puzzles | 3% | — |
| DTBench | 48.5% | — |
| LMCA | 9.7% | — |
| Adversarial NLI | 58.1% | — |
| BIG-Bench Hard | 61.6% | — |
| CommonsenseQA 2.0 | 57% | — |
| Epoch Capabilities Index | 118.55 | — |
| ForecastBench | 50.4 | — |
| WinoGrande | 81.6% | — |
Math Grok 4.20 Multi-Agent leads
GPT-3.5-turbo: 6.3 (#327), Grok 4.20 Multi-Agent: 39.4 (#104)
| Benchmark | GPT-3.5-turbo | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Math | 1142 | 1442 |
| FrontierMath (Tiers 1-3) | 0% | — |
| OTIS Mock AIME 2024-2025 | 2.2% | — |
| MATH Level 5 | 15.9% | — |
| GSM8K | 57.8% | — |
Knowledge Grok 4.20 Multi-Agent leads
GPT-3.5-turbo: 10.0 (#303), Grok 4.20 Multi-Agent: 40.4 (#119)
| Benchmark | GPT-3.5-turbo | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Expert | 1070 | 1445 |
| GPQA Diamond | 28% | — |
| ARC (AI2) Challenge | 87.4% | — |
| BoolQ | 87% | — |
| MMLU | 71.4% | — |
| OpenBookQA | 86% | — |
| TriviaQA | 85.8% | — |
Multimodal Not comparable
GPT-3.5-turbo: —, Grok 4.20 Multi-Agent: 40.5 (#48)
| Benchmark | GPT-3.5-turbo | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Vision | — | 1259 |
Multilingual Grok 4.20 Multi-Agent leads
GPT-3.5-turbo: 31.5 (#258), Grok 4.20 Multi-Agent: 54.4 (#43)
| Benchmark | GPT-3.5-turbo | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Non-English | 1108 | 1440 |
| LMArena Chinese | 1075 | 1475 |
| LMArena French | 1118 | 1466 |
| LMArena German | 1090 | 1456 |
| LMArena Japanese | 1043 | 1405 |
| LMArena Korean | 1019 | 1416 |
| LMArena Russian | 1123 | 1457 |
| LMArena Spanish | 1121 | 1447 |
Instruction Following Grok 4.20 Multi-Agent leads
GPT-3.5-turbo: 57.9 (#262), Grok 4.20 Multi-Agent: 74.8 (#84)
| Benchmark | GPT-3.5-turbo | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Instruction Following | 1119 | 1420 |
Long Context Grok 4.20 Multi-Agent leads
GPT-3.5-turbo: 34.0 (#254), Grok 4.20 Multi-Agent: 43.7 (#88)
| Benchmark | GPT-3.5-turbo | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Longer Query | 1121 | 1431 |
Writing & Preference Grok 4.20 Multi-Agent leads
GPT-3.5-turbo: 25.3 (#305), Grok 4.20 Multi-Agent: 64.0 (#59)
| Benchmark | GPT-3.5-turbo | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Text | 1125 | 1450 |
| LMArena Creative Writing | 1092 | 1436 |
| LMArena Multi-Turn | 1117 | 1452 |
| EQ-Bench Creative Writing | 451 | — |
Frequently asked questions
Is GPT-3.5-turbo better than Grok 4.20 Multi-Agent?
Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 2.1× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Which is cheaper, GPT-3.5-turbo or Grok 4.20 Multi-Agent?
GPT-3.5-turbo is cheaper. It lists at $0.50 per million input tokens and $1.50 per million output tokens; Grok 4.20 Multi-Agent lists at $1.25 and $2.50.
Is GPT-3.5-turbo or Grok 4.20 Multi-Agent better for coding?
Grok 4.20 Multi-Agent scores higher on coding benchmarks: 43.0 versus 23.9 in the Noometry coding category.
Which has the bigger context window?
Grok 4.20 Multi-Agent does, with 1M tokens against 16K.
How many benchmarks do GPT-3.5-turbo and Grok 4.20 Multi-Agent share?
17 benchmarks have published results for both models. GPT-3.5-turbo has 44 scored results on Noometry and Grok 4.20 Multi-Agent has 20.