# Grok 4.20 Multi-Agent vs Qwen2.5-Coder-32B

> Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B 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.

- Canonical page: https://noometry.com/compare/grok-4-20-multi-agent-vs-qwen2-5-coder-32b
- Last updated: 2026-10-12
- Shared benchmarks: 12

## Summary

- They share 12 benchmarks with published results for both. Grok 4.20 Multi-Agent scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.20 Multi-Agent leads 43.9 to 21.2.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 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 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.

## Snapshot

| | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 46.2 | 33.4 |
| Rank | 65 | 245 |
| Context | 1M | 33K |
| Input $/M | $1.25 | $0.66 |
| Output $/M | $2.50 | $1 |
| Weights | Proprietary | Open |

## Coding

- Grok 4.20 Multi-Agent: 43.0 (#92)
- Qwen2.5-Coder-32B: 22.6 (#333)

| Benchmark | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1457 | 1276 |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |

## Agentic & Tool Use

- Grok 4.20 Multi-Agent: —
- Qwen2.5-Coder-32B: —

| Benchmark | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Search | 1204 | — |

## Reasoning

- Grok 4.20 Multi-Agent: 43.9 (#48)
- Qwen2.5-Coder-32B: 21.2 (#225)

| Benchmark | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1448 | 1251 |
| NYT Connections (extended) | 89.6% | — |
| LiveBench Reasoning | — | 42.1% |
| LiveBench Data Analysis | — | 49.9% |
| Epoch Capabilities Index | — | 119.49 |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |

## Math

- Grok 4.20 Multi-Agent: 39.4 (#104)
- Qwen2.5-Coder-32B: 33.3 (#204)

| Benchmark | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1442 | 1251 |
| LiveBench Math | — | 46.6% |
| GSM8K | — | 93% |

## Knowledge

- Grok 4.20 Multi-Agent: 40.4 (#119)
- Qwen2.5-Coder-32B: 33.4 (#203)

| Benchmark | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1445 | 1221 |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |

## Multimodal

- Grok 4.20 Multi-Agent: 40.5 (#48)
- Qwen2.5-Coder-32B: —

| Benchmark | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1259 | — |

## Multilingual

- Grok 4.20 Multi-Agent: 54.4 (#43)
- Qwen2.5-Coder-32B: 37.8 (#235)

| Benchmark | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1440 | 1205 |
| LMArena Chinese | 1475 | 1222 |
| LMArena Russian | 1457 | 1228 |
| LMArena French | 1466 | — |
| LMArena German | 1456 | — |
| LMArena Japanese | 1405 | — |
| LMArena Korean | 1416 | — |
| LMArena Spanish | 1447 | — |

## Instruction Following

- Grok 4.20 Multi-Agent: 74.8 (#84)
- Qwen2.5-Coder-32B: 61.4 (#245)

| Benchmark | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1420 | 1223 |
| LiveBench Instruction Following | — | 58.7% |

## Long Context

- Grok 4.20 Multi-Agent: 43.7 (#88)
- Qwen2.5-Coder-32B: 38.0 (#208)

| Benchmark | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1431 | 1251 |

## Writing & Preference

- Grok 4.20 Multi-Agent: 64.0 (#59)
- Qwen2.5-Coder-32B: 41.6 (#240)

| Benchmark | Grok 4.20 Multi-Agent | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1450 | 1230 |
| LMArena Creative Writing | 1436 | 1174 |
| LMArena Multi-Turn | 1452 | 1222 |
| LiveBench Language | — | 23.3% |

## FAQ

### Is Grok 4.20 Multi-Agent better than Qwen2.5-Coder-32B?

Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B 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, Grok 4.20 Multi-Agent or Qwen2.5-Coder-32B?

Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; Grok 4.20 Multi-Agent lists at $1.25 and $2.50.

### Is Grok 4.20 Multi-Agent or Qwen2.5-Coder-32B better for coding?

Grok 4.20 Multi-Agent scores higher on coding benchmarks: 43.0 versus 22.6 in the Noometry coding category.

### Which has the bigger context window?

Grok 4.20 Multi-Agent does, with 1M tokens against 33K.

### How many benchmarks do Grok 4.20 Multi-Agent and Qwen2.5-Coder-32B share?

12 benchmarks have published results for both models. Grok 4.20 Multi-Agent has 20 scored results on Noometry and Qwen2.5-Coder-32B has 31.