# gpt-oss-120b vs Grok 4.20 Multi-Agent

> Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 36.3 on the Noometry Index. gpt-oss-120b costs 22× 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/gpt-oss-120b-vs-grok-4-20-multi-agent
- Last updated: 2026-10-12
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. gpt-oss-120b scores higher in 2 categories and Grok 4.20 Multi-Agent in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.20 Multi-Agent leads 43.9 to 20.0.
- gpt-oss-120b is cheaper at $0.037 / $0.17 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 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.

## Snapshot

| | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| Provider | OpenAI | xAI |
| Noometry Index | 36.3 | 46.2 |
| Rank | 217 | 65 |
| Context | 131K | 1M |
| Input $/M | $0.037 | $1.25 |
| Output $/M | $0.17 | $2.50 |
| Weights | Open | Proprietary |

## Coding

- gpt-oss-120b: 33.5 (#256)
- Grok 4.20 Multi-Agent: 43.0 (#92)

| Benchmark | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Coding | 1380 | 1457 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |

## Agentic & Tool Use

- gpt-oss-120b: 12.2 (#153)
- Grok 4.20 Multi-Agent: —

| Benchmark | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| LMArena Search | — | 1204 |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |

## Reasoning

- gpt-oss-120b: 20.0 (#245)
- Grok 4.20 Multi-Agent: 43.9 (#48)

| Benchmark | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Hard Prompts | 1364 | 1448 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| NYT Connections (extended) | — | 89.6% |
| CritPt | 1.1% | — |
| Chess Puzzles | 20% | — |
| Mystery Game Puzzles | 2% | — |
| DTBench | 76.3% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
| Epoch Capabilities Index | 139.93 | — |

## Math

- gpt-oss-120b: 52.5 (#50)
- Grok 4.20 Multi-Agent: 39.4 (#104)

| Benchmark | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Math | 1389 | 1442 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| Omni-MATH | 68.8% | — |

## Knowledge

- gpt-oss-120b: 42.4 (#96)
- Grok 4.20 Multi-Agent: 40.4 (#119)

| Benchmark | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Expert | 1356 | 1445 |
| GPQA Diamond | 75.8% | — |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |

## Multimodal

- gpt-oss-120b: —
- Grok 4.20 Multi-Agent: 40.5 (#48)

| Benchmark | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Vision | — | 1259 |

## Multilingual

- gpt-oss-120b: 48.0 (#147)
- Grok 4.20 Multi-Agent: 54.4 (#43)

| Benchmark | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Non-English | 1351 | 1440 |
| LMArena Chinese | 1385 | 1475 |
| LMArena French | 1369 | 1466 |
| LMArena German | 1353 | 1456 |
| LMArena Japanese | 1331 | 1405 |
| LMArena Korean | 1282 | 1416 |
| LMArena Russian | 1343 | 1457 |
| LMArena Spanish | 1389 | 1447 |

## Instruction Following

- gpt-oss-120b: 69.3 (#173)
- Grok 4.20 Multi-Agent: 74.8 (#84)

| Benchmark | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Instruction Following | 1318 | 1420 |
| IFEval | 83.6% | — |

## Long Context

- gpt-oss-120b: 31.4 (#278)
- Grok 4.20 Multi-Agent: 43.7 (#88)

| Benchmark | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Longer Query | 1319 | 1431 |
| Fiction.LiveBench | 44.4% | — |

## Writing & Preference

- gpt-oss-120b: 46.5 (#217)
- Grok 4.20 Multi-Agent: 64.0 (#59)

| Benchmark | gpt-oss-120b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Text | 1365 | 1450 |
| LMArena Creative Writing | 1275 | 1436 |
| LMArena Multi-Turn | 1340 | 1452 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |

## FAQ

### Is gpt-oss-120b better than Grok 4.20 Multi-Agent?

Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 36.3 on the Noometry Index. gpt-oss-120b costs 22× 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-oss-120b or Grok 4.20 Multi-Agent?

gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Grok 4.20 Multi-Agent lists at $1.25 and $2.50.

### Is gpt-oss-120b or Grok 4.20 Multi-Agent better for coding?

Grok 4.20 Multi-Agent scores higher on coding benchmarks: 43.0 versus 33.5 in the Noometry coding category.

### Which has the bigger context window?

Grok 4.20 Multi-Agent does, with 1M tokens against 131K.

### How many benchmarks do gpt-oss-120b and Grok 4.20 Multi-Agent share?

17 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Grok 4.20 Multi-Agent has 20.