# Grok 4.20 Multi-Agent vs Llama-3.3-70B-Instruct

> Grok 4.20 Multi-Agent is the stronger model overall, scoring 45.5 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 10× 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-llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. Grok 4.20 Multi-Agent scores higher in 8 categories and Llama-3.3-70B-Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.20 Multi-Agent leads 42.4 to 13.7.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 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 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 45.5 | 30.3 |
| Rank | 62 | 283 |
| Context | 1M | 128K |
| Input $/M | $1.25 | $0.10 |
| Output $/M | $2.50 | $0.32 |
| Weights | Proprietary | Open |

## Coding

- Grok 4.20 Multi-Agent: 42.4 (#91)
- Llama-3.3-70B-Instruct: 30.6 (#284)

| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Coding | 1458 | 1268 |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |

## Agentic & Tool Use

- Grok 4.20 Multi-Agent: —
- Llama-3.3-70B-Instruct: 25.6 (#100)

| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| LMArena Search | 1204 | — |

## Reasoning

- Grok 4.20 Multi-Agent: 42.4 (#44)
- Llama-3.3-70B-Instruct: 13.7 (#325)

| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1448 | 1257 |
| SimpleBench | — | 19.9% |
| NYT Connections (extended) | 89.6% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.32 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |

## Math

- Grok 4.20 Multi-Agent: 36.6 (#106)
- Llama-3.3-70B-Instruct: 14.3 (#292)

| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Math | 1441 | 1268 |
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |

## Knowledge

- Grok 4.20 Multi-Agent: 39.7 (#116)
- Llama-3.3-70B-Instruct: 30.5 (#220)

| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Expert | 1445 | 1225 |
| GPQA Diamond | — | 47.4% |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |

## Multimodal

- Grok 4.20 Multi-Agent: 39.8 (#44)
- Llama-3.3-70B-Instruct: —

| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1259 | — |

## Multilingual

- Grok 4.20 Multi-Agent: 54.6 (#40)
- Llama-3.3-70B-Instruct: 40.1 (#215)

| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1440 | 1236 |
| LMArena Chinese | 1475 | 1217 |
| LMArena French | 1467 | 1280 |
| LMArena German | 1456 | 1251 |
| LMArena Japanese | 1405 | 1149 |
| LMArena Korean | 1416 | 1142 |
| LMArena Russian | 1457 | 1252 |
| LMArena Spanish | 1447 | 1269 |

## Instruction Following

- Grok 4.20 Multi-Agent: 75.0 (#80)
- Llama-3.3-70B-Instruct: 71.3 (#152)

| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1420 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- Grok 4.20 Multi-Agent: 43.7 (#84)
- Llama-3.3-70B-Instruct: 26.4 (#290)

| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1430 | 1256 |
| Fiction.LiveBench | — | 33.3% |

## Writing & Preference

- Grok 4.20 Multi-Agent: 64.1 (#57)
- Llama-3.3-70B-Instruct: 47.8 (#203)

| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1450 | 1274 |
| LMArena Creative Writing | 1435 | 1250 |
| LMArena Multi-Turn | 1451 | 1279 |
| LiveBench Language | — | 39.2% |

## FAQ

### Is Grok 4.20 Multi-Agent better than Llama-3.3-70B-Instruct?

Grok 4.20 Multi-Agent is the stronger model overall, scoring 45.5 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 10× 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 Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Grok 4.20 Multi-Agent lists at $1.25 and $2.50.

### Is Grok 4.20 Multi-Agent or Llama-3.3-70B-Instruct better for coding?

Grok 4.20 Multi-Agent scores higher on coding benchmarks: 42.4 versus 30.6 in the Noometry coding category.

### Which has the bigger context window?

Grok 4.20 Multi-Agent does, with 1M tokens against 128K.

### How many benchmarks do Grok 4.20 Multi-Agent and Llama-3.3-70B-Instruct share?

17 benchmarks have published results for both models. Grok 4.20 Multi-Agent has 20 scored results on Noometry and Llama-3.3-70B-Instruct has 43.