# Grok 4.6 vs Llama-3.3-70B-Instruct

> Grok 4.6 is the stronger model overall, scoring 56.8 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 19× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/grok-4-6-vs-llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 26

## Summary

- They share 26 benchmarks with published results for both. Grok 4.6 scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Grok 4.6 leads 66.8 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 99.2% for Grok 4.6 and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 56.8 | 30.3 |
| Rank | 19 | 283 |
| Context | 500K | 128K |
| Input $/M | $2 | $0.10 |
| Output $/M | $6 | $0.32 |
| Weights | Proprietary | Open |

## Coding

- Grok 4.6: 58.0 (#14)
- Llama-3.3-70B-Instruct: 30.6 (#284)

| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 56.5% | 26% |
| WeirdML | 67.3% | 14.4% |
| LMArena Coding | 1464 | 1268 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1620 | — |
| FrontierSWE | 25.3% | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 1,508 | — |

## Agentic & Tool Use

- Grok 4.6: 39.4 (#25)
- Llama-3.3-70B-Instruct: 25.6 (#100)

| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| APEX-Agents | 65.3% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |

## Reasoning

- Grok 4.6: 61.3 (#18)
- Llama-3.3-70B-Instruct: 13.7 (#325)

| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 75.9% | 19.9% |
| CritPt | 19.7% | 0% |
| LMArena Hard Prompts | 1445 | 1257 |
| DTBench | 97.3% | 59.5% |
| LMCA | 48.5% | 17.5% |
| Epoch Capabilities Index | 156.48 | 127.32 |
| ARC-AGI-2 | 67.1% | — |
| NYT Connections (extended) | 80% | — |
| ARC-AGI-1 | 87.5% | — |
| Chess Puzzles | 40% | — |
| EBR-Bench | 30.5% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 34% | — |
| LiveBench Data Analysis | — | 49.5% |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |

## Math

- Grok 4.6: 66.8 (#19)
- Llama-3.3-70B-Instruct: 14.3 (#292)

| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.2% | 5.1% |
| LMArena Math | 1416 | 1268 |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| ProofBench | 51% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |

## Knowledge

- Grok 4.6: 63.3 (#18)
- Llama-3.3-70B-Instruct: 30.5 (#220)

| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 94% | 47.4% |
| LMArena Expert | 1463 | 1225 |
| SimpleQA Verified | 49.3% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |

## Multimodal

- Grok 4.6: 43.9 (#18)
- Llama-3.3-70B-Instruct: —

| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1265 | — |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |

## Multilingual

- Grok 4.6: 53.3 (#67)
- Llama-3.3-70B-Instruct: 40.1 (#215)

| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1422 | 1236 |
| LMArena Chinese | 1479 | 1217 |
| LMArena French | 1458 | 1280 |
| LMArena German | 1425 | 1251 |
| LMArena Japanese | 1383 | 1149 |
| LMArena Korean | 1395 | 1142 |
| LMArena Russian | 1427 | 1252 |
| LMArena Spanish | 1408 | 1269 |

## Instruction Following

- Grok 4.6: 75.6 (#59)
- Llama-3.3-70B-Instruct: 71.3 (#152)

| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1431 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- Grok 4.6: 44.4 (#62)
- Llama-3.3-70B-Instruct: 26.4 (#290)

| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1453 | 1256 |
| Fiction.LiveBench | — | 33.3% |

## Writing & Preference

- Grok 4.6: 62.5 (#77)
- Llama-3.3-70B-Instruct: 47.8 (#203)

| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1427 | 1274 |
| LMArena Creative Writing | 1430 | 1250 |
| LMArena Multi-Turn | 1422 | 1279 |
| LiveBench Language | — | 39.2% |

## FAQ

### Is Grok 4.6 better than Llama-3.3-70B-Instruct?

Grok 4.6 is the stronger model overall, scoring 56.8 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 19× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.

### Which is cheaper, Grok 4.6 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 4.6 lists at $2 and $6.

### Is Grok 4.6 or Llama-3.3-70B-Instruct better for coding?

Grok 4.6 scores higher on coding benchmarks: 58.0 versus 30.6 in the Noometry coding category.

### Which has the bigger context window?

Grok 4.6 does, with 500K tokens against 128K.

### How many benchmarks do Grok 4.6 and Llama-3.3-70B-Instruct share?

26 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Llama-3.3-70B-Instruct has 43.