# Grok 4.3 vs Llama-3.3-70B-Instruct

> Grok 4.3 is the stronger model overall, scoring 43.6 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 4.3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/grok-4-3-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 4.3 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 4.3 leads 45.8 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.3% for Grok 4.3 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 $1.25 / $2.50 for Grok 4.3.
- Grok 4.3 accepts more context: 1M tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 43.6 | 30.3 |
| Rank | 80 | 283 |
| Context | 1M | 128K |
| Input $/M | $1.25 | $0.10 |
| Output $/M | $2.50 | $0.32 |
| Weights | Proprietary | Open |

## Coding

- Grok 4.3: 41.3 (#117)
- Llama-3.3-70B-Instruct: 30.6 (#284)

| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 47.3% | 26% |
| WeirdML | 49.9% | 14.4% |
| LMArena Coding | 1415 | 1268 |
| LMArena WebDev | 1357 | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 944.17 | — |

## Agentic & Tool Use

- Grok 4.3: 27.6 (#93)
- Llama-3.3-70B-Instruct: 25.6 (#100)

| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| GDP.pdf | 8% | — |
| LMArena Search | 1165 | — |
| Vending-Bench 2 | 35.26 | — |

## Reasoning

- Grok 4.3: 34.9 (#60)
- Llama-3.3-70B-Instruct: 13.7 (#325)

| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 8% | 0% |
| LMArena Hard Prompts | 1397 | 1257 |
| DTBench | 90.7% | 59.5% |
| LMCA | 38.3% | 17.5% |
| Epoch Capabilities Index | 149.13 | 127.32 |
| ForecastBench | 60.3 | 58.6 |
| SimpleBench | — | 19.9% |
| NYT Connections (extended) | 55.2% | — |
| Chess Puzzles | 25% | — |
| LiveBench Reasoning | — | 50.8% |
| LiveBench Data Analysis | — | 49.5% |
| LiveBench | — | 50.2% |

## Math

- Grok 4.3: 45.8 (#60)
- Llama-3.3-70B-Instruct: 14.3 (#292)

| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.3% | 5.1% |
| LMArena Math | 1388 | 1268 |
| FrontierMath (Tiers 1-3) | 42.8% | — |
| FrontierMath Tier 4 | 14.6% | — |
| ProofBench | 11% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |

## Knowledge

- Grok 4.3: 52.5 (#57)
- Llama-3.3-70B-Instruct: 30.5 (#220)

| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 88.8% | 47.4% |
| LMArena Expert | 1385 | 1225 |
| SimpleQA Verified | 33.2% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |

## Multimodal

- Grok 4.3: 31.6 (#96)
- Llama-3.3-70B-Instruct: —

| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1231 | — |
| Blueprint-Bench 2 | 0% | — |

## Multilingual

- Grok 4.3: 50.7 (#114)
- Llama-3.3-70B-Instruct: 40.1 (#215)

| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1385 | 1236 |
| LMArena Chinese | 1423 | 1217 |
| LMArena French | 1412 | 1280 |
| LMArena German | 1395 | 1251 |
| LMArena Japanese | 1379 | 1149 |
| LMArena Korean | 1357 | 1142 |
| LMArena Russian | 1399 | 1252 |
| LMArena Spanish | 1402 | 1269 |

## Instruction Following

- Grok 4.3: 72.3 (#135)
- Llama-3.3-70B-Instruct: 71.3 (#152)

| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1366 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- Grok 4.3: 42.5 (#118)
- Llama-3.3-70B-Instruct: 26.4 (#290)

| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1393 | 1256 |
| Fiction.LiveBench | — | 33.3% |

## Writing & Preference

- Grok 4.3: 58.6 (#112)
- Llama-3.3-70B-Instruct: 47.8 (#203)

| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1397 | 1274 |
| LMArena Creative Writing | 1380 | 1250 |
| LMArena Multi-Turn | 1406 | 1279 |
| EQ-Bench 4 | 1075 | — |
| LiveBench Language | — | 39.2% |

## FAQ

### Is Grok 4.3 better than Llama-3.3-70B-Instruct?

Grok 4.3 is the stronger model overall, scoring 43.6 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 4.3's lead doesn't matter for your workload.

### Which is cheaper, Grok 4.3 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 4.3 lists at $1.25 and $2.50.

### Is Grok 4.3 or Llama-3.3-70B-Instruct better for coding?

Grok 4.3 scores higher on coding benchmarks: 41.3 versus 30.6 in the Noometry coding category.

### Which has the bigger context window?

Grok 4.3 does, with 1M tokens against 128K.

### How many benchmarks do Grok 4.3 and Llama-3.3-70B-Instruct share?

26 benchmarks have published results for both models. Grok 4.3 has 40 scored results on Noometry and Llama-3.3-70B-Instruct has 43.