Model comparison
Grok 4 vs Llama-3.3-70B-Instruct
Grok 4 is the stronger model overall, scoring 47.8 to 30.3 on the Noometry Index.
Last verified . 27 shared benchmarks.
MetaSummary
- They share 27 benchmarks with published results for both. Grok 4 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 long context, where Grok 4 leads 63.1 to 26.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 84% for Grok 4 and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Grok 4 | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 47.8 | 30.3 |
| Released | 2025-07-09 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | — | 128K |
| Max output | — | 4K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.32 |
| Results tracked | 48 | 43 |
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Category by category
Coding Grok 4 leads
Grok 4: 49.9 (#44), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | Grok 4 | Llama-3.3-70B-Instruct |
|---|---|---|
| WeirdML | 45.7% | 14.4% |
| LMArena Coding | 1408 | 1268 |
| Aider Polyglot | 79.6% | — |
| SciCode | — | 26% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use Grok 4 leads
Grok 4: 32.1 (#62), Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | Grok 4 | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 63% | 31.9% |
| BALROG | 43.6% | 23% |
| Terminal-Bench | 27.2% | — |
| GDPval | 21.1% | — |
| Cybench | 43% | — |
| DeepResearch Bench | 47.3% | — |
| LMArena Search | 1142 | — |
| METR Time Horizons | 66.6% | — |
Reasoning Grok 4 leads
Grok 4: 36.9 (#59), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | Grok 4 | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 60.5% | 19.9% |
| LMArena Hard Prompts | 1409 | 1257 |
| Epoch Capabilities Index | 146.46 | 127.32 |
| ForecastBench | 60.9 | 58.6 |
| ARC-AGI-2 | 16% | — |
| Kagi LLM Benchmark | 73.6% | — |
| ARC-AGI-1 | 66.7% | — |
| CritPt | — | 0% |
| Chess Puzzles | 28% | — |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| LiveBench | — | 50.2% |
Math Grok 4 leads
Grok 4: 46.5 (#58), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | Grok 4 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 84% | 5.1% |
| LMArena Math | 1422 | 1268 |
| Omni-MATH | 60.3% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
| FrontierMath (Feb 2025 set) | 19.7% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Grok 4 leads
Grok 4: 53.7 (#50), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | Grok 4 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 87% | 47.4% |
| Confabulations | 12.4% | 22.8% |
| LMArena Expert | 1417 | 1225 |
| MMLU-Pro | 85.1% | — |
| Vectara Hallucination Rate | — | 4.1% |
| GPQA (HELM) | 72.7% | — |
| MMLU | — | 86.3% |
Multimodal Not comparable
Grok 4: 33.1 (#89), Llama-3.3-70B-Instruct: —
| Benchmark | Grok 4 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1211 | — |
| GeoBench | 45% | — |
Multilingual Grok 4 leads
Grok 4: 52.0 (#98), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | Grok 4 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1403 | 1236 |
| LMArena Chinese | 1428 | 1217 |
| LMArena French | 1418 | 1280 |
| LMArena German | 1429 | 1251 |
| LMArena Japanese | 1396 | 1149 |
| LMArena Korean | 1376 | 1142 |
| LMArena Russian | 1409 | 1252 |
| LMArena Spanish | 1419 | 1269 |
Instruction Following Grok 4 leads
Grok 4: 79.4 (#4), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | Grok 4 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1387 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
| IFEval | 94.9% | — |
Long Context Grok 4 leads
Grok 4: 63.1 (#4), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | Grok 4 | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | 94.4% | 33.3% |
| LMArena Longer Query | 1409 | 1256 |
Writing & Preference Grok 4 leads
Grok 4: 58.7 (#111), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | Grok 4 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1412 | 1274 |
| LMArena Creative Writing | 1397 | 1250 |
| LMArena Multi-Turn | 1416 | 1279 |
| Short-Story Creative Writing | 76.9% | — |
| WildBench | 79.7% | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Grok 4 better than Llama-3.3-70B-Instruct?
Grok 4 is the stronger model overall, scoring 47.8 to 30.3 on the Noometry Index.
Is Grok 4 or Llama-3.3-70B-Instruct better for coding?
Grok 4 scores higher on coding benchmarks: 49.9 versus 30.6 in the Noometry coding category.
How many benchmarks do Grok 4 and Llama-3.3-70B-Instruct share?
27 benchmarks have published results for both models. Grok 4 has 48 scored results on Noometry and Llama-3.3-70B-Instruct has 43.