Model comparison
Grok 4.6 vs Llama-3.3-70B-Instruct
Grok 4.6 is the stronger model overall, scoring 56.9 to 30.6 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.
Last verified . 26 shared benchmarks.
MetaSummary
- 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 67.0 to 15.3.
- 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.
Side by side
| Grok 4.6 | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 56.9 | 30.6 |
| Released | 2026-08-12 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 500K | 128K |
| Max output | 500K | 4K |
| Input $ / M tokens | $2 | $0.10 |
| Output $ / M tokens | $6 | $0.32 |
| Results tracked | 49 | 43 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.5 (#16), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 56.5% | 26% |
| WeirdML | 67.3% | 14.4% |
| LMArena Coding | 1465 | 1268 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1617 | — |
| 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 leads
Grok 4.6: 39.4 (#27), Llama-3.3-70B-Instruct: 25.8 (#105)
| 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 leads
Grok 4.6: 61.4 (#20), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 75.9% | 19.9% |
| CritPt | 19.7% | 0% |
| LMArena Hard Prompts | 1447 | 1257 |
| DTBench | 97.3% | 59.5% |
| LMCA | 48.5% | 17.5% |
| Epoch Capabilities Index | 156.44 | 127.33 |
| 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 leads
Grok 4.6: 67.0 (#24), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.2% | 5.1% |
| LMArena Math | 1423 | 1267 |
| 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 leads
Grok 4.6: 63.3 (#20), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 94% | 47.4% |
| LMArena Expert | 1467 | 1225 |
| SimpleQA Verified | 49.3% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multimodal Not comparable
Grok 4.6: 43.6 (#23), Llama-3.3-70B-Instruct: —
| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1263 | — |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.6 leads
Grok 4.6: 53.0 (#74), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1420 | 1236 |
| LMArena Chinese | 1480 | 1217 |
| LMArena French | 1461 | 1281 |
| LMArena German | 1431 | 1251 |
| LMArena Japanese | 1376 | 1150 |
| LMArena Korean | 1397 | 1143 |
| LMArena Russian | 1422 | 1252 |
| LMArena Spanish | 1404 | 1270 |
Instruction Following Grok 4.6 leads
Grok 4.6: 75.4 (#63), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1431 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context Grok 4.6 leads
Grok 4.6: 44.5 (#66), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1454 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference Grok 4.6 leads
Grok 4.6: 62.3 (#80), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | Grok 4.6 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1428 | 1274 |
| LMArena Creative Writing | 1428 | 1250 |
| LMArena Multi-Turn | 1425 | 1280 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Grok 4.6 better than Llama-3.3-70B-Instruct?
Grok 4.6 is the stronger model overall, scoring 56.9 to 30.6 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: 58.5 versus 31.0 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.