Model comparison
Grok 4.6 vs Llama 3.1-70B
Grok 4.6 is the stronger model overall, scoring 56.8 to 29.4 on the Noometry Index. Llama 3.1-70B costs 7.5× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
MetaSummary
- They share 23 benchmarks with published results for both. Grok 4.6 scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Grok 4.6 leads 66.8 to 12.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 99.2% for Grok 4.6 and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.6 | Llama 3.1-70B | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 56.8 | 29.4 |
| Released | 2026-08-12 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 500K | 128K |
| Max output | 500K | 4K |
| Input $ / M tokens | $2 | $0.40 |
| Output $ / M tokens | $6 | $0.40 |
| Results tracked | 49 | 35 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.0 (#14), Llama 3.1-70B: 29.8 (#292)
| Benchmark | Grok 4.6 | Llama 3.1-70B |
|---|---|---|
| WeirdML | 67.3% | 9% |
| LMArena Coding | 1464 | 1260 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1620 | — |
| FrontierSWE | 25.3% | — |
| SciCode | 56.5% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 1,508 | — |
Agentic & Tool Use Grok 4.6 leads
Grok 4.6: 39.4 (#25), Llama 3.1-70B: 24.9 (#104)
| Benchmark | Grok 4.6 | Llama 3.1-70B |
|---|---|---|
| APEX-Agents | 65.3% | — |
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |
Reasoning Grok 4.6 leads
Grok 4.6: 61.3 (#18), Llama 3.1-70B: 20.8 (#215)
| Benchmark | Grok 4.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1445 | 1241 |
| DTBench | 97.3% | 60% |
| LMCA | 48.5% | 14.8% |
| Epoch Capabilities Index | 156.48 | 125.91 |
| ARC-AGI-2 | 67.1% | — |
| SimpleBench | 75.9% | — |
| NYT Connections (extended) | 80% | — |
| ARC-AGI-1 | 87.5% | — |
| CritPt | 19.7% | — |
| Chess Puzzles | 40% | — |
| EBR-Bench | 30.5% | — |
| Mystery Game Puzzles | 34% | — |
Math Grok 4.6 leads
Grok 4.6: 66.8 (#19), Llama 3.1-70B: 12.7 (#298)
| Benchmark | Grok 4.6 | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.2% | 3.6% |
| LMArena Math | 1416 | 1252 |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| ProofBench | 51% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
Knowledge Grok 4.6 leads
Grok 4.6: 63.3 (#18), Llama 3.1-70B: 24.1 (#264)
| Benchmark | Grok 4.6 | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 94% | 44.2% |
| LMArena Expert | 1463 | 1209 |
| SimpleQA Verified | 49.3% | — |
| MMLU-Pro | — | 65.3% |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multimodal Not comparable
Grok 4.6: 43.9 (#18), Llama 3.1-70B: —
| Benchmark | Grok 4.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1265 | — |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.6 leads
Grok 4.6: 53.3 (#67), Llama 3.1-70B: 38.9 (#220)
| Benchmark | Grok 4.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1422 | 1219 |
| LMArena Chinese | 1479 | 1215 |
| LMArena French | 1458 | 1260 |
| LMArena German | 1425 | 1222 |
| LMArena Japanese | 1383 | 1132 |
| LMArena Korean | 1395 | 1140 |
| LMArena Russian | 1427 | 1234 |
| LMArena Spanish | 1408 | 1253 |
Instruction Following Grok 4.6 leads
Grok 4.6: 75.6 (#59), Llama 3.1-70B: 65.5 (#218)
| Benchmark | Grok 4.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1431 | 1231 |
| IFEval | — | 82.1% |
Long Context Grok 4.6 leads
Grok 4.6: 44.4 (#62), Llama 3.1-70B: 37.6 (#208)
| Benchmark | Grok 4.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1453 | 1241 |
Writing & Preference Grok 4.6 leads
Grok 4.6: 62.5 (#77), Llama 3.1-70B: 35.6 (#262)
| Benchmark | Grok 4.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1427 | 1261 |
| LMArena Creative Writing | 1430 | 1232 |
| LMArena Multi-Turn | 1422 | 1256 |
| EQ-Bench Creative Writing | — | 784 |
| WildBench | — | 75.8% |
Frequently asked questions
Is Grok 4.6 better than Llama 3.1-70B?
Grok 4.6 is the stronger model overall, scoring 56.8 to 29.4 on the Noometry Index. Llama 3.1-70B costs 7.5× 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 3.1-70B?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; Grok 4.6 lists at $2 and $6.
Is Grok 4.6 or Llama 3.1-70B better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.0 versus 29.8 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 3.1-70B share?
23 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Llama 3.1-70B has 35.