Model comparison
Grok 4.6 vs Llama 3.2 3B
Grok 4.6 is the stronger model overall, scoring 56.9 to 28.9 on the Noometry Index. Llama 3.2 3B costs 25× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
MetaSummary
- They share 13 benchmarks with published results for both. Grok 4.6 scores higher in 9 categories and Llama 3.2 3B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.6 leads 61.4 to 21.0.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 131K.
- Llama 3.2 3B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.6 | Llama 3.2 3B | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 56.9 | 28.9 |
| Released | 2026-08-12 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 500K | 131K |
| Max output | 500K | 118K |
| Input $ / M tokens | $2 | $0.05 |
| Output $ / M tokens | $6 | $0.33 |
| Results tracked | 49 | 18 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.5 (#16), Llama 3.2 3B: 27.6 (#319)
| Benchmark | Grok 4.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Coding | 1465 | 1098 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1617 | — |
| FrontierSWE | 25.3% | — |
| SciCode | 56.5% | — |
| WeirdML | 67.3% | — |
| BigCodeBench Instruct | — | 23.4% |
| BigCodeBench Complete | — | 28.3% |
| ALE-Bench | 1,508 | — |
Agentic & Tool Use Grok 4.6 leads
Grok 4.6: 39.4 (#27), Llama 3.2 3B: 20.1 (#143)
| Benchmark | Grok 4.6 | Llama 3.2 3B |
|---|---|---|
| APEX-Agents | 65.3% | — |
| Berkeley Function Calling Leaderboard | — | 21.9% |
| BALROG | — | 10.1% |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |
Reasoning Grok 4.6 leads
Grok 4.6: 61.4 (#20), Llama 3.2 3B: 21.0 (#228)
| Benchmark | Grok 4.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1447 | 1095 |
| 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% | — |
| DTBench | 97.3% | — |
| LMCA | 48.5% | — |
| Epoch Capabilities Index | 156.44 | — |
Math Grok 4.6 leads
Grok 4.6: 67.0 (#24), Llama 3.2 3B: 32.4 (#214)
| Benchmark | Grok 4.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1423 | 1126 |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| OTIS Mock AIME 2024-2025 | 99.2% | — |
| ProofBench | 51% | — |
Knowledge Grok 4.6 leads
Grok 4.6: 63.3 (#20), Llama 3.2 3B: 29.7 (#235)
| Benchmark | Grok 4.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1467 | 1090 |
| GPQA Diamond | 94% | — |
| SimpleQA Verified | 49.3% | — |
Multimodal Not comparable
Grok 4.6: 43.6 (#23), Llama 3.2 3B: —
| Benchmark | Grok 4.6 | Llama 3.2 3B |
|---|---|---|
| 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 3.2 3B: 26.2 (#281)
| Benchmark | Grok 4.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1420 | 1019 |
| LMArena Chinese | 1480 | 1017 |
| LMArena German | 1431 | 1056 |
| LMArena Russian | 1422 | 949 |
| LMArena French | 1461 | — |
| LMArena Japanese | 1376 | — |
| LMArena Korean | 1397 | — |
| LMArena Spanish | 1404 | — |
Instruction Following Grok 4.6 leads
Grok 4.6: 75.4 (#63), Llama 3.2 3B: 56.0 (#275)
| Benchmark | Grok 4.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1431 | 1089 |
Long Context Grok 4.6 leads
Grok 4.6: 44.5 (#66), Llama 3.2 3B: 33.4 (#261)
| Benchmark | Grok 4.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1454 | 1100 |
Writing & Preference Grok 4.6 leads
Grok 4.6: 62.3 (#80), Llama 3.2 3B: 24.7 (#307)
| Benchmark | Grok 4.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1428 | 1110 |
| LMArena Creative Writing | 1428 | 1094 |
| LMArena Multi-Turn | 1425 | 1105 |
| EQ-Bench Creative Writing | — | 595 |
Frequently asked questions
Is Grok 4.6 better than Llama 3.2 3B?
Grok 4.6 is the stronger model overall, scoring 56.9 to 28.9 on the Noometry Index. Llama 3.2 3B costs 25× 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 3.2 3B?
Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; Grok 4.6 lists at $2 and $6.
Is Grok 4.6 or Llama 3.2 3B better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.5 versus 27.6 in the Noometry coding category.
Which has the bigger context window?
Grok 4.6 does, with 500K tokens against 131K.
How many benchmarks do Grok 4.6 and Llama 3.2 3B share?
13 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Llama 3.2 3B has 18.