Model comparison
Grok 4.5 vs Llama 3.2 1B
Grok 4.5 is the stronger model overall, scoring 55.0 to 20.1 on the Noometry Index. Llama 3.2 1B costs 43× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
MetaSummary
- They share 18 benchmarks with published results for both. Grok 4.5 scores higher in 9 categories and Llama 3.2 1B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Grok 4.5 leads 62.3 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 97.8% for Grok 4.5 and 0.6% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $2 / $6 for Grok 4.5.
- Grok 4.5 accepts more context: 500K tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.5 | Llama 3.2 1B | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 55.0 | 20.1 |
| Released | 2026-07-08 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 500K | 60K |
| Max output | 500K | 54K |
| Input $ / M tokens | $2 | $0.027 |
| Output $ / M tokens | $6 | $0.20 |
| Results tracked | 52 | 22 |
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Category by category
Coding Grok 4.5 leads
Grok 4.5: 52.2 (#35), Llama 3.2 1B: 21.1 (#338)
| Benchmark | Grok 4.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1474 | 1070 |
| DeepSWE | 53.8% | — |
| FrontierCode | 42.4% | — |
| LMArena WebDev | 1553 | — |
| SciCode | 54.1% | — |
| WeirdML | 46.4% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 1,309 | — |
Agentic & Tool Use Grok 4.5 leads
Grok 4.5: 44.4 (#17), Llama 3.2 1B: 14.6 (#150)
| Benchmark | Grok 4.5 | Llama 3.2 1B |
|---|---|---|
| APEX-Agents | 56.2% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| τ²-bench Banking | 47.9% | — |
| PostTrainBench | 23.4% | — |
| BALROG | — | 6.6% |
| GBAEval | 65.4% | — |
| GDP.pdf | 14% | — |
| LMArena Search | 1213 | — |
| Vending-Bench 2 | 3,887 | — |
Reasoning Grok 4.5 leads
Grok 4.5: 56.1 (#25), Llama 3.2 1B: 16.2 (#308)
| Benchmark | Grok 4.5 | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 36% | 0% |
| LMArena Hard Prompts | 1462 | 1044 |
| Epoch Capabilities Index | 153.92 | 101.99 |
| ARC-AGI-2 | 52.6% | — |
| SimpleBench | 70% | — |
| Kagi LLM Benchmark | 83.5% | — |
| NYT Connections (extended) | 79.9% | — |
| ARC-AGI-1 | 87.2% | — |
| CritPt | 15.4% | — |
| DTBench | 96.5% | — |
| LMCA | 45.2% | — |
| Surface Evolver Bench | 74.4% | — |
Math Grok 4.5 leads
Grok 4.5: 60.9 (#35), Llama 3.2 1B: 10.4 (#313)
| Benchmark | Grok 4.5 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 97.8% | 0.6% |
| LMArena Math | 1459 | 1086 |
| FrontierMath (Tiers 1-3) | 57.2% | — |
| FrontierMath Tier 4 | 24.4% | — |
| ProofBench | 31% | — |
Knowledge Grok 4.5 leads
Grok 4.5: 62.3 (#24), Llama 3.2 1B: 7.2 (#312)
| Benchmark | Grok 4.5 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 93.4% | 23.9% |
| LMArena Expert | 1466 | 1007 |
| SimpleQA Verified | 48.3% | — |
Multimodal Not comparable
Grok 4.5: 37.6 (#72), Llama 3.2 1B: —
| Benchmark | Grok 4.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1288 | — |
| Blueprint-Bench 2 | 27.3% | — |
| Furniture Assembly | 22.5% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.5 leads
Grok 4.5: 54.4 (#42), Llama 3.2 1B: 23.8 (#292)
| Benchmark | Grok 4.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1440 | 973 |
| LMArena Chinese | 1496 | 959 |
| LMArena German | 1446 | 1014 |
| LMArena Russian | 1448 | 941 |
| LMArena French | 1456 | — |
| LMArena Japanese | 1428 | — |
| LMArena Korean | 1404 | — |
| LMArena Spanish | 1450 | — |
Instruction Following Grok 4.5 leads
Grok 4.5: 76.0 (#48), Llama 3.2 1B: 52.4 (#290)
| Benchmark | Grok 4.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1446 | 1031 |
Long Context Grok 4.5 leads
Grok 4.5: 44.8 (#56), Llama 3.2 1B: 31.9 (#274)
| Benchmark | Grok 4.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1463 | 1050 |
Writing & Preference Grok 4.5 leads
Grok 4.5: 65.8 (#42), Llama 3.2 1B: 21.3 (#310)
| Benchmark | Grok 4.5 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1448 | 1055 |
| LMArena Creative Writing | 1442 | 1033 |
| EQ-Bench Creative Writing | 1579 | 200 |
| LMArena Multi-Turn | 1456 | 1030 |
Frequently asked questions
Is Grok 4.5 better than Llama 3.2 1B?
Grok 4.5 is the stronger model overall, scoring 55.0 to 20.1 on the Noometry Index. Llama 3.2 1B costs 43× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.
Which is cheaper, Grok 4.5 or Llama 3.2 1B?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Grok 4.5 lists at $2 and $6.
Is Grok 4.5 or Llama 3.2 1B better for coding?
Grok 4.5 scores higher on coding benchmarks: 52.2 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Grok 4.5 does, with 500K tokens against 60K.
How many benchmarks do Grok 4.5 and Llama 3.2 1B share?
18 benchmarks have published results for both models. Grok 4.5 has 52 scored results on Noometry and Llama 3.2 1B has 22.