# Grok 4.5 vs Llama 3.1-8B

> Grok 4.5 is the stronger model overall, scoring 55.0 to 23.0 on the Noometry Index. Llama 3.1-8B costs 52× less per token, which makes it the better buy when Grok 4.5's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/grok-4-5-vs-llama-3-1-8b
- Last updated: 2026-10-12
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. Grok 4.5 scores higher in 9 categories and Llama 3.1-8B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Grok 4.5 leads 62.3 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 97.8% for Grok 4.5 and 1.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $2 / $6 for Grok 4.5.
- Grok 4.5 accepts more context: 500K tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 55.0 | 23.0 |
| Rank | 25 | 352 |
| Context | 500K | 128K |
| Input $/M | $2 | $0.05 |
| Output $/M | $6 | $0.08 |
| Weights | Proprietary | Open |

## Coding

- Grok 4.5: 52.2 (#35)
- Llama 3.1-8B: 20.2 (#340)

| Benchmark | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| SciCode | 54.1% | 13.2% |
| WeirdML | 46.4% | 1.7% |
| LMArena Coding | 1474 | 1195 |
| DeepSWE | 53.8% | — |
| FrontierCode | 42.4% | — |
| LMArena WebDev | 1553 | — |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 1,309 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- Grok 4.5: 44.4 (#17)
- Llama 3.1-8B: 22.5 (#131)

| Benchmark | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| APEX-Agents | 56.2% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| τ²-bench Banking | 47.9% | — |
| PostTrainBench | 23.4% | — |
| BALROG | — | 15.1% |
| GBAEval | 65.4% | — |
| GDP.pdf | 14% | — |
| LMArena Search | 1213 | — |
| Vending-Bench 2 | 3,887 | — |

## Reasoning

- Grok 4.5: 56.1 (#25)
- Llama 3.1-8B: 14.9 (#321)

| Benchmark | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| CritPt | 15.4% | 0% |
| Chess Puzzles | 36% | 0% |
| LMArena Hard Prompts | 1462 | 1175 |
| DTBench | 96.5% | 50.9% |
| LMCA | 45.2% | 5.4% |
| Epoch Capabilities Index | 153.92 | 116.57 |
| ARC-AGI-2 | 52.6% | — |
| SimpleBench | 70% | — |
| Kagi LLM Benchmark | 83.5% | — |
| NYT Connections (extended) | 79.9% | — |
| ARC-AGI-1 | 87.2% | — |
| Surface Evolver Bench | 74.4% | — |
| PIQA | — | 81.2% |

## Math

- Grok 4.5: 60.9 (#35)
- Llama 3.1-8B: 10.2 (#317)

| Benchmark | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 97.8% | 1.7% |
| LMArena Math | 1459 | 1179 |
| FrontierMath (Tiers 1-3) | 57.2% | — |
| FrontierMath Tier 4 | 24.4% | — |
| ProofBench | 31% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |

## Knowledge

- Grok 4.5: 62.3 (#24)
- Llama 3.1-8B: 8.0 (#307)

| Benchmark | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 93.4% | 27% |
| LMArena Expert | 1466 | 1144 |
| SimpleQA Verified | 48.3% | — |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multimodal

- Grok 4.5: 37.6 (#72)
- Llama 3.1-8B: —

| Benchmark | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1288 | — |
| Blueprint-Bench 2 | 27.3% | — |
| Furniture Assembly | 22.5% | — |
| LMArena Document | 1452 | — |

## Multilingual

- Grok 4.5: 54.4 (#42)
- Llama 3.1-8B: 34.0 (#249)

| Benchmark | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1440 | 1148 |
| LMArena Chinese | 1496 | 1151 |
| LMArena French | 1456 | 1177 |
| LMArena German | 1446 | 1144 |
| LMArena Japanese | 1428 | 1061 |
| LMArena Korean | 1404 | 1053 |
| LMArena Russian | 1448 | 1158 |
| LMArena Spanish | 1450 | 1169 |

## Instruction Following

- Grok 4.5: 76.0 (#48)
- Llama 3.1-8B: 58.9 (#258)

| Benchmark | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1446 | 1159 |
| IFEval | — | 74.3% |

## Long Context

- Grok 4.5: 44.8 (#56)
- Llama 3.1-8B: 35.8 (#238)

| Benchmark | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1463 | 1182 |

## Writing & Preference

- Grok 4.5: 65.8 (#42)
- Llama 3.1-8B: 29.7 (#290)

| Benchmark | Grok 4.5 | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1448 | 1187 |
| LMArena Creative Writing | 1442 | 1154 |
| EQ-Bench Creative Writing | 1579 | 713 |
| LMArena Multi-Turn | 1456 | 1172 |
| WildBench | — | 68.7% |

## FAQ

### Is Grok 4.5 better than Llama 3.1-8B?

Grok 4.5 is the stronger model overall, scoring 55.0 to 23.0 on the Noometry Index. Llama 3.1-8B costs 52× 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 3.1-8B?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Grok 4.5 lists at $2 and $6.

### Is Grok 4.5 or Llama 3.1-8B better for coding?

Grok 4.5 scores higher on coding benchmarks: 52.2 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

Grok 4.5 does, with 500K tokens against 128K.

### How many benchmarks do Grok 4.5 and Llama 3.1-8B share?

27 benchmarks have published results for both models. Grok 4.5 has 52 scored results on Noometry and Llama 3.1-8B has 43.