# Claude Fable 5 vs Llama 3.1-8B

> Claude Fable 5 is the stronger model overall, scoring 66.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 348× less per token, which makes it the better buy when Claude Fable 5's lead doesn't matter for your workload.

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

## Summary

- They share 27 benchmarks with published results for both. Claude Fable 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 math, where Claude Fable 5 leads 88.5 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for Claude Fable 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 $10 / $50 for Claude Fable 5.
- Claude Fable 5 accepts more context: 1M tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 66.8 | 23.0 |
| Rank | 5 | 352 |
| Context | 1M | 128K |
| Input $/M | $10 | $0.05 |
| Output $/M | $50 | $0.08 |
| Weights | Proprietary | Open |

## Coding

- Claude Fable 5: 70.6 (#4)
- Llama 3.1-8B: 20.2 (#340)

| Benchmark | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| SciCode | 61% | 13.2% |
| WeirdML | 91.9% | 1.7% |
| LMArena Coding | 1519 | 1195 |
| DeepSWE | 69.9% | — |
| FrontierCode | 53.5% | — |
| LMArena WebDev | 1625 | — |
| FrontierSWE | 47% | — |
| GSO | 78.4% | — |
| BigCodeBench Instruct | — | 32.8% |
| MirrorCode | 63.9% | — |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 2,041 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- Claude Fable 5: 54.0 (#2)
- Llama 3.1-8B: 22.5 (#131)

| Benchmark | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| APEX-Agents | 63.6% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| Remote Labor Index | 16.1% | — |
| τ²-bench Banking | 39.7% | — |
| PostTrainBench | 41.8% | — |
| BALROG | — | 15.1% |
| GBAEval | 74.5% | — |
| GDP.pdf | 30% | — |
| LMArena Search | 1230 | — |
| Vending-Bench 2 | 5,680 | — |

## Reasoning

- Claude Fable 5: 76.8 (#6)
- Llama 3.1-8B: 14.9 (#321)

| Benchmark | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| CritPt | 28.6% | 0% |
| Chess Puzzles | 41% | 0% |
| LMArena Hard Prompts | 1508 | 1175 |
| DTBench | 98.4% | 50.9% |
| LMCA | 61.1% | 5.4% |
| Epoch Capabilities Index | 162.06 | 116.57 |
| ARC-AGI-2 | 89.2% | — |
| SimpleBench | 81.9% | — |
| Kagi LLM Benchmark | 91.4% | — |
| NYT Connections (extended) | 92.7% | — |
| ARC-AGI-1 | 98.5% | — |
| EnigmaEval | 39.3% | — |
| EBR-Bench | 39.5% | — |
| Mystery Game Puzzles | 52% | — |
| Surface Evolver Bench | 95% | — |
| Bench to the Future 3 | 0.13 | — |
| PIQA | — | 81.2% |

## Math

- Claude Fable 5: 88.5 (#5)
- Llama 3.1-8B: 10.2 (#317)

| Benchmark | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 1.7% |
| LMArena Math | 1519 | 1179 |
| FrontierMath (Tiers 1-3) | 87% | — |
| FrontierMath Tier 4 | 90.2% | — |
| ProofBench | 95% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| FrontierMath Erdős | 0% | — |
| GSM8K | — | 82.4% |

## Knowledge

- Claude Fable 5: 62.2 (#25)
- Llama 3.1-8B: 8.0 (#307)

| Benchmark | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 85.9% | 27% |
| LMArena Expert | 1534 | 1144 |
| SimpleQA Verified | 70.7% | — |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multimodal

- Claude Fable 5: 45.3 (#17)
- Llama 3.1-8B: —

| Benchmark | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1324 | — |
| Blueprint-Bench 2 | 38.6% | — |
| Furniture Assembly | 35.8% | — |
| LMArena Document | 1496 | — |

## Multilingual

- Claude Fable 5: 57.3 (#9)
- Llama 3.1-8B: 34.0 (#249)

| Benchmark | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1481 | 1148 |
| LMArena Chinese | 1543 | 1151 |
| LMArena French | 1505 | 1177 |
| LMArena German | 1486 | 1144 |
| LMArena Japanese | 1506 | 1061 |
| LMArena Korean | 1488 | 1053 |
| LMArena Russian | 1504 | 1158 |
| LMArena Spanish | 1498 | 1169 |

## Instruction Following

- Claude Fable 5: 78.6 (#8)
- Llama 3.1-8B: 58.9 (#258)

| Benchmark | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1502 | 1159 |
| IFEval | — | 74.3% |

## Long Context

- Claude Fable 5: 46.3 (#23)
- Llama 3.1-8B: 35.8 (#238)

| Benchmark | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1509 | 1182 |

## Writing & Preference

- Claude Fable 5: 75.9 (#5)
- Llama 3.1-8B: 29.7 (#290)

| Benchmark | Claude Fable 5 | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1491 | 1187 |
| LMArena Creative Writing | 1494 | 1154 |
| EQ-Bench Creative Writing | 1943 | 713 |
| LMArena Multi-Turn | 1504 | 1172 |
| WildBench | — | 68.7% |
| EQ-Bench 4 | 1340 | — |

## FAQ

### Is Claude Fable 5 better than Llama 3.1-8B?

Claude Fable 5 is the stronger model overall, scoring 66.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 348× less per token, which makes it the better buy when Claude Fable 5's lead doesn't matter for your workload.

### Which is cheaper, Claude Fable 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; Claude Fable 5 lists at $10 and $50.

### Is Claude Fable 5 or Llama 3.1-8B better for coding?

Claude Fable 5 scores higher on coding benchmarks: 70.6 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

Claude Fable 5 does, with 1M tokens against 128K.

### How many benchmarks do Claude Fable 5 and Llama 3.1-8B share?

27 benchmarks have published results for both models. Claude Fable 5 has 62 scored results on Noometry and Llama 3.1-8B has 43.