# Kimi K3 vs Llama 3.1-8B

> Kimi K3 is the stronger model overall, scoring 59.5 to 23.0 on the Noometry Index. Llama 3.1-8B costs 104× less per token, which makes it the better buy when Kimi K3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/kimi-k3-vs-llama-3-1-8b
- Last updated: 2026-10-10
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. Kimi K3 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 Kimi K3 leads 74.2 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 97.2% for Kimi K3 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 $3 / $15 for Kimi K3.
- Kimi K3 accepts more context: 1.05M tokens versus 128K.

## Snapshot

| | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| Provider | Moonshot AI | Meta |
| Noometry Index | 59.5 | 23.0 |
| Rank | 15 | 352 |
| Context | 1.05M | 128K |
| Input $/M | $3 | $0.05 |
| Output $/M | $15 | $0.08 |
| Weights | Open | Open |

## Coding

- Kimi K3: 61.0 (#10)
- Llama 3.1-8B: 20.2 (#340)

| Benchmark | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| SciCode | 59.5% | 13.2% |
| WeirdML | 82.6% | 1.7% |
| LMArena Coding | 1508 | 1195 |
| DeepSWE | 68.5% | — |
| FrontierCode | 44.2% | — |
| LMArena WebDev | 1654 | — |
| FrontierSWE | 25.9% | — |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 1,524 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- Kimi K3: 41.8 (#20)
- Llama 3.1-8B: 22.5 (#131)

| Benchmark | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| APEX-Agents | 50.6% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 32% | — |
| BALROG | — | 15.1% |
| GBAEval | 48.3% | — |
| GDP.pdf | 19% | — |
| Vending-Bench 2 | 5,165 | — |

## Reasoning

- Kimi K3: 63.0 (#17)
- Llama 3.1-8B: 14.9 (#321)

| Benchmark | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| CritPt | 23.4% | 0% |
| Chess Puzzles | 39% | 0% |
| LMArena Hard Prompts | 1496 | 1175 |
| DTBench | 91.2% | 50.9% |
| LMCA | 52.7% | 5.4% |
| Epoch Capabilities Index | 157.45 | 116.57 |
| ARC-AGI-2 | 60.4% | — |
| SimpleBench | 60.7% | — |
| NYT Connections (extended) | 93.6% | — |
| ARC-AGI-1 | 94.5% | — |
| Mystery Game Puzzles | 26% | — |
| Surface Evolver Bench | 95% | — |
| ForecastBench | 61.1 | — |
| PIQA | — | 81.2% |

## Math

- Kimi K3: 74.2 (#16)
- Llama 3.1-8B: 10.2 (#317)

| Benchmark | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 97.2% | 1.7% |
| LMArena Math | 1491 | 1179 |
| FrontierMath (Tiers 1-3) | 72.2% | — |
| FrontierMath Tier 4 | 39% | — |
| MathArena Final-Answer Competitions | 87.8% | — |
| ProofBench | 87% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |

## Knowledge

- Kimi K3: 63.2 (#21)
- Llama 3.1-8B: 8.0 (#307)

| Benchmark | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 93.1% | 27% |
| LMArena Expert | 1521 | 1144 |
| SimpleQA Verified | 50.6% | — |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multimodal

- Kimi K3: 37.8 (#70)
- Llama 3.1-8B: —

| Benchmark | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| Blueprint-Bench 2 | 29.5% | — |
| Furniture Assembly | 34.2% | — |

## Multilingual

- Kimi K3: 56.3 (#21)
- Llama 3.1-8B: 34.0 (#249)

| Benchmark | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1466 | 1148 |
| LMArena Chinese | 1529 | 1151 |
| LMArena French | 1491 | 1177 |
| LMArena German | 1488 | 1144 |
| LMArena Japanese | 1487 | 1061 |
| LMArena Korean | 1458 | 1053 |
| LMArena Russian | 1482 | 1158 |
| LMArena Spanish | 1472 | 1169 |

## Instruction Following

- Kimi K3: 77.7 (#14)
- Llama 3.1-8B: 58.9 (#258)

| Benchmark | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1483 | 1159 |
| IFEval | — | 74.3% |

## Long Context

- Kimi K3: 45.8 (#29)
- Llama 3.1-8B: 35.8 (#238)

| Benchmark | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1494 | 1182 |

## Writing & Preference

- Kimi K3: 76.6 (#4)
- Llama 3.1-8B: 29.7 (#290)

| Benchmark | Kimi K3 | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1476 | 1187 |
| LMArena Creative Writing | 1454 | 1154 |
| EQ-Bench Creative Writing | 2082 | 713 |
| LMArena Multi-Turn | 1488 | 1172 |
| WildBench | — | 68.7% |
| EQ-Bench 4 | 1339 | — |

## FAQ

### Is Kimi K3 better than Llama 3.1-8B?

Kimi K3 is the stronger model overall, scoring 59.5 to 23.0 on the Noometry Index. Llama 3.1-8B costs 104× less per token, which makes it the better buy when Kimi K3's lead doesn't matter for your workload.

### Which is cheaper, Kimi K3 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; Kimi K3 lists at $3 and $15.

### Is Kimi K3 or Llama 3.1-8B better for coding?

Kimi K3 scores higher on coding benchmarks: 61.0 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

Kimi K3 does, with 1.05M tokens against 128K.

### How many benchmarks do Kimi K3 and Llama 3.1-8B share?

27 benchmarks have published results for both models. Kimi K3 has 53 scored results on Noometry and Llama 3.1-8B has 43.