# Kimi K2 (Jul 2025) vs Llama 3.1-8B

> Kimi K2 (Jul 2025) is the stronger model overall, scoring 40.9 to 22.8 on the Noometry Index. Llama 3.1-8B costs 17× less per token, which makes it the better buy when Kimi K2 (Jul 2025)'s lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/kimi-k2-vs-llama-3-1-8b
- Last updated: 2026-10-10
- Shared benchmarks: 26

## Summary

- They share 26 benchmarks with published results for both. Kimi K2 (Jul 2025) 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 writing & preference, where Kimi K2 (Jul 2025) leads 62.5 to 29.9.
- The biggest single-benchmark swing is Omni-MATH: 65.4% for Kimi K2 (Jul 2025) and 13.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.57 / $2.30 for Kimi K2 (Jul 2025).
- Kimi K2 (Jul 2025) accepts more context: 262K tokens versus 128K.

## Snapshot

| | Kimi K2 (Jul 2025) | Llama 3.1-8B |
|---|---|---|
| Provider | Moonshot AI | Meta |
| Noometry Index | 40.9 | 22.8 |
| Rank | 134 | 347 |
| Context | 262K | 128K |
| Input $/M | $0.57 | $0.05 |
| Output $/M | $2.30 | $0.08 |
| Weights | Open | Open |

## Coding

- Kimi K2 (Jul 2025): 43.1 (#83)
- Llama 3.1-8B: 19.9 (#335)

| Benchmark | Kimi K2 (Jul 2025) | Llama 3.1-8B |
|---|---|---|
| WeirdML | 42.8% | 1.7% |
| LMArena Coding | 1399 | 1195 |
| SWE-bench Verified (bash only) | 63.4% | — |
| Aider Polyglot | 59.1% | — |
| SciCode | — | 13.2% |
| GSO | 4.9% | — |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 597.5 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- Kimi K2 (Jul 2025): 32.2 (#59)
- Llama 3.1-8B: 22.3 (#123)

| Benchmark | Kimi K2 (Jul 2025) | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 59.1% | 25.8% |
| Terminal-Bench | 35.7% | — |
| BALROG | — | 15.1% |
| METR Time Horizons | 59.2% | — |

## Reasoning

- Kimi K2 (Jul 2025): 22.7 (#173)
- Llama 3.1-8B: 14.5 (#316)

| Benchmark | Kimi K2 (Jul 2025) | Llama 3.1-8B |
|---|---|---|
| LMArena Hard Prompts | 1384 | 1175 |
| Epoch Capabilities Index | 146.02 | 116.48 |
| SimpleBench | 26.3% | — |
| Kagi LLM Benchmark | 64.4% | — |
| CritPt | — | 0% |
| Chess Puzzles | — | 0% |
| DTBench | — | 50.9% |
| LMCA | — | 5.4% |
| ForecastBench | 60.2 | — |
| PIQA | — | 81.2% |

## Math

- Kimi K2 (Jul 2025): 40.1 (#81)
- Llama 3.1-8B: 9.5 (#311)

| Benchmark | Kimi K2 (Jul 2025) | Llama 3.1-8B |
|---|---|---|
| Omni-MATH | 65.4% | 13.7% |
| LMArena Math | 1396 | 1179 |
| OTIS Mock AIME 2024-2025 | — | 1.7% |
| MATH Level 5 | — | 22.9% |
| FrontierMath (Feb 2025 set) | 21.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 82.4% |

## Knowledge

- Kimi K2 (Jul 2025): 36.9 (#153)
- Llama 3.1-8B: 8.0 (#302)

| Benchmark | Kimi K2 (Jul 2025) | Llama 3.1-8B |
|---|---|---|
| MMLU-Pro | 81.9% | 40.6% |
| GPQA (HELM) | 65.3% | 24.7% |
| LMArena Expert | 1363 | 1144 |
| GPQA Diamond | — | 27% |
| Confabulations | 20.4% | — |
| Vectara Hallucination Rate | 17.9% | — |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multilingual

- Kimi K2 (Jul 2025): 49.8 (#125)
- Llama 3.1-8B: 34.2 (#244)

| Benchmark | Kimi K2 (Jul 2025) | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1372 | 1148 |
| LMArena Chinese | 1416 | 1151 |
| LMArena French | 1379 | 1176 |
| LMArena German | 1386 | 1144 |
| LMArena Japanese | 1349 | 1061 |
| LMArena Korean | 1324 | 1053 |
| LMArena Russian | 1384 | 1158 |
| LMArena Spanish | 1383 | 1169 |

## Instruction Following

- Kimi K2 (Jul 2025): 71.3 (#151)
- Llama 3.1-8B: 59.1 (#253)

| Benchmark | Kimi K2 (Jul 2025) | Llama 3.1-8B |
|---|---|---|
| IFEval | 85% | 74.3% |
| LMArena Instruction Following | 1347 | 1159 |

## Long Context

- Kimi K2 (Jul 2025): 41.2 (#140)
- Llama 3.1-8B: 35.8 (#233)

| Benchmark | Kimi K2 (Jul 2025) | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1353 | 1182 |
| Fiction.LiveBench | 66.7% | — |
| CL-bench | 17.6% | — |

## Writing & Preference

- Kimi K2 (Jul 2025): 62.5 (#76)
- Llama 3.1-8B: 29.9 (#285)

| Benchmark | Kimi K2 (Jul 2025) | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1380 | 1187 |
| LMArena Creative Writing | 1349 | 1154 |
| EQ-Bench Creative Writing | 1666 | 713 |
| WildBench | 86.2% | 68.7% |
| LMArena Multi-Turn | 1371 | 1172 |
| Short-Story Creative Writing | 85.6% | — |

## FAQ

### Is Kimi K2 (Jul 2025) better than Llama 3.1-8B?

Kimi K2 (Jul 2025) is the stronger model overall, scoring 40.9 to 22.8 on the Noometry Index. Llama 3.1-8B costs 17× less per token, which makes it the better buy when Kimi K2 (Jul 2025)'s lead doesn't matter for your workload.

### Which is cheaper, Kimi K2 (Jul 2025) 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 K2 (Jul 2025) lists at $0.57 and $2.30.

### Is Kimi K2 (Jul 2025) or Llama 3.1-8B better for coding?

Kimi K2 (Jul 2025) scores higher on coding benchmarks: 43.1 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

Kimi K2 (Jul 2025) does, with 262K tokens against 128K.

### How many benchmarks do Kimi K2 (Jul 2025) and Llama 3.1-8B share?

26 benchmarks have published results for both models. Kimi K2 (Jul 2025) has 42 scored results on Noometry and Llama 3.1-8B has 43.