# Kimi K2.6 vs Llama 3.2 3B

> Kimi K2.6 is the stronger model overall, scoring 47.5 to 28.4 on the Noometry Index. Llama 3.2 3B costs 14× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/kimi-k2-6-vs-llama-3-2-3b
- Last updated: 2026-10-10
- Shared benchmarks: 14

## Summary

- They share 14 benchmarks with published results for both. Kimi K2.6 scores higher in 9 categories and Llama 3.2 3B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Kimi K2.6 leads 68.7 to 24.9.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.95 / $4 for Kimi K2.6.
- Kimi K2.6 accepts more context: 262K tokens versus 131K.

## Snapshot

| | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| Provider | Moonshot AI | Meta |
| Noometry Index | 47.5 | 28.4 |
| Rank | 52 | 318 |
| Context | 262K | 131K |
| Input $/M | $0.95 | $0.05 |
| Output $/M | $4 | $0.33 |
| Weights | Open | Open |

## Coding

- Kimi K2.6: 50.5 (#41)
- Llama 3.2 3B: 27.1 (#314)

| Benchmark | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Coding | 1488 | 1098 |
| SWE-bench Verified | 76.7% | — |
| LMArena WebDev | 1509 | — |
| SciCode | 53.5% | — |
| WeirdML | 55.9% | — |
| BigCodeBench Instruct | — | 23.4% |
| BigCodeBench Complete | — | 28.3% |
| ALE-Bench | 1,093 | — |

## Agentic & Tool Use

- Kimi K2.6: 21.8 (#128)
- Llama 3.2 3B: 19.9 (#135)

| Benchmark | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 21.9% |
| OSWorld 2.0 | 4.6% | — |
| BALROG | — | 10.1% |
| ExploitBench | 18.4% | — |
| GBAEval | 0.9% | — |
| GDP.pdf | 12% | — |
| Vending-Bench 2 | 6,205 | — |

## Reasoning

- Kimi K2.6: 39.6 (#51)
- Llama 3.2 3B: 20.1 (#225)

| Benchmark | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1470 | 1095 |
| NYT Connections (extended) | 87.2% | — |
| CritPt | 8% | — |
| Chess Puzzles | 26% | — |
| EBR-Bench | 2.4% | — |
| Mystery Game Puzzles | 18% | — |
| DTBench | 90.9% | — |
| LMCA | 37.3% | — |
| Epoch Capabilities Index | 151.03 | — |

## Math

- Kimi K2.6: 56.8 (#34)
- Llama 3.2 3B: 29.9 (#213)

| Benchmark | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1475 | 1126 |
| FrontierMath (Tiers 1-3) | 57.2% | — |
| FrontierMath Tier 4 | 25.6% | — |
| MathArena Final-Answer Competitions | 72.9% | — |
| OTIS Mock AIME 2024-2025 | 96.1% | — |
| ProofBench | 16% | — |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Tier 4 (v1) | 14.6% | — |

## Knowledge

- Kimi K2.6: 53.9 (#48)
- Llama 3.2 3B: 29.1 (#232)

| Benchmark | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1491 | 1090 |
| GPQA Diamond | 90.8% | — |
| SimpleQA Verified | 34.9% | — |
| Vectara Hallucination Rate | 10.8% | — |

## Multimodal

- Kimi K2.6: 31.8 (#95)
- Llama 3.2 3B: —

| Benchmark | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Vision | 1282 | — |
| Blueprint-Bench 2 | 3.9% | — |
| Furniture Assembly | 21.7% | — |
| LMArena Document | 1451 | — |

## Multilingual

- Kimi K2.6: 55.1 (#35)
- Llama 3.2 3B: 26.4 (#276)

| Benchmark | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1446 | 1019 |
| LMArena Chinese | 1521 | 1017 |
| LMArena German | 1449 | 1056 |
| LMArena Russian | 1446 | 949 |
| LMArena French | 1471 | — |
| LMArena Japanese | 1442 | — |
| LMArena Korean | 1428 | — |
| LMArena Spanish | 1463 | — |

## Instruction Following

- Kimi K2.6: 76.5 (#40)
- Llama 3.2 3B: 56.3 (#270)

| Benchmark | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1451 | 1089 |

## Long Context

- Kimi K2.6: 44.9 (#50)
- Llama 3.2 3B: 33.4 (#256)

| Benchmark | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1468 | 1101 |

## Writing & Preference

- Kimi K2.6: 68.7 (#25)
- Llama 3.2 3B: 24.9 (#302)

| Benchmark | Kimi K2.6 | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1455 | 1110 |
| LMArena Creative Writing | 1434 | 1094 |
| EQ-Bench Creative Writing | 1725 | 595 |
| LMArena Multi-Turn | 1453 | 1105 |
| EQ-Bench 4 | 1202 | — |

## FAQ

### Is Kimi K2.6 better than Llama 3.2 3B?

Kimi K2.6 is the stronger model overall, scoring 47.5 to 28.4 on the Noometry Index. Llama 3.2 3B costs 14× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.

### Which is cheaper, Kimi K2.6 or Llama 3.2 3B?

Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; Kimi K2.6 lists at $0.95 and $4.

### Is Kimi K2.6 or Llama 3.2 3B better for coding?

Kimi K2.6 scores higher on coding benchmarks: 50.5 versus 27.1 in the Noometry coding category.

### Which has the bigger context window?

Kimi K2.6 does, with 262K tokens against 131K.

### How many benchmarks do Kimi K2.6 and Llama 3.2 3B share?

14 benchmarks have published results for both models. Kimi K2.6 has 51 scored results on Noometry and Llama 3.2 3B has 18.