# Kimi K2.7 Code vs Llama-3.3-70B-Instruct

> Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 11× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/kimi-k2-7-code-vs-llama-3-3-70b-instruct
- Last updated: 2026-10-11
- Shared benchmarks: 7

## Summary

- They share 7 benchmarks with published results for both. Kimi K2.7 Code scores higher in 4 categories and Llama-3.3-70B-Instruct in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.7 Code leads 52.9 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 95.6% for Kimi K2.7 Code and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 128K.

## Snapshot

| | Kimi K2.7 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | Moonshot AI | Meta |
| Noometry Index | 43.3 | 30.6 |
| Rank | 94 | 291 |
| Context | 262K | 128K |
| Input $/M | $0.95 | $0.10 |
| Output $/M | $4 | $0.32 |
| Weights | Open | Open |

## Coding

- Kimi K2.7 Code: 42.9 (#95)
- Llama-3.3-70B-Instruct: 31.0 (#290)

| Benchmark | Kimi K2.7 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 47.5% | 26% |
| WeirdML | 54.1% | 14.4% |
| DeepSWE | 30.5% | — |
| FrontierCode | 30.1% | — |
| LMArena WebDev | 1473 | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| LMArena Coding | — | 1268 |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 886.23 | — |

## Agentic & Tool Use

- Kimi K2.7 Code: 24.0 (#122)
- Llama-3.3-70B-Instruct: 25.8 (#105)

| Benchmark | Kimi K2.7 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| APEX-Agents | 37.6% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| GBAEval | 0.9% | — |
| Vending-Bench 2 | 5,083 | — |

## Reasoning

- Kimi K2.7 Code: 39.0 (#61)
- Llama-3.3-70B-Instruct: 14.1 (#327)

| Benchmark | Kimi K2.7 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 57.9% | 19.9% |
| CritPt | 10% | 0% |
| Epoch Capabilities Index | 149.97 | 127.33 |
| Chess Puzzles | 21% | — |
| LiveBench Reasoning | — | 50.8% |
| LMArena Hard Prompts | — | 1257 |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Surface Evolver Bench | 48.8% | — |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |

## Math

- Kimi K2.7 Code: 52.9 (#48)
- Llama-3.3-70B-Instruct: 15.3 (#298)

| Benchmark | Kimi K2.7 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 95.6% | 5.1% |
| FrontierMath (Tiers 1-3) | 54% | — |
| FrontierMath Tier 4 | 12.2% | — |
| LiveBench Math | — | 42.2% |
| LMArena Math | — | 1267 |
| MATH Level 5 | — | 41.6% |

## Knowledge

- Kimi K2.7 Code: 53.5 (#57)
- Llama-3.3-70B-Instruct: 30.6 (#226)

| Benchmark | Kimi K2.7 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 87.9% | 47.4% |
| SimpleQA Verified | 36.5% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| LMArena Expert | — | 1225 |
| MMLU | — | 86.3% |

## Multilingual

- Kimi K2.7 Code: —
- Llama-3.3-70B-Instruct: 39.9 (#220)

| Benchmark | Kimi K2.7 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | — | 1236 |
| LMArena Chinese | — | 1217 |
| LMArena French | — | 1281 |
| LMArena German | — | 1251 |
| LMArena Japanese | — | 1150 |
| LMArena Korean | — | 1143 |
| LMArena Russian | — | 1252 |
| LMArena Spanish | — | 1270 |

## Instruction Following

- Kimi K2.7 Code: —
- Llama-3.3-70B-Instruct: 71.1 (#157)

| Benchmark | Kimi K2.7 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| LiveBench Instruction Following | — | 82.7% |
| LMArena Instruction Following | — | 1242 |

## Long Context

- Kimi K2.7 Code: —
- Llama-3.3-70B-Instruct: 26.4 (#295)

| Benchmark | Kimi K2.7 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | — | 33.3% |
| LMArena Longer Query | — | 1256 |

## Writing & Preference

- Kimi K2.7 Code: —
- Llama-3.3-70B-Instruct: 47.6 (#207)

| Benchmark | Kimi K2.7 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | — | 1274 |
| LMArena Creative Writing | — | 1250 |
| LMArena Multi-Turn | — | 1280 |
| LiveBench Language | — | 39.2% |

## FAQ

### Is Kimi K2.7 Code better than Llama-3.3-70B-Instruct?

Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 11× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.

### Which is cheaper, Kimi K2.7 Code or Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

### Is Kimi K2.7 Code or Llama-3.3-70B-Instruct better for coding?

Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 31.0 in the Noometry coding category.

### Which has the bigger context window?

Kimi K2.7 Code does, with 262K tokens against 128K.

### How many benchmarks do Kimi K2.7 Code and Llama-3.3-70B-Instruct share?

7 benchmarks have published results for both models. Kimi K2.7 Code has 19 scored results on Noometry and Llama-3.3-70B-Instruct has 43.