# GPT-3.5-turbo vs Kimi K2.7 Code

> Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 23.1 on the Noometry Index. GPT-3.5-turbo costs 2.3× 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/gpt-3-5-turbo-vs-kimi-k2-7-code
- Last updated: 2026-10-10
- Shared benchmarks: 6

## Summary

- They share 6 benchmarks with published results for both. GPT-3.5-turbo scores higher in 0 categories and Kimi K2.7 Code in 4 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.7 Code leads 52.6 to 6.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 2.2% for GPT-3.5-turbo and 95.6% for Kimi K2.7 Code.
- GPT-3.5-turbo is cheaper at $0.50 / $1.50 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 16K.
- Kimi K2.7 Code has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-3.5-turbo | Kimi K2.7 Code |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Noometry Index | 23.1 | 43.0 |
| Rank | 345 | 90 |
| Context | 16K | 262K |
| Input $/M | $0.50 | $0.95 |
| Output $/M | $1.50 | $4 |
| Weights | Proprietary | Open |

## Coding

- GPT-3.5-turbo: 23.5 (#326)
- Kimi K2.7 Code: 42.4 (#92)

| Benchmark | GPT-3.5-turbo | Kimi K2.7 Code |
|---|---|---|
| WeirdML | 3.5% | 54.1% |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| LMArena WebDev | — | 1473 |
| SciCode | — | 47.5% |
| BigCodeBench Instruct | 39.1% | — |
| LMArena Coding | 1137 | — |
| BigCodeBench Complete | 50.6% | — |
| ALE-Bench | — | 886.23 |
| HumanEval+ | 70.7% | — |
| MBPP+ | 69.7% | — |

## Agentic & Tool Use

- GPT-3.5-turbo: —
- Kimi K2.7 Code: 23.9 (#115)

| Benchmark | GPT-3.5-turbo | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | — | 37.6% |
| GBAEval | — | 0.9% |
| METR Time Horizons | 21.5% | — |
| Vending-Bench 2 | — | 5,083 |

## Reasoning

- GPT-3.5-turbo: 13.3 (#328)
- Kimi K2.7 Code: 38.2 (#56)

| Benchmark | GPT-3.5-turbo | Kimi K2.7 Code |
|---|---|---|
| Chess Puzzles | 0% | 21% |
| Epoch Capabilities Index | 118.48 | 150.01 |
| SimpleBench | — | 57.9% |
| CritPt | — | 10% |
| LMArena Hard Prompts | 1108 | — |
| Mystery Game Puzzles | 3% | — |
| DTBench | 48.5% | — |
| LMCA | 9.7% | — |
| Surface Evolver Bench | — | 48.8% |
| Adversarial NLI | 58.1% | — |
| BIG-Bench Hard | 61.6% | — |
| CommonsenseQA 2.0 | 57% | — |
| ForecastBench | 50.4 | — |
| WinoGrande | 81.6% | — |

## Math

- GPT-3.5-turbo: 6.0 (#321)
- Kimi K2.7 Code: 52.6 (#39)

| Benchmark | GPT-3.5-turbo | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0% | 54% |
| OTIS Mock AIME 2024-2025 | 2.2% | 95.6% |
| FrontierMath Tier 4 | — | 12.2% |
| LMArena Math | 1142 | — |
| MATH Level 5 | 15.9% | — |
| GSM8K | 57.8% | — |

## Knowledge

- GPT-3.5-turbo: 10.0 (#298)
- Kimi K2.7 Code: 53.6 (#51)

| Benchmark | GPT-3.5-turbo | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 28% | 87.9% |
| SimpleQA Verified | — | 36.5% |
| LMArena Expert | 1070 | — |
| ARC (AI2) Challenge | 87.4% | — |
| BoolQ | 87% | — |
| MMLU | 71.4% | — |
| OpenBookQA | 86% | — |
| TriviaQA | 85.8% | — |

## Multilingual

- GPT-3.5-turbo: 31.6 (#253)
- Kimi K2.7 Code: —

| Benchmark | GPT-3.5-turbo | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1108 | — |
| LMArena Chinese | 1075 | — |
| LMArena French | 1117 | — |
| LMArena German | 1090 | — |
| LMArena Japanese | 1043 | — |
| LMArena Korean | 1019 | — |
| LMArena Russian | 1123 | — |
| LMArena Spanish | 1120 | — |

## Instruction Following

- GPT-3.5-turbo: 58.1 (#257)
- Kimi K2.7 Code: —

| Benchmark | GPT-3.5-turbo | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1119 | — |

## Long Context

- GPT-3.5-turbo: 34.0 (#249)
- Kimi K2.7 Code: —

| Benchmark | GPT-3.5-turbo | Kimi K2.7 Code |
|---|---|---|
| LMArena Longer Query | 1121 | — |

## Writing & Preference

- GPT-3.5-turbo: 25.4 (#300)
- Kimi K2.7 Code: —

| Benchmark | GPT-3.5-turbo | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1125 | — |
| LMArena Creative Writing | 1092 | — |
| EQ-Bench Creative Writing | 451 | — |
| LMArena Multi-Turn | 1117 | — |

## FAQ

### Is GPT-3.5-turbo better than Kimi K2.7 Code?

Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 23.1 on the Noometry Index. GPT-3.5-turbo costs 2.3× 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, GPT-3.5-turbo or Kimi K2.7 Code?

GPT-3.5-turbo is cheaper. It lists at $0.50 per million input tokens and $1.50 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

### Is GPT-3.5-turbo or Kimi K2.7 Code better for coding?

Kimi K2.7 Code scores higher on coding benchmarks: 42.4 versus 23.5 in the Noometry coding category.

### Which has the bigger context window?

Kimi K2.7 Code does, with 262K tokens against 16K.

### How many benchmarks do GPT-3.5-turbo and Kimi K2.7 Code share?

6 benchmarks have published results for both models. GPT-3.5-turbo has 44 scored results on Noometry and Kimi K2.7 Code has 19.