# GPT-5 Nano vs Kimi K2.7 Code

> Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 33.4 on the Noometry Index. GPT-5 Nano costs 12× 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-5-nano-vs-kimi-k2-7-code
- Last updated: 2026-10-10
- Shared benchmarks: 9

## Summary

- They share 9 benchmarks with published results for both. GPT-5 Nano scores higher in 1 category and Kimi K2.7 Code in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.7 Code leads 52.6 to 29.1.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 20% for GPT-5 Nano and 54% for Kimi K2.7 Code.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- GPT-5 Nano accepts more context: 400K tokens versus 262K.
- Kimi K2.7 Code has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Noometry Index | 33.4 | 43.0 |
| Rank | 234 | 90 |
| Context | 400K | 262K |
| Input $/M | $0.05 | $0.95 |
| Output $/M | $0.40 | $4 |
| Weights | Proprietary | Open |

## Coding

- GPT-5 Nano: 33.3 (#248)
- Kimi K2.7 Code: 42.4 (#92)

| Benchmark | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| WeirdML | 38.1% | 54.1% |
| ALE-Bench | 718.67 | 886.23 |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| SWE-bench Verified (bash only) | 34.8% | — |
| LMArena WebDev | — | 1473 |
| SciCode | — | 47.5% |
| LMArena Coding | 1351 | — |

## Agentic & Tool Use

- GPT-5 Nano: 25.6 (#99)
- Kimi K2.7 Code: 23.9 (#115)

| Benchmark | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| APEX-Agents | — | 37.6% |
| Berkeley Function Calling Leaderboard | 51.5% | — |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 5,083 |

## Reasoning

- GPT-5 Nano: 16.4 (#291)
- Kimi K2.7 Code: 38.2 (#56)

| Benchmark | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| Chess Puzzles | 27% | 21% |
| Epoch Capabilities Index | 139.39 | 150.01 |
| ARC-AGI-2 | 2.6% | — |
| SimpleBench | — | 57.9% |
| Kagi LLM Benchmark | 62.2% | — |
| ARC-AGI-1 | 20.7% | — |
| CritPt | — | 10% |
| LMArena Hard Prompts | 1329 | — |
| Mystery Game Puzzles | 9% | — |
| DTBench | 62.7% | — |
| LMCA | 7.9% | — |
| Surface Evolver Bench | — | 48.8% |
| ForecastBench | 59.1 | — |

## Math

- GPT-5 Nano: 29.1 (#221)
- Kimi K2.7 Code: 52.6 (#39)

| Benchmark | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | 20% | 54% |
| FrontierMath Tier 4 | 2.4% | 12.2% |
| OTIS Mock AIME 2024-2025 | 81.1% | 95.6% |
| ProofBench | 12% | — |
| Omni-MATH | 54.6% | — |
| LMArena Math | 1318 | — |
| MATH Level 5 | 95.2% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GPT-5 Nano: 35.8 (#166)
- Kimi K2.7 Code: 53.6 (#51)

| Benchmark | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 69.4% | 87.9% |
| SimpleQA Verified | 11.7% | 36.5% |
| MMLU-Pro | 77.8% | — |
| Vectara Hallucination Rate | 10.5% | — |
| GPQA (HELM) | 67.9% | — |
| LMArena Expert | 1321 | — |

## Multimodal

- GPT-5 Nano: 30.7 (#102)
- Kimi K2.7 Code: —

| Benchmark | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| LMArena Vision | 1159 | — |
| VPCT | 37.2% | — |

## Multilingual

- GPT-5 Nano: 45.5 (#167)
- Kimi K2.7 Code: —

| Benchmark | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1313 | — |
| LMArena Chinese | 1355 | — |
| LMArena German | 1329 | — |
| LMArena Korean | 1269 | — |
| LMArena Russian | 1296 | — |
| LMArena Spanish | 1360 | — |

## Instruction Following

- GPT-5 Nano: 75.2 (#72)
- Kimi K2.7 Code: —

| Benchmark | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| IFEval | 93.2% | — |
| LMArena Instruction Following | 1306 | — |

## Long Context

- GPT-5 Nano: 31.3 (#275)
- Kimi K2.7 Code: —

| Benchmark | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| Fiction.LiveBench | 44.4% | — |
| LMArena Longer Query | 1313 | — |

## Writing & Preference

- GPT-5 Nano: 39.4 (#244)
- Kimi K2.7 Code: —

| Benchmark | GPT-5 Nano | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1320 | — |
| LMArena Creative Writing | 1250 | — |
| EQ-Bench Creative Writing | 705 | — |
| WildBench | 80.6% | — |
| LMArena Multi-Turn | 1311 | — |

## FAQ

### Is GPT-5 Nano better than Kimi K2.7 Code?

Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 33.4 on the Noometry Index. GPT-5 Nano costs 12× 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-5 Nano or Kimi K2.7 Code?

GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

### Is GPT-5 Nano or Kimi K2.7 Code better for coding?

Kimi K2.7 Code scores higher on coding benchmarks: 42.4 versus 33.3 in the Noometry coding category.

### Which has the bigger context window?

GPT-5 Nano does, with 400K tokens against 262K.

### How many benchmarks do GPT-5 Nano and Kimi K2.7 Code share?

9 benchmarks have published results for both models. GPT-5 Nano has 48 scored results on Noometry and Kimi K2.7 Code has 19.