# GPT-5 Mini vs Kimi K2.7 Code

> Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 41.8 on the Noometry Index. GPT-5 Mini costs 2.5× 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-mini-vs-kimi-k2-7-code
- Last updated: 2026-10-11
- Shared benchmarks: 12

## Summary

- They share 12 benchmarks with published results for both. GPT-5 Mini 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 reasoning, where Kimi K2.7 Code leads 39.0 to 23.9.
- The biggest single-benchmark swing is SimpleQA Verified: 21.6% for GPT-5 Mini and 36.5% for Kimi K2.7 Code.
- GPT-5 Mini is cheaper at $0.25 / $2 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- GPT-5 Mini accepts more context: 400K tokens versus 262K.
- Kimi K2.7 Code has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Noometry Index | 41.8 | 43.3 |
| Rank | 128 | 94 |
| Context | 400K | 262K |
| Input $/M | $0.25 | $0.95 |
| Output $/M | $2 | $4 |
| Weights | Proprietary | Open |

## Coding

- GPT-5 Mini: 40.1 (#146)
- Kimi K2.7 Code: 42.9 (#95)

| Benchmark | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| SciCode | 39.2% | 47.5% |
| WeirdML | 52.7% | 54.1% |
| ALE-Bench | 799.77 | 886.23 |
| SWE-bench Verified | 64.7% | — |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| SWE-bench Verified (bash only) | 59.8% | — |
| LMArena WebDev | — | 1473 |
| SWE-bench Multilingual | 39.7% | — |
| LMArena Coding | 1406 | — |
| AlgoTune | 1.38 | — |

## Agentic & Tool Use

- GPT-5 Mini: 31.1 (#70)
- Kimi K2.7 Code: 24.0 (#122)

| Benchmark | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| Vending-Bench 2 | -31.18 | 5,083 |
| Terminal-Bench | 34.8% | — |
| APEX-Agents | — | 37.6% |
| Berkeley Function Calling Leaderboard | 55.5% | — |
| GBAEval | — | 0.9% |

## Reasoning

- GPT-5 Mini: 23.9 (#168)
- Kimi K2.7 Code: 39.0 (#61)

| Benchmark | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| CritPt | 0% | 10% |
| Chess Puzzles | 30% | 21% |
| Epoch Capabilities Index | 145.52 | 149.97 |
| ARC-AGI-2 | 4.4% | — |
| SimpleBench | — | 57.9% |
| Kagi LLM Benchmark | 70.3% | — |
| ARC-AGI-1 | 54.3% | — |
| EnigmaEval | 8.2% | — |
| LMArena Hard Prompts | 1380 | — |
| Mystery Game Puzzles | 10% | — |
| DTBench | 80.5% | — |
| LMCA | 34.2% | — |
| Surface Evolver Bench | — | 48.8% |
| ForecastBench | 61 | — |

## Math

- GPT-5 Mini: 46.7 (#69)
- Kimi K2.7 Code: 52.9 (#48)

| Benchmark | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | 46.7% | 54% |
| FrontierMath Tier 4 | 12.2% | 12.2% |
| OTIS Mock AIME 2024-2025 | 86.7% | 95.6% |
| ProofBench | 9% | — |
| Omni-MATH | 72.2% | — |
| LMArena Math | 1378 | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |

## Knowledge

- GPT-5 Mini: 45.6 (#86)
- Kimi K2.7 Code: 53.5 (#57)

| Benchmark | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 75% | 87.9% |
| SimpleQA Verified | 21.6% | 36.5% |
| Humanity's Last Exam | 19.4% | — |
| MMLU-Pro | 83.5% | — |
| Confabulations | 13.3% | — |
| Vectara Hallucination Rate | 12.9% | — |
| GPQA (HELM) | 75.6% | — |
| LMArena Expert | 1379 | — |

## Multimodal

- GPT-5 Mini: 35.6 (#85)
- Kimi K2.7 Code: —

| Benchmark | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| LMArena Vision | 1202 | — |
| VPCT | 40.2% | — |

## Multilingual

- GPT-5 Mini: 48.9 (#137)
- Kimi K2.7 Code: —

| Benchmark | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1363 | — |
| LMArena Chinese | 1385 | — |
| LMArena French | 1386 | — |
| LMArena German | 1366 | — |
| LMArena Japanese | 1341 | — |
| LMArena Korean | 1308 | — |
| LMArena Russian | 1362 | — |
| LMArena Spanish | 1355 | — |

## Instruction Following

- GPT-5 Mini: 76.2 (#46)
- Kimi K2.7 Code: —

| Benchmark | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| IFEval | 92.7% | — |
| LMArena Instruction Following | 1357 | — |

## Long Context

- GPT-5 Mini: 41.9 (#132)
- Kimi K2.7 Code: —

| Benchmark | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| Fiction.LiveBench | 69.4% | — |
| LMArena Longer Query | 1355 | — |

## Writing & Preference

- GPT-5 Mini: 55.2 (#148)
- Kimi K2.7 Code: —

| Benchmark | GPT-5 Mini | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1373 | — |
| LMArena Creative Writing | 1325 | — |
| Short-Story Creative Writing | 83.1% | — |
| EQ-Bench Creative Writing | 1313 | — |
| WildBench | 85.5% | — |
| LMArena Multi-Turn | 1363 | — |

## FAQ

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

Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 41.8 on the Noometry Index. GPT-5 Mini costs 2.5× 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 Mini or Kimi K2.7 Code?

GPT-5 Mini is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

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

Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 40.1 in the Noometry coding category.

### Which has the bigger context window?

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

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

12 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Kimi K2.7 Code has 19.