Model comparison
GPT-4o mini vs Kimi K2.7 Code
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 25.5 on the Noometry Index. GPT-4o mini costs 6.5× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Last verified . 8 shared benchmarks.
Summary
- They share 8 benchmarks with published results for both. GPT-4o 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 math, where Kimi K2.7 Code leads 52.9 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.9% for GPT-4o mini and 95.6% for Kimi K2.7 Code.
- GPT-4o mini is cheaper at $0.15 / $0.60 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.
- Kimi K2.7 Code has downloadable open weights; the other is API-only.
Side by side
| GPT-4o mini | Kimi K2.7 Code | |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Noometry Index | 25.5 | 43.3 |
| Released | 2024-07-18 | 2026-06-12 |
| Weights | Proprietary | Open |
| Context window | 128K | 262K |
| Max output | 16K | 262K |
| Input $ / M tokens | $0.15 | $0.95 |
| Output $ / M tokens | $0.60 | $4 |
| Results tracked | 60 | 19 |
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Category by category
Coding Kimi K2.7 Code leads
GPT-4o mini: 22.0 (#335), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | GPT-4o mini | Kimi K2.7 Code |
|---|---|---|
| WeirdML | 11.8% | 54.1% |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| Aider Polyglot | 3.6% | — |
| LMArena WebDev | — | 1473 |
| SciCode | — | 47.5% |
| BigCodeBench Instruct | 46.1% | — |
| LiveBench Coding | 43.1% | — |
| LMArena Coding | 1290 | — |
| BigCodeBench Complete | 57.4% | — |
| ALE-Bench | — | 886.23 |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use GPT-4o mini leads
GPT-4o mini: 27.5 (#101), Kimi K2.7 Code: 24.0 (#122)
| Benchmark | GPT-4o mini | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | — | 37.6% |
| BALROG | 17.4% | — |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 5,083 |
Reasoning Kimi K2.7 Code leads
GPT-4o mini: 8.7 (#347), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | GPT-4o mini | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | 10.7% | 57.9% |
| Chess Puzzles | 0% | 21% |
| Epoch Capabilities Index | 126.56 | 149.97 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 28.8% | — |
| CritPt | — | 10% |
| LiveBench Reasoning | 32.8% | — |
| LMArena Hard Prompts | 1267 | — |
| Mystery Game Puzzles | 12% | — |
| DTBench | 54.4% | — |
| LiveBench Data Analysis | 50% | — |
| LMCA | 10.4% | — |
| Surface Evolver Bench | — | 48.8% |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |
Math Kimi K2.7 Code leads
GPT-4o mini: 10.4 (#314), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | GPT-4o mini | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0.7% | 54% |
| OTIS Mock AIME 2024-2025 | 6.9% | 95.6% |
| FrontierMath Tier 4 | — | 12.2% |
| Omni-MATH | 28% | — |
| LiveBench Math | 36.3% | — |
| LMArena Math | 1267 | — |
| MATH Level 5 | 52.6% | — |
| GSM8K | 91.3% | — |
Knowledge Kimi K2.7 Code leads
GPT-4o mini: 17.7 (#284), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | GPT-4o mini | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 37.7% | 87.9% |
| SimpleQA Verified | 8.3% | 36.5% |
| MMLU-Pro | 60.3% | — |
| Confabulations | 37.2% | — |
| GPQA (HELM) | 36.8% | — |
| LMArena Expert | 1235 | — |
| BoolQ | 88.7% | — |
| MMLU | 81.8% | — |
Multimodal Not comparable
GPT-4o mini: 25.9 (#122), Kimi K2.7 Code: —
| Benchmark | GPT-4o mini | Kimi K2.7 Code |
|---|---|---|
| LMArena Vision | 1066 | — |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |
Multilingual Not comparable
GPT-4o mini: 42.0 (#199), Kimi K2.7 Code: —
| Benchmark | GPT-4o mini | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1266 | — |
| LMArena Chinese | 1265 | — |
| LMArena French | 1297 | — |
| LMArena German | 1272 | — |
| LMArena Japanese | 1216 | — |
| LMArena Korean | 1195 | — |
| LMArena Russian | 1275 | — |
| LMArena Spanish | 1276 | — |
Instruction Following Not comparable
GPT-4o mini: 61.9 (#239), Kimi K2.7 Code: —
| Benchmark | GPT-4o mini | Kimi K2.7 Code |
|---|---|---|
| LiveBench Instruction Following | 56.8% | — |
| IFEval | 78.2% | — |
| LMArena Instruction Following | 1258 | — |
Long Context Not comparable
GPT-4o mini: 39.1 (#186), Kimi K2.7 Code: —
| Benchmark | GPT-4o mini | Kimi K2.7 Code |
|---|---|---|
| LMArena Longer Query | 1289 | — |
Writing & Preference Not comparable
GPT-4o mini: 39.5 (#248), Kimi K2.7 Code: —
| Benchmark | GPT-4o mini | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1286 | — |
| LMArena Creative Writing | 1268 | — |
| Short-Story Creative Writing | 67.2% | — |
| EQ-Bench Creative Writing | 873 | — |
| WildBench | 79.1% | — |
| LMArena Multi-Turn | 1285 | — |
| LiveBench Language | 28.6% | — |
Frequently asked questions
Is GPT-4o mini better than Kimi K2.7 Code?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 25.5 on the Noometry Index. GPT-4o mini costs 6.5× 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-4o mini or Kimi K2.7 Code?
GPT-4o mini is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is GPT-4o mini or Kimi K2.7 Code better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 22.0 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 GPT-4o mini and Kimi K2.7 Code share?
8 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and Kimi K2.7 Code has 19.