Model comparison
GPT-4 Turbo vs Kimi K2.7 Code
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 30.5 on the Noometry Index.
Last verified . 7 shared benchmarks.
Summary
- They share 7 benchmarks with published results for both. GPT-4 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 52.9 to 9.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.7% for GPT-4 Turbo and 95.6% for Kimi K2.7 Code.
- Kimi K2.7 Code is cheaper at $0.95 / $4 per million input/output tokens, against $10 / $30 for GPT-4 Turbo.
- 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-4 Turbo | Kimi K2.7 Code | |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Noometry Index | 30.5 | 43.3 |
| Released | 2023-11-06 | 2026-06-12 |
| Weights | Proprietary | Open |
| Context window | 128K | 262K |
| Max output | 4K | 262K |
| Input $ / M tokens | $10 | $0.95 |
| Output $ / M tokens | $30 | $4 |
| Results tracked | 36 | 19 |
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Category by category
Coding Kimi K2.7 Code leads
GPT-4 Turbo: 33.8 (#249), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | GPT-4 Turbo | Kimi K2.7 Code |
|---|---|---|
| WeirdML | 18% | 54.1% |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| LMArena WebDev | — | 1473 |
| SciCode | — | 47.5% |
| BigCodeBench Instruct | 48.2% | — |
| LMArena Coding | 1268 | — |
| BigCodeBench Complete | 58.2% | — |
| ALE-Bench | — | 886.23 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73.3% | — |
Agentic & Tool Use Not comparable
GPT-4 Turbo: —, Kimi K2.7 Code: 24.0 (#122)
| Benchmark | GPT-4 Turbo | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | — | 37.6% |
| GBAEval | — | 0.9% |
| METR Time Horizons | 36.7% | — |
| Vending-Bench 2 | — | 5,083 |
Reasoning Kimi K2.7 Code leads
GPT-4 Turbo: 15.3 (#317), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | GPT-4 Turbo | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | 25.1% | 57.9% |
| Chess Puzzles | 6% | 21% |
| Epoch Capabilities Index | 127.25 | 149.97 |
| CritPt | — | 10% |
| LMArena Hard Prompts | 1251 | — |
| DTBench | 61.6% | — |
| LMCA | 9.8% | — |
| Surface Evolver Bench | — | 48.8% |
| ForecastBench | 59.4 | — |
Math Kimi K2.7 Code leads
GPT-4 Turbo: 9.0 (#322), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | GPT-4 Turbo | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0.7% | 54% |
| OTIS Mock AIME 2024-2025 | 6.7% | 95.6% |
| FrontierMath Tier 4 | — | 12.2% |
| LMArena Math | 1272 | — |
| MATH Level 5 | 46.7% | — |
Knowledge Kimi K2.7 Code leads
GPT-4 Turbo: 24.3 (#268), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | GPT-4 Turbo | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 46.6% | 87.9% |
| SimpleQA Verified | — | 36.5% |
| Confabulations | 28.4% | — |
| LMArena Expert | 1223 | — |
| MMLU | 81.3% | — |
Multimodal Not comparable
GPT-4 Turbo: 30.6 (#110), Kimi K2.7 Code: —
| Benchmark | GPT-4 Turbo | Kimi K2.7 Code |
|---|---|---|
| LMArena Vision | 1090 | — |
Multilingual Not comparable
GPT-4 Turbo: 40.5 (#216), Kimi K2.7 Code: —
| Benchmark | GPT-4 Turbo | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1245 | — |
| LMArena Chinese | 1242 | — |
| LMArena French | 1276 | — |
| LMArena German | 1259 | — |
| LMArena Japanese | 1194 | — |
| LMArena Korean | 1187 | — |
| LMArena Russian | 1259 | — |
| LMArena Spanish | 1260 | — |
Instruction Following Not comparable
GPT-4 Turbo: 65.8 (#216), Kimi K2.7 Code: —
| Benchmark | GPT-4 Turbo | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1249 | — |
Long Context Not comparable
GPT-4 Turbo: 38.0 (#206), Kimi K2.7 Code: —
| Benchmark | GPT-4 Turbo | Kimi K2.7 Code |
|---|---|---|
| LMArena Longer Query | 1254 | — |
Writing & Preference Not comparable
GPT-4 Turbo: 47.7 (#206), Kimi K2.7 Code: —
| Benchmark | GPT-4 Turbo | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1272 | — |
| LMArena Creative Writing | 1269 | — |
| LMArena Multi-Turn | 1267 | — |
Frequently asked questions
Is GPT-4 Turbo better than Kimi K2.7 Code?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 30.5 on the Noometry Index.
Which is cheaper, GPT-4 Turbo or Kimi K2.7 Code?
Kimi K2.7 Code is cheaper. It lists at $0.95 per million input tokens and $4 per million output tokens; GPT-4 Turbo lists at $10 and $30.
Is GPT-4 Turbo or Kimi K2.7 Code better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 33.8 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-4 Turbo and Kimi K2.7 Code share?
7 benchmarks have published results for both models. GPT-4 Turbo has 36 scored results on Noometry and Kimi K2.7 Code has 19.