Model comparison
GPT-5.4 vs Kimi K2.7 Code
GPT-5.4 is the stronger model overall, scoring 59.4 to 43.3 on the Noometry Index. Kimi K2.7 Code costs 3.3× less per token, which makes it the better buy when GPT-5.4's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
Summary
- They share 16 benchmarks with published results for both. GPT-5.4 scores higher in 5 categories and Kimi K2.7 Code in 0 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.4 leads 61.8 to 39.0.
- The biggest single-benchmark swing is GBAEval: 45.1% for GPT-5.4 and 0.9% for Kimi K2.7 Code.
- Kimi K2.7 Code is cheaper at $0.95 / $4 per million input/output tokens, against $2.50 / $15 for GPT-5.4.
- GPT-5.4 accepts more context: 1.05M tokens versus 262K.
- Kimi K2.7 Code has downloadable open weights; the other is API-only.
Side by side
| GPT-5.4 | Kimi K2.7 Code | |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Noometry Index | 59.4 | 43.3 |
| Released | 2026-03-05 | 2026-06-12 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 128K | 262K |
| Input $ / M tokens | $2.50 | $0.95 |
| Output $ / M tokens | $15 | $4 |
| Results tracked | 68 | 19 |
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Category by category
Coding GPT-5.4 leads
GPT-5.4: 52.6 (#33), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | GPT-5.4 | Kimi K2.7 Code |
|---|---|---|
| DeepSWE | 51.8% | 30.5% |
| LMArena WebDev | 1465 | 1473 |
| SciCode | 56.6% | 47.5% |
| WeirdML | 77.7% | 54.1% |
| ALE-Bench | 1,607 | 886.23 |
| SWE-bench Verified | 76.9% | — |
| FrontierCode | — | 30.1% |
| GSO | 31.4% | — |
| LMArena Coding | 1497 | — |
| MirrorCode | 15.6% | — |
| AlgoTune | 1.85 | — |
Agentic & Tool Use GPT-5.4 leads
GPT-5.4: 46.5 (#13), Kimi K2.7 Code: 24.0 (#122)
| Benchmark | GPT-5.4 | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | 52.4% | 37.6% |
| GBAEval | 45.1% | 0.9% |
| Vending-Bench 2 | 6,144 | 5,083 |
| Terminal-Bench | 81.8% | — |
| τ²-bench Banking | 39.4% | — |
| DeepResearch Bench | 35.1% | — |
| PostTrainBench | 19% | — |
| LMArena Search | 1197 | — |
| METR Time Horizons | 74.3% | — |
Reasoning GPT-5.4 leads
GPT-5.4: 61.8 (#19), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | GPT-5.4 | Kimi K2.7 Code |
|---|---|---|
| CritPt | 23.4% | 10% |
| Chess Puzzles | 44% | 21% |
| Epoch Capabilities Index | 156.81 | 149.97 |
| ARC-AGI-2 | 74% | — |
| SimpleBench | — | 57.9% |
| Kagi LLM Benchmark | 63.8% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 93.7% | — |
| EnigmaEval | 16% | — |
| Thematic Generalization | 80% | — |
| EBR-Bench | 25.4% | — |
| LMArena Hard Prompts | 1485 | — |
| Mystery Game Puzzles | 37% | — |
| DTBench | 94.4% | — |
| LMCA | 52% | — |
| Surface Evolver Bench | — | 48.8% |
| ForecastBench | 59.5 | — |
Math GPT-5.4 leads
GPT-5.4: 73.5 (#19), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | GPT-5.4 | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | 78.6% | 54% |
| FrontierMath Tier 4 | 49% | 12.2% |
| OTIS Mock AIME 2024-2025 | 97.8% | 95.6% |
| MathArena Final-Answer Competitions | 83.1% | — |
| ProofBench | 56% | — |
| LMArena Math | 1488 | — |
| FrontierMath (Feb 2025 set) | 47.6% | — |
| FrontierMath Tier 4 (v1) | 27.1% | — |
Knowledge GPT-5.4 leads
GPT-5.4: 65.3 (#14), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | GPT-5.4 | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 93.3% | 87.9% |
| SimpleQA Verified | 45.1% | 36.5% |
| Humanity's Last Exam | 36.2% | — |
| Vectara Hallucination Rate | 7% | — |
| LMArena Expert | 1507 | — |
Multimodal Not comparable
GPT-5.4: 43.7 (#20), Kimi K2.7 Code: —
| Benchmark | GPT-5.4 | Kimi K2.7 Code |
|---|---|---|
| LMArena Vision | 1303 | — |
| Blueprint-Bench 2 | 27.1% | — |
| Furniture Assembly | 37.5% | — |
| LMArena Document | 1471 | — |
Multilingual Not comparable
GPT-5.4: 56.2 (#23), Kimi K2.7 Code: —
| Benchmark | GPT-5.4 | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1465 | — |
| LMArena Chinese | 1519 | — |
| LMArena French | 1493 | — |
| LMArena German | 1472 | — |
| LMArena Japanese | 1485 | — |
| LMArena Korean | 1448 | — |
| LMArena Russian | 1480 | — |
| LMArena Spanish | 1454 | — |
Instruction Following Not comparable
GPT-5.4: 77.1 (#27), Kimi K2.7 Code: —
| Benchmark | GPT-5.4 | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1469 | — |
Long Context Not comparable
GPT-5.4: 50.3 (#8), Kimi K2.7 Code: —
| Benchmark | GPT-5.4 | Kimi K2.7 Code |
|---|---|---|
| CL-bench | 27.9% | — |
| CL-bench Life | 21.7% | — |
| LMArena Longer Query | 1473 | — |
Writing & Preference Not comparable
GPT-5.4: 71.9 (#17), Kimi K2.7 Code: —
| Benchmark | GPT-5.4 | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1469 | — |
| LMArena Creative Writing | 1439 | — |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1272 | — |
| LMArena Multi-Turn | 1482 | — |
Frequently asked questions
Is GPT-5.4 better than Kimi K2.7 Code?
GPT-5.4 is the stronger model overall, scoring 59.4 to 43.3 on the Noometry Index. Kimi K2.7 Code costs 3.3× less per token, which makes it the better buy when GPT-5.4's lead doesn't matter for your workload.
Which is cheaper, GPT-5.4 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-5.4 lists at $2.50 and $15.
Is GPT-5.4 or Kimi K2.7 Code better for coding?
GPT-5.4 scores higher on coding benchmarks: 52.6 versus 42.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 does, with 1.05M tokens against 262K.
How many benchmarks do GPT-5.4 and Kimi K2.7 Code share?
16 benchmarks have published results for both models. GPT-5.4 has 68 scored results on Noometry and Kimi K2.7 Code has 19.