Model comparison
DeepSeek V4.1 Flash vs Kimi K2.7 Code
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 43.3 on the Noometry Index.
Last verified . 11 shared benchmarks.
Summary
- They share 11 benchmarks with published results for both. DeepSeek V4.1 Flash 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 math, where DeepSeek V4.1 Flash leads 66.7 to 52.9.
- The biggest single-benchmark swing is FrontierMath Tier 4: 26.8% for DeepSeek V4.1 Flash and 12.2% for Kimi K2.7 Code.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 262K.
Side by side
| DeepSeek V4.1 Flash | Kimi K2.7 Code | |
|---|---|---|
| Provider | DeepSeek | Moonshot AI |
| Noometry Index | 52.8 | 43.3 |
| Released | 2026-09-09 | 2026-06-12 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 393K | 262K |
| Input $ / M tokens | $0.15 | $0.95 |
| Output $ / M tokens | $0.60 | $4 |
| Results tracked | 37 | 19 |
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Category by category
Coding DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 52.9 (#32), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena WebDev | 1619 | 1473 |
| SciCode | 51.9% | 47.5% |
| ALE-Bench | 1,092 | 886.23 |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| WeirdML | — | 54.1% |
| LMArena Coding | 1506 | — |
Agentic & Tool Use DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 31.2 (#69), Kimi K2.7 Code: 24.0 (#122)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | 39.5% | 37.6% |
| GBAEval | — | 0.9% |
| GDP.pdf | 19.8% | — |
| Vending-Bench 2 | — | 5,083 |
Reasoning DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 50.2 (#36), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code |
|---|---|---|
| CritPt | 14.3% | 10% |
| Surface Evolver Bench | 46.3% | 48.8% |
| Epoch Capabilities Index | 154.9 | 149.97 |
| SimpleBench | — | 57.9% |
| NYT Connections (extended) | 89.6% | — |
| Chess Puzzles | — | 21% |
| LMArena Hard Prompts | 1483 | — |
| Mystery Game Puzzles | 43% | — |
| DTBench | 89.9% | — |
| LMCA | 47% | — |
Math DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 66.7 (#25), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | 67.4% | 54% |
| FrontierMath Tier 4 | 26.8% | 12.2% |
| OTIS Mock AIME 2024-2025 | 98.3% | 95.6% |
| ProofBench | 54% | — |
| LMArena Math | 1477 | — |
Knowledge DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 57.9 (#38), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 89.8% | 87.9% |
| SimpleQA Verified | — | 36.5% |
| LMArena Expert | 1506 | — |
Multimodal Not comparable
DeepSeek V4.1 Flash: 39.1 (#61), Kimi K2.7 Code: —
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena Vision | 1277 | — |
| Furniture Assembly | 34.2% | — |
Multilingual Not comparable
DeepSeek V4.1 Flash: 55.0 (#35), Kimi K2.7 Code: —
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1448 | — |
| LMArena Chinese | 1497 | — |
| LMArena French | 1452 | — |
| LMArena German | 1484 | — |
| LMArena Japanese | 1412 | — |
| LMArena Korean | 1452 | — |
| LMArena Russian | 1471 | — |
| LMArena Spanish | 1459 | — |
Instruction Following Not comparable
DeepSeek V4.1 Flash: 77.3 (#26), Kimi K2.7 Code: —
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1474 | — |
Long Context Not comparable
DeepSeek V4.1 Flash: 45.2 (#47), Kimi K2.7 Code: —
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena Longer Query | 1475 | — |
Writing & Preference Not comparable
DeepSeek V4.1 Flash: 65.4 (#48), Kimi K2.7 Code: —
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1462 | — |
| LMArena Creative Writing | 1435 | — |
| EQ-Bench Creative Writing | 1540 | — |
| LMArena Multi-Turn | 1457 | — |
Frequently asked questions
Is DeepSeek V4.1 Flash better than Kimi K2.7 Code?
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 43.3 on the Noometry Index.
Which is cheaper, DeepSeek V4.1 Flash or Kimi K2.7 Code?
DeepSeek V4.1 Flash 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 DeepSeek V4.1 Flash or Kimi K2.7 Code better for coding?
DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 42.9 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4.1 Flash does, with 1M tokens against 262K.
How many benchmarks do DeepSeek V4.1 Flash and Kimi K2.7 Code share?
11 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and Kimi K2.7 Code has 19.