Model comparison
DeepSeek V4.1 Flash vs Kimi K2 (Jul 2025)
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 41.2 on the Noometry Index.
Last verified . 20 shared benchmarks.
DeepSeekSummary
- They share 20 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 8 categories and Kimi K2 (Jul 2025) in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4.1 Flash leads 50.2 to 23.3.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.57 / $2.30 for Kimi K2 (Jul 2025).
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 262K.
Side by side
| DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) | |
|---|---|---|
| Provider | DeepSeek | Moonshot AI |
| Noometry Index | 52.8 | 41.2 |
| Released | 2026-09-09 | 2025-07-12 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 393K | 262K |
| Input $ / M tokens | $0.15 | $0.57 |
| Output $ / M tokens | $0.60 | $2.30 |
| Results tracked | 37 | 42 |
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Category by category
Coding DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 52.9 (#32), Kimi K2 (Jul 2025): 42.4 (#102)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Coding | 1506 | 1399 |
| ALE-Bench | 1,092 | 597.5 |
| SWE-bench Verified (bash only) | — | 63.4% |
| Aider Polyglot | — | 59.1% |
| LMArena WebDev | 1619 | — |
| SciCode | 51.9% | — |
| GSO | — | 4.9% |
| WeirdML | — | 42.8% |
Agentic & Tool Use Kimi K2 (Jul 2025) leads
DeepSeek V4.1 Flash: 31.2 (#69), Kimi K2 (Jul 2025): 32.4 (#64)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| Terminal-Bench | — | 35.7% |
| APEX-Agents | 39.5% | — |
| Berkeley Function Calling Leaderboard | — | 59.1% |
| GDP.pdf | 19.8% | — |
| METR Time Horizons | — | 59.2% |
Reasoning DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 50.2 (#36), Kimi K2 (Jul 2025): 23.3 (#179)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Hard Prompts | 1483 | 1384 |
| Epoch Capabilities Index | 154.9 | 146.01 |
| SimpleBench | — | 26.3% |
| Kagi LLM Benchmark | — | 64.4% |
| NYT Connections (extended) | 89.6% | — |
| CritPt | 14.3% | — |
| Mystery Game Puzzles | 43% | — |
| DTBench | 89.9% | — |
| LMCA | 47% | — |
| Surface Evolver Bench | 46.3% | — |
| ForecastBench | — | 60.2 |
Math DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 66.7 (#25), Kimi K2 (Jul 2025): 42.7 (#83)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Math | 1477 | 1397 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 26.8% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 54% | — |
| Omni-MATH | — | 65.4% |
| FrontierMath (Feb 2025 set) | — | 21.4% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 57.9 (#38), Kimi K2 (Jul 2025): 37.3 (#157)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Expert | 1506 | 1365 |
| GPQA Diamond | 89.8% | — |
| MMLU-Pro | — | 81.9% |
| Confabulations | — | 20.4% |
| Vectara Hallucination Rate | — | 17.9% |
| GPQA (HELM) | — | 65.3% |
Multimodal Not comparable
DeepSeek V4.1 Flash: 39.1 (#61), Kimi K2 (Jul 2025): —
| Benchmark | DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Vision | 1277 | — |
| Furniture Assembly | 34.2% | — |
Multilingual DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 55.0 (#35), Kimi K2 (Jul 2025): 49.6 (#130)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Non-English | 1448 | 1372 |
| LMArena Chinese | 1497 | 1415 |
| LMArena French | 1452 | 1379 |
| LMArena German | 1484 | 1387 |
| LMArena Japanese | 1412 | 1349 |
| LMArena Korean | 1452 | 1325 |
| LMArena Russian | 1471 | 1385 |
| LMArena Spanish | 1459 | 1386 |
Instruction Following DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 77.3 (#26), Kimi K2 (Jul 2025): 71.1 (#156)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Instruction Following | 1474 | 1348 |
| IFEval | — | 85% |
Long Context DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 45.2 (#47), Kimi K2 (Jul 2025): 41.2 (#145)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Longer Query | 1475 | 1353 |
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 17.6% |
Writing & Preference DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 65.4 (#48), Kimi K2 (Jul 2025): 62.3 (#78)
| Benchmark | DeepSeek V4.1 Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Text | 1462 | 1380 |
| LMArena Creative Writing | 1435 | 1350 |
| EQ-Bench Creative Writing | 1540 | 1666 |
| LMArena Multi-Turn | 1457 | 1371 |
| Short-Story Creative Writing | — | 85.6% |
| WildBench | — | 86.2% |
Frequently asked questions
Is DeepSeek V4.1 Flash better than Kimi K2 (Jul 2025)?
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 41.2 on the Noometry Index.
Which is cheaper, DeepSeek V4.1 Flash or Kimi K2 (Jul 2025)?
DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Kimi K2 (Jul 2025) lists at $0.57 and $2.30.
Is DeepSeek V4.1 Flash or Kimi K2 (Jul 2025) better for coding?
DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 42.4 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 (Jul 2025) share?
20 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and Kimi K2 (Jul 2025) has 42.