Model comparison
DeepSeek-V3.2-Speciale vs Kimi K2.6
Kimi K2.6 is the stronger model overall, scoring 47.5 to 39.1 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.0× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
DeepSeekSummary
- They share 2 benchmarks with published results for both. DeepSeek-V3.2-Speciale scores higher in 0 categories and Kimi K2.6 in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Kimi K2.6 leads 68.7 to 46.1.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 55.9% for Kimi K2.6.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $0.95 / $4 for Kimi K2.6.
- Kimi K2.6 accepts more context: 262K tokens versus 128K.
Side by side
| DeepSeek-V3.2-Speciale | Kimi K2.6 | |
|---|---|---|
| Provider | DeepSeek | Moonshot AI |
| Noometry Index | 39.1 | 47.5 |
| Released | 2025-12-01 | 2026-04-20 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 128K | 262K |
| Input $ / M tokens | $0.58 | $0.95 |
| Output $ / M tokens | $1.68 | $4 |
| Results tracked | 3 | 51 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Kimi K2.6 leads
DeepSeek-V3.2-Speciale: 39.9 (#136), Kimi K2.6: 50.5 (#41)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.6 |
|---|---|---|
| WeirdML | 46.7% | 55.9% |
| SWE-bench Verified | — | 76.7% |
| LMArena WebDev | — | 1509 |
| SciCode | — | 53.5% |
| LMArena Coding | — | 1488 |
| ALE-Bench | — | 1,093 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, Kimi K2.6: 21.8 (#128)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.6 |
|---|---|---|
| OSWorld 2.0 | — | 4.6% |
| ExploitBench | — | 18.4% |
| GBAEval | — | 0.9% |
| GDP.pdf | — | 12% |
| Vending-Bench 2 | — | 6,205 |
Reasoning Kimi K2.6 leads
DeepSeek-V3.2-Speciale: 32.1 (#65), Kimi K2.6: 39.6 (#51)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.6 |
|---|---|---|
| SimpleBench | 52.6% | — |
| NYT Connections (extended) | — | 87.2% |
| CritPt | — | 8% |
| Chess Puzzles | — | 26% |
| EBR-Bench | — | 2.4% |
| LMArena Hard Prompts | — | 1470 |
| Mystery Game Puzzles | — | 18% |
| DTBench | — | 90.9% |
| LMCA | — | 37.3% |
| Epoch Capabilities Index | — | 151.03 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Kimi K2.6: 56.8 (#34)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.6 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 57.2% |
| FrontierMath Tier 4 | — | 25.6% |
| MathArena Final-Answer Competitions | — | 72.9% |
| OTIS Mock AIME 2024-2025 | — | 96.1% |
| ProofBench | — | 16% |
| LMArena Math | — | 1475 |
| FrontierMath (Feb 2025 set) | — | 39% |
| FrontierMath Tier 4 (v1) | — | 14.6% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Kimi K2.6: 53.9 (#48)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.6 |
|---|---|---|
| GPQA Diamond | — | 90.8% |
| SimpleQA Verified | — | 34.9% |
| Vectara Hallucination Rate | — | 10.8% |
| LMArena Expert | — | 1491 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, Kimi K2.6: 31.8 (#95)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.6 |
|---|---|---|
| LMArena Vision | — | 1282 |
| Blueprint-Bench 2 | — | 3.9% |
| Furniture Assembly | — | 21.7% |
| LMArena Document | — | 1451 |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Kimi K2.6: 55.1 (#35)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.6 |
|---|---|---|
| LMArena Non-English | — | 1446 |
| LMArena Chinese | — | 1521 |
| LMArena French | — | 1471 |
| LMArena German | — | 1449 |
| LMArena Japanese | — | 1442 |
| LMArena Korean | — | 1428 |
| LMArena Russian | — | 1446 |
| LMArena Spanish | — | 1463 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Kimi K2.6: 76.5 (#40)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.6 |
|---|---|---|
| LMArena Instruction Following | — | 1451 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Kimi K2.6: 44.9 (#50)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.6 |
|---|---|---|
| LMArena Longer Query | — | 1468 |
Writing & Preference Kimi K2.6 leads
DeepSeek-V3.2-Speciale: 46.1 (#217), Kimi K2.6: 68.7 (#25)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.6 |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 1725 |
| LMArena Text | — | 1455 |
| LMArena Creative Writing | — | 1434 |
| EQ-Bench 4 | — | 1202 |
| LMArena Multi-Turn | — | 1453 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Kimi K2.6?
Kimi K2.6 is the stronger model overall, scoring 47.5 to 39.1 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.0× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or Kimi K2.6?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; Kimi K2.6 lists at $0.95 and $4.
Is DeepSeek-V3.2-Speciale or Kimi K2.6 better for coding?
Kimi K2.6 scores higher on coding benchmarks: 50.5 versus 39.9 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.6 does, with 262K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and Kimi K2.6 share?
2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Kimi K2.6 has 51.