Model comparison
DeepSeek-V3.2-Speciale vs Kimi K2.7 Code
Kimi K2.7 Code is the stronger model overall, scoring 43.0 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 K2.7 Code'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 K2.7 Code in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Kimi K2.7 Code leads 38.2 to 32.1.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 54.1% for Kimi K2.7 Code.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 128K.
Side by side
| DeepSeek-V3.2-Speciale | Kimi K2.7 Code | |
|---|---|---|
| Provider | DeepSeek | Moonshot AI |
| Noometry Index | 39.1 | 43.0 |
| Released | 2025-12-01 | 2026-06-12 |
| 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 | 19 |
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Category by category
Coding Kimi K2.7 Code leads
DeepSeek-V3.2-Speciale: 39.9 (#136), Kimi K2.7 Code: 42.4 (#92)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.7 Code |
|---|---|---|
| WeirdML | 46.7% | 54.1% |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| LMArena WebDev | — | 1473 |
| SciCode | — | 47.5% |
| ALE-Bench | — | 886.23 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, Kimi K2.7 Code: 23.9 (#115)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | — | 37.6% |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 5,083 |
Reasoning Kimi K2.7 Code leads
DeepSeek-V3.2-Speciale: 32.1 (#65), Kimi K2.7 Code: 38.2 (#56)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | 52.6% | 57.9% |
| CritPt | — | 10% |
| Chess Puzzles | — | 21% |
| Surface Evolver Bench | — | 48.8% |
| Epoch Capabilities Index | — | 150.01 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Kimi K2.7 Code: 52.6 (#39)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| OTIS Mock AIME 2024-2025 | — | 95.6% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Kimi K2.7 Code: 53.6 (#51)
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | — | 87.9% |
| SimpleQA Verified | — | 36.5% |
Writing & Preference Not comparable
DeepSeek-V3.2-Speciale: 46.1 (#217), Kimi K2.7 Code: —
| Benchmark | DeepSeek-V3.2-Speciale | Kimi K2.7 Code |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | — |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Kimi K2.7 Code?
Kimi K2.7 Code is the stronger model overall, scoring 43.0 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 K2.7 Code's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or Kimi K2.7 Code?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is DeepSeek-V3.2-Speciale or Kimi K2.7 Code better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.4 versus 39.9 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 DeepSeek-V3.2-Speciale and Kimi K2.7 Code share?
2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Kimi K2.7 Code has 19.