Model comparison
DeepSeek V3.2 vs Kimi K2.6
Head-to-head evidence from 17 shared benchmark results across 7 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: DeepSeek V3.2 unranked; Kimi K2.6 #13
Evidence parity. DeepSeek V3.2 and Kimi K2.6 share 17 comparable benchmark results. 2 of 8 categories are comparable. 3 results are unique to DeepSeek V3.2; 43 to Kimi K2.6.
Updated July 13, 2026- Shared results
- 17
- DeepSeek V3.2 only
- 3
- Kimi K2.6 only
- 43
- Comparable categories
- 2 / 8
Pick Kimi K2.6 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 17 shared benchmark results across 7 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Kimi K2.6 is clearly ahead on the provisional aggregate, 74 to 54. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.6's sharpest advantage is in mathematics, where it averages 67.1 against 17.1. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 22.100% to 38.966%.
Kimi K2.6 is also the more expensive model on tokens at $0.95 input / $4.00 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 9.5x on output cost alone. Kimi K2.6 is the reasoning model in the pair, while DeepSeek V3.2 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Kimi K2.6 gives you the larger context window at 256K, compared with 128K for DeepSeek V3.2.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | DeepSeek V3.2 | Δ | Kimi K2.6 |
|---|---|---|---|
| Math | DeepSeek V3.217.1 | Margin→ 50.0 | Kimi K2.667.1 |
| Coding | DeepSeek V3.260.9 | Margin→ 11.7 | Kimi K2.672.6 |
| Agentic | DeepSeek V3.2Not measured | MarginNo overlap | Kimi K2.673.5 |
| Knowledge | DeepSeek V3.2Not measured | MarginNo overlap | Kimi K2.642.2 |
| Multimodal | DeepSeek V3.2Not measured | MarginNo overlap | Kimi K2.679.8 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 22.100%B 38.966%Winner: Kimi K2.6Δ 16.9FrontierMath v2 (Tiers 1-3): DeepSeek V3.2 scored 22.100%; Kimi K2.6 scored 38.966%. Kimi K2.6 wins this benchmark. - Source ↗
FrontierMath v2 (Tier 4)
MathA 2.100%B 14.580%Winner: Kimi K2.6Δ 12.5FrontierMath v2 (Tier 4): DeepSeek V3.2 scored 2.100%; Kimi K2.6 scored 14.580%. Kimi K2.6 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | Kimi K2.6 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | Kimi K2.6$0.95 input / $4 output | DeepSeek V3.2 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | Kimi K2.6Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | Kimi K2.6Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | Kimi K2.6256K | Kimi K2.6 lists the larger context window. |
Benchmark Deep Dive
Agentic23 benchmarks
| Benchmark | DeepSeek V3.2 | Kimi K2.6 | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | 62.3% | Kimi K2.6 leads |
| VITA-BenchSource | 18.5% | — | Not comparable |
| Tau2-TelecomSource | 78.9% | 95.9% | Kimi K2.6 leads |
| Gert LabsSource | 29.57% | 56.82% | Kimi K2.6 leads |
| Terminal-Bench 2.0Source | — | 66.7% | Not comparable |
| BrowseCompSource | — | 83.2% | Not comparable |
| OSWorld-VerifiedSource | — | 73.1% | Not comparable |
| ToolathlonSource | — | 50% | Not comparable |
| MCP AtlasSource | — | 55.9% | Not comparable |
| DeepSearchQASource | — | 92.5% | Not comparable |
| WideResearchSource | — | 80.8% | Not comparable |
| AA Agentic IndexSource | — | 30.3% | Not comparable |
| GDPval-AASource | — | 34.5% | Not comparable |
| GDPval-AASource | — | 1190 | Not comparable |
| APEX-Agents-AASource | — | 28.5% | Not comparable |
| ResearchClawBenchSource | — | 18.0% | Not comparable |
| OSWorld 2.0Source | — | 4.6% | Not comparable |
| AA BriefcaseSource | — | 809 | Not comparable |
| AA AutomationBenchSource | — | 19.6% | Not comparable |
| AA EnterpriseOps-GymSource | — | 38.5% | Not comparable |
| AA Harvey LABSource | — | 0.0% | Not comparable |
| AA ITBenchSource | — | 31.2% | Not comparable |
| AA Tau3 BankingSource | — | 20.6% | Not comparable |
CodingKimi K2.6 wins15 benchmarks
| Benchmark | DeepSeek V3.2 | Kimi K2.6 | Result |
|---|---|---|---|
| SWE-RebenchSource | 60.9% | — | Not comparable |
| React Native EvalsSource | 71.5% | — | Not comparable |
| Terminal-Bench HardSource | 32.6% | 43.9% | Kimi K2.6 leads |
| AA-SciCodeSource | 38.7% | 53.5% | Kimi K2.6 leads |
| SWE-bench VerifiedSource | — | 80.2% | Not comparable |
| LiveCodeBenchSource | — | 89.6% | Not comparable |
| LiveCodeBench v6Source | — | 89.6% | Not comparable |
| SWE-bench ProSource | — | 58.6% | Not comparable |
| SWE MultilingualSource | — | 76.7% | Not comparable |
| SciCodeSource | — | 52.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 66.7% | Not comparable |
| Vibe Code BenchSource | — | 37.89% | Not comparable |
| cursorBench31Source | — | 47.6% | Not comparable |
| AA Coding IndexSource | — | 61.8% | Not comparable |
| AA Terminal-Bench 2.1Source | — | 65.9% | Not comparable |
Reasoning2 benchmarks
Knowledge10 benchmarks
| Benchmark | DeepSeek V3.2 | Kimi K2.6 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 44.2% | Kimi K2.6 leads |
| AA-GPQA DiamondSource | 75.1% | 91.1% | Kimi K2.6 leads |
| AA-HLESource | 10.5% | 35.9% | Kimi K2.6 leads |
| AA-Omniscience IndexSource | -46.7% | 6.4% | Kimi K2.6 leads |
| AA-Omniscience AccuracySource | 24.2% | 32.8% | Kimi K2.6 leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 39.3% | Kimi K2.6 leads |
| GPQASource | — | 90.5% | Not comparable |
| GPQA-DSource | — | 90.5% | Not comparable |
| HLESource | — | 34.7% | Not comparable |
| AA Openness IndexSource | — | 33.3% | Not comparable |
MathKimi K2.6 wins5 benchmarks
Multimodal7 benchmarks
| Benchmark | DeepSeek V3.2 | Kimi K2.6 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1217 | 1318 | Kimi K2.6 leads |
| MMMU-ProSource | — | 79.4% | Not comparable |
| MMMU-Pro w/ PythonSource | — | 80.1% | Not comparable |
| CharXivSource | — | 80.4% | Not comparable |
| MathVisionSource | — | 87.4% | Not comparable |
| V*Source | — | 96.9% | Not comparable |
| AA-MMMU-ProSource | — | 79.4% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | Kimi K2.6 | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 76.0% | Kimi K2.6 leads |
Frequently Asked Questions (3)
Which is better, DeepSeek V3.2 or Kimi K2.6?
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 74 to 54. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 22.100% and 38.966%.
Which is better for coding, DeepSeek V3.2 or Kimi K2.6?
Kimi K2.6 has the edge for coding in this comparison, averaging 72.6 versus 60.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V3.2 or Kimi K2.6?
Kimi K2.6 has the edge for math in this comparison, averaging 67.1 versus 17.1. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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