Model comparison
DeepSeek V3 vs Kimi K2.6
Head-to-head evidence from 19 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3 #147 (Supported); Kimi K2.6 #74 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3 and Kimi K2.6 share 19 comparable benchmark results. 3 of 8 categories are comparable. 3 results are unique to DeepSeek V3; 32 to Kimi K2.6.
Updated July 23, 2026- Shared results
- 19
- DeepSeek V3 only
- 3
- Kimi K2.6 only
- 32
- Comparable categories
- 3 / 8
Pick Kimi K2.6 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 19 shared benchmark results across 7 evidence categories; 3 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 BenchAlign aggregate, 56.79 to 44.97. 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 1.7. The single biggest benchmark swing on the page is SWE-bench Verified, 42% to 80.2%. DeepSeek V3 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Kimi K2.6 is also the more expensive model on tokens at $0.95 input / $4.00 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. That is roughly 3.6x on output cost alone. Kimi K2.6 is the reasoning model in the pair, while DeepSeek V3 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.
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 | Δ | Kimi K2.6 |
|---|---|---|---|
| Math | DeepSeek V31.7 | Margin→ 65.4 | Kimi K2.667.1 |
| Knowledge | DeepSeek V372.7 | Margin← 30.5 | Kimi K2.642.2 |
| Coding | DeepSeek V338.9 | Margin→ 25.5 | Kimi K2.664.4 |
| Agentic | DeepSeek V3Not measured | MarginNo overlap | Kimi K2.673.5 |
| Multimodal | DeepSeek V3Not measured | MarginNo overlap | Kimi K2.679.8 |
| Inst. Following | DeepSeek V386.1 | MarginNo overlap | Kimi K2.6Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 42%B 80.2%Winner: Kimi K2.6Δ 38.2SWE-bench Verified: DeepSeek V3 scored 42%; Kimi K2.6 scored 80.2%. Kimi K2.6 wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 1.724%B 38.966%Winner: Kimi K2.6Δ 37.2FrontierMath v2 (Tiers 1-3): DeepSeek V3 scored 1.724%; Kimi K2.6 scored 38.966%. Kimi K2.6 wins this benchmark. - Source ↗
GPQA
KnowledgeA 59.1%B 90.5%Winner: Kimi K2.6Δ 31.4GPQA: DeepSeek V3 scored 59.1%; Kimi K2.6 scored 90.5%. 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 | Kimi K2.6 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3$0.27 input / $1.1 output | Kimi K2.6$0.95 input / $4 output | DeepSeek V3 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3Not available | Kimi K2.6Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3Not available | Kimi K2.6Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3128K | Kimi K2.6256K | Kimi K2.6 lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | DeepSeek V3 | Kimi K2.6 | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.6% | 30.3% | Kimi K2.6 leads |
| τ²-bench resultsSource | 22.8% | 95.9% | Kimi K2.6 leads |
| GDPval-AASource | 0.0% | 34.5% | Kimi K2.6 leads |
| GDPval-AASource | 217 | 1189 | 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 |
| Claw-EvalSource | — | 62.3% | Not comparable |
| DeepSearchQASource | — | 92.5% | Not comparable |
| WideResearchSource | — | 80.8% | Not comparable |
| APEX-Agents-AASource | — | 28.5% | Not comparable |
| Gert LabsSource | — | 56.82% | Not comparable |
| ResearchClawBenchSource | — | 18.0% | Not comparable |
| OSWorld 2.0Source | — | 4.6% | Not comparable |
| terminalBenchHardSource | — | 43.9% | Not comparable |
CodingKimi K2.6 wins11 benchmarks
| Benchmark | DeepSeek V3 | Kimi K2.6 | Result |
|---|---|---|---|
| LiveCodeBenchSource | 37.6% | — | Not comparable |
| SWE-bench VerifiedSource | 42% | 80.2% | Kimi K2.6 leads |
| AA Coding IndexSource | 23.0% | 61.8% | Kimi K2.6 leads |
| AA-SciCodeSource | 35.4% | 53.5% | Kimi K2.6 leads |
| 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 |
Reasoning2 benchmarks
KnowledgeDeepSeek V3 wins10 benchmarks
| Benchmark | DeepSeek V3 | Kimi K2.6 | Result |
|---|---|---|---|
| GPQASource | 59.1% | 90.5% | Kimi K2.6 leads |
| MMLU-ProSource | 75.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 14.2% | 44.2% | Kimi K2.6 leads |
| AA-GPQA DiamondSource | 55.7% | 91.1% | Kimi K2.6 leads |
| AA-HLESource | 3.6% | 35.9% | Kimi K2.6 leads |
| AA-Omniscience IndexSource | -41.3% | 6.4% | Kimi K2.6 leads |
| AA-Omniscience AccuracySource | 25.4% | 32.8% | Kimi K2.6 leads |
| AA-Omniscience Hallucination RateSource | 89.4% | 39.3% | Kimi K2.6 leads |
| GPQA-DSource | — | 90.5% | Not comparable |
| HLESource | — | 34.7% | Not comparable |
MathKimi K2.6 wins5 benchmarks
Multimodal7 benchmarks
| Benchmark | DeepSeek V3 | Kimi K2.6 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1150 | 1306 | 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 |
Frequently Asked Questions (4)
Which is better, DeepSeek V3 or Kimi K2.6?
Kimi K2.6 is ahead on BenchLM's BenchAlign leaderboard, 56.79 to 44.97. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 42% and 80.2%.
Which is better for knowledge tasks, DeepSeek V3 or Kimi K2.6?
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 72.7 versus 42.2. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V3 or Kimi K2.6?
Kimi K2.6 has the edge for coding in this comparison, averaging 64.4 versus 38.9. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V3 or Kimi K2.6?
Kimi K2.6 has the edge for math in this comparison, averaging 67.1 versus 1.7. 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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