Model comparison
DeepSeek V3.1 vs Kimi K2
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.1 #95 (Supported); Kimi K2 #189 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.1 and Kimi K2 share 12 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to DeepSeek V3.1; 2 to Kimi K2.
Updated July 20, 2026- Shared results
- 12
- DeepSeek V3.1 only
- 0
- Kimi K2 only
- 2
- Comparable categories
- 0 / 8
Benchmark data for DeepSeek V3.1 and Kimi K2 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Kimi K2 is priced at $0.60 input / $2.50 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for DeepSeek V3.1.
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 V3.1 | Δ | Kimi K2 |
|---|---|---|---|
| Math | DeepSeek V3.1Not measured | MarginNo overlap | Kimi K216.1 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.1 | Kimi K2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.1$0 input / $0 output | Kimi K2$0.6 input / $2.5 output | DeepSeek V3.1 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.1Not available | Kimi K243 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.1Not available | Kimi K21.51 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.1128K | Kimi K2128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | DeepSeek V3.1 | Kimi K2 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 34.8% | 61.1% | Kimi K2 leads |
Coding1 benchmarks
| Benchmark | DeepSeek V3.1 | Kimi K2 | Result |
|---|---|---|---|
| AA-SciCodeSource | 36.7% | 34.5% | DeepSeek V3.1 leads |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | DeepSeek V3.1 | Kimi K2 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 21.1% | 19.4% | DeepSeek V3.1 leads |
| AA-GPQA DiamondSource | 73.5% | 76.6% | Kimi K2 leads |
| AA-HLESource | 6.3% | 7.0% | Kimi K2 leads |
| AA-Omniscience IndexSource | -41.1% | -27.5% | Kimi K2 leads |
| AA-Omniscience AccuracySource | 23.1% | 26.8% | Kimi K2 leads |
| AA-Omniscience Hallucination RateSource | 83.5% | 74.2% | Kimi K2 leads |
Math2 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V3.1 | Kimi K2 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1155 | 1083 | DeepSeek V3.1 leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.1 | Kimi K2 | Result |
|---|---|---|---|
| AA-IFBenchSource | 37.8% | 41.5% | Kimi K2 leads |
Frequently Asked Questions (3)
Can I compare DeepSeek V3.1 and Kimi K2 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for DeepSeek V3.1 and Kimi K2 today?
DeepSeek V3.1: $0.00 input / $0.00 output per 1M tokens Kimi K2: $0.60 input / $2.50 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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