Model comparison
DeepSeek V3 vs Kimi K2.7 Code
Head-to-head evidence from 16 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3 #154 (Supported); Kimi K2.7 Code #92 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3 and Kimi K2.7 Code share 16 comparable benchmark results. 0 of 8 categories are comparable. 6 results are unique to DeepSeek V3; 7 to Kimi K2.7 Code.
Updated July 27, 2026- Shared results
- 16
- DeepSeek V3 only
- 6
- Kimi K2.7 Code only
- 7
- Comparable categories
- 0 / 8
Benchmark data for DeepSeek V3 and Kimi K2.7 Code is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 16 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 K2.7 Code is priced at $0.95 input / $4.00 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. Kimi K2.7 Code has 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 K2.7 Code |
|---|---|---|---|
| Coding | DeepSeek V338.9 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Knowledge | DeepSeek V372.7 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Math | DeepSeek V31.7 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Inst. Following | DeepSeek V386.1 | MarginNo overlap | Kimi K2.7 CodeNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3 | Kimi K2.7 Code | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3$0.27 input / $1.1 output | Kimi K2.7 Code$0.95 input / $4 output | DeepSeek V3 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3Not available | Kimi K2.7 CodeNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3Not available | Kimi K2.7 CodeNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3128K | Kimi K2.7 Code256K | Kimi K2.7 Code lists the larger context window. |
Benchmark Deep Dive
Agentic7 benchmarks
| Benchmark | DeepSeek V3 | Kimi K2.7 Code | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.6% | 29.6% | Kimi K2.7 Code leads |
| τ²-bench resultsSource | 22.8% | 90.1% | Kimi K2.7 Code leads |
| GDPval-AASource | 0.0% | 34.3% | Kimi K2.7 Code leads |
| GDPval-AASource | 231 | 1186 | Kimi K2.7 Code leads |
| Kimi Claw 24/7Source | — | 46.9% | Not comparable |
| MCP AtlasSource | — | 76% | Not comparable |
| MCP Mark VerifiedSource | — | 81.1% | Not comparable |
Coding8 benchmarks
| Benchmark | DeepSeek V3 | Kimi K2.7 Code | Result |
|---|---|---|---|
| LiveCodeBenchSource | 37.6% | — | Not comparable |
| SWE-bench VerifiedSource | 42% | — | Not comparable |
| AA Coding IndexSource | 23.0% | 60.8% | Kimi K2.7 Code leads |
| AA-SciCodeSource | 35.4% | 47.5% | Kimi K2.7 Code leads |
| Kimi Code Bench v2Source | — | 62.0% | Not comparable |
| ProgramBenchSource | — | 53.6% | Not comparable |
| MLS-Bench LiteSource | — | 35.1% | Not comparable |
| cursorBench32Source | — | 49.7% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | DeepSeek V3 | Kimi K2.7 Code | Result |
|---|---|---|---|
| GPQASource | 59.1% | — | Not comparable |
| MMLU-ProSource | 75.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 14.2% | 42.0% | Kimi K2.7 Code leads |
| AA-GPQA DiamondSource | 55.7% | 89.6% | Kimi K2.7 Code leads |
| AA-HLESource | 3.6% | 32.8% | Kimi K2.7 Code leads |
| AA-Omniscience IndexSource | -41.3% | -10.7% | Kimi K2.7 Code leads |
| AA-Omniscience AccuracySource | 25.4% | 38.6% | Kimi K2.7 Code leads |
| AA-Omniscience Hallucination RateSource | 89.4% | 80.3% | Kimi K2.7 Code leads |
Math1 benchmarks
| Benchmark | DeepSeek V3 | Kimi K2.7 Code | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 1.724% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | DeepSeek V3 | Kimi K2.7 Code | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1145 | 1300 | Kimi K2.7 Code leads |
Frequently Asked Questions (3)
Can I compare DeepSeek V3 and Kimi K2.7 Code 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 and Kimi K2.7 Code today?
DeepSeek V3: $0.27 input / $1.10 output per 1M tokens Kimi K2.7 Code: $0.95 input / $4.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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