Model comparison
DeepSeek V4 Pro vs Kimi K2.7 Code
Head-to-head evidence from 2 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro #49 (Supported); Kimi K2.7 Code #92 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Pro and Kimi K2.7 Code share 2 comparable benchmark results. 0 of 8 categories are comparable. 21 results are unique to DeepSeek V4 Pro; 21 to Kimi K2.7 Code.
Updated July 27, 2026- Shared results
- 2
- DeepSeek V4 Pro only
- 21
- Kimi K2.7 Code only
- 21
- Comparable categories
- 0 / 8
Benchmark data for DeepSeek V4 Pro and Kimi K2.7 Code is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 2 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 $0.43 input / $0.87 output per 1M tokens for DeepSeek V4 Pro. DeepSeek V4 Pro has the larger context window at 1M, compared with 256K for Kimi K2.7 Code.
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 V4 Pro | Δ | Kimi K2.7 Code |
|---|---|---|---|
| Agentic | DeepSeek V4 Pro59.1 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Coding | DeepSeek V4 Pro65.3 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Knowledge | DeepSeek V4 Pro41.3 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Math | DeepSeek V4 Pro31.7 | 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 V4 Pro | Kimi K2.7 Code | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro$0.435 input / $0.87 output | Kimi K2.7 Code$0.95 input / $4 output | DeepSeek V4 Pro has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 ProNot available | Kimi K2.7 CodeNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 ProNot available | Kimi K2.7 CodeNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro1M | Kimi K2.7 Code256K | DeepSeek V4 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic12 benchmarks
| Benchmark | DeepSeek V4 Pro | Kimi K2.7 Code | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 59.1% | — | Not comparable |
| MCP AtlasSource | 69.4% | 76% | Kimi K2.7 Code leads |
| ToolathlonSource | 46.3% | — | Not comparable |
| Claw-EvalSource | 59.8% | — | Not comparable |
| Gert LabsSource | 50.28% | — | Not comparable |
| ResearchClawBenchSource | 17.1% | — | Not comparable |
| Kimi Claw 24/7Source | — | 46.9% | Not comparable |
| MCP Mark VerifiedSource | — | 81.1% | Not comparable |
| AA Agentic IndexSource | — | 29.6% | Not comparable |
| τ²-bench resultsSource | — | 90.1% | Not comparable |
| GDPval-AASource | — | 34.3% | Not comparable |
| GDPval-AASource | — | 1186 | Not comparable |
Coding10 benchmarks
| Benchmark | DeepSeek V4 Pro | Kimi K2.7 Code | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.6% | — | Not comparable |
| SWE-bench ProSource | 52.1% | — | Not comparable |
| SWE MultilingualSource | 69.8% | — | Not comparable |
| Terminal-Bench 2.0Source | 59.1% | — | Not comparable |
| 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 |
| AA Coding IndexSource | — | 60.8% | Not comparable |
| AA-SciCodeSource | — | 47.5% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | DeepSeek V4 Pro | Kimi K2.7 Code | Result |
|---|---|---|---|
| MMLU-ProSource | 82.9% | — | Not comparable |
| SimpleQASource | 45% | — | Not comparable |
| Chinese-SimpleQASource | 75.8% | — | Not comparable |
| GPQASource | 72.9% | — | Not comparable |
| GPQA-DSource | 72.9% | — | Not comparable |
| HLESource | 7.7% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 42.0% | Not comparable |
| AA-GPQA DiamondSource | — | 89.6% | Not comparable |
| AA-HLESource | — | 32.8% | Not comparable |
| AA-Omniscience IndexSource | — | -10.7% | Not comparable |
| AA-Omniscience AccuracySource | — | 38.6% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 80.3% | Not comparable |
Math4 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V4 Pro | Kimi K2.7 Code | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1260 | 1300 | Kimi K2.7 Code leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Pro | Kimi K2.7 Code | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 63.1% | Not comparable |
Frequently Asked Questions (3)
Can I compare DeepSeek V4 Pro 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 V4 Pro and Kimi K2.7 Code today?
DeepSeek V4 Pro: $0.43 input / $0.87 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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