Model comparison
DeepSeek V4 Pro (Max) vs Kimi K2.5 (Reasoning)
Head-to-head evidence from 23 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro (Max) unranked (Not scored); Kimi K2.5 (Reasoning) #57 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Pro (Max) and Kimi K2.5 (Reasoning) share 23 comparable benchmark results. 3 of 8 categories are comparable. 25 results are unique to DeepSeek V4 Pro (Max); 4 to Kimi K2.5 (Reasoning).
Updated July 23, 2026- Shared results
- 23
- DeepSeek V4 Pro (Max) only
- 25
- Kimi K2.5 (Reasoning) only
- 4
- Comparable categories
- 3 / 8
Treat this as a split decision. DeepSeek V4 Pro (Max) makes more sense if agentic is the priority or you want the cheaper token bill; Kimi K2.5 (Reasoning) is the better fit if knowledge is the priority.
Confidence note. This is a partial-evidence comparison with 23 shared benchmark results across 6 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
DeepSeek V4 Pro (Max) and Kimi K2.5 (Reasoning) finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Kimi K2.5 (Reasoning) is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.43 input / $0.87 output per 1M tokens for DeepSeek V4 Pro (Max). That is roughly 3.4x on output cost alone. DeepSeek V4 Pro (Max) gives you the larger context window at 1M, compared with 128K for Kimi K2.5 (Reasoning).
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 (Max) | Δ | Kimi K2.5 (Reasoning) |
|---|---|---|---|
| Knowledge | DeepSeek V4 Pro (Max)60.1 | Margin→ 27.1 | Kimi K2.5 (Reasoning)87.2 |
| Agentic | DeepSeek V4 Pro (Max)74.5 | Margin← 19.5 | Kimi K2.5 (Reasoning)55.0 |
| Coding | DeepSeek V4 Pro (Max)70.9 | Margin→ 5.9 | Kimi K2.5 (Reasoning)76.8 |
| Math | DeepSeek V4 Pro (Max)95.2 | MarginNo overlap | Kimi K2.5 (Reasoning)Not measured |
| Multimodal | DeepSeek V4 Pro (Max)Not measured | MarginNo overlap | Kimi K2.5 (Reasoning)78.5 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
BrowseComp
AgenticA 83.4%B 60.6%Winner: DeepSeek V4 Pro (Max)Δ 22.8BrowseComp: DeepSeek V4 Pro (Max) scored 83.4%; Kimi K2.5 (Reasoning) scored 60.6%. DeepSeek V4 Pro (Max) wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 67.9%B 50.8%Winner: DeepSeek V4 Pro (Max)Δ 17.1Terminal-Bench 2.0: DeepSeek V4 Pro (Max) scored 67.9%; Kimi K2.5 (Reasoning) scored 50.8%. DeepSeek V4 Pro (Max) wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 80.6%B 76.8%Winner: DeepSeek V4 Pro (Max)Δ 3.8SWE-bench Verified: DeepSeek V4 Pro (Max) scored 80.6%; Kimi K2.5 (Reasoning) scored 76.8%. DeepSeek V4 Pro (Max) wins this benchmark. - Source ↗
GPQA
KnowledgeA 90.1%B 87.6%Winner: DeepSeek V4 Pro (Max)Δ 2.5GPQA: DeepSeek V4 Pro (Max) scored 90.1%; Kimi K2.5 (Reasoning) scored 87.6%. DeepSeek V4 Pro (Max) wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 87.5%B 87.1%Winner: DeepSeek V4 Pro (Max)Δ 0.4MMLU-Pro: DeepSeek V4 Pro (Max) scored 87.5%; Kimi K2.5 (Reasoning) scored 87.1%. DeepSeek V4 Pro (Max) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro (Max) | Kimi K2.5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro (Max)$0.435 input / $0.87 output | Kimi K2.5 (Reasoning)$0.6 input / $3 output | DeepSeek V4 Pro (Max) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Pro (Max)Not available | Kimi K2.5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Pro (Max)Not available | Kimi K2.5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro (Max)1M | Kimi K2.5 (Reasoning)128K | DeepSeek V4 Pro (Max) lists the larger context window. |
Benchmark Deep Dive
AgenticDeepSeek V4 Pro (Max) wins18 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 67.9% | 50.8% | DeepSeek V4 Pro (Max) leads |
| BrowseCompSource | 83.4% | 60.6% | DeepSeek V4 Pro (Max) leads |
| HLE w/ toolsSource | 48.2% | — | Not comparable |
| MCP AtlasSource | 73.6% | — | Not comparable |
| GDPval-AASource | 1307 | 1009 | DeepSeek V4 Pro (Max) leads |
| ToolathlonSource | 51.8% | — | Not comparable |
| AA Agentic IndexSource | 36.4% | 21.7% | DeepSeek V4 Pro (Max) leads |
| APEX-Agents-AASource | 24.3% | 11.5% | DeepSeek V4 Pro (Max) leads |
| τ²-bench resultsSource | 96.2% | 95.9% | DeepSeek V4 Pro (Max) leads |
| GDPval-AASource | 40.4% | 25.4% | DeepSeek V4 Pro (Max) leads |
| AA BriefcaseSource | 932 | — | Not comparable |
| AA EnterpriseOps-GymSource | 40.4% | — | Not comparable |
| AA Harvey LABSource | 84.4% | — | Not comparable |
| AA ITBenchSource | 38.3% | — | Not comparable |
| AA Tau3 BankingSource | 25.8% | — | Not comparable |
| terminalBenchHardSource | 46.2% | — | Not comparable |
| aaTerminalBench21Source | 64% | — | Not comparable |
| Gert LabsSource | — | 32.58% | Not comparable |
CodingKimi K2.5 (Reasoning) wins8 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| CodeforcesSource | 3206.0 | — | Not comparable |
| SWE-bench VerifiedSource | 80.6% | 76.8% | DeepSeek V4 Pro (Max) leads |
| SWE-bench ProSource | 55.4% | — | Not comparable |
| SWE MultilingualSource | 76.2% | — | Not comparable |
| Terminal-Bench 2.0Source | 67.9% | — | Not comparable |
| Vibe Code BenchSource | 49.93% | 17.54% | DeepSeek V4 Pro (Max) leads |
| AA Coding IndexSource | 59.4% | 46.8% | DeepSeek V4 Pro (Max) leads |
| AA-SciCodeSource | 50.0% | 49.0% | DeepSeek V4 Pro (Max) leads |
Reasoning4 benchmarks
KnowledgeKimi K2.5 (Reasoning) wins13 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| MMLU-ProSource | 87.5% | 87.1% | DeepSeek V4 Pro (Max) leads |
| SimpleQASource | 57.9% | — | Not comparable |
| Chinese-SimpleQASource | 84.4% | — | Not comparable |
| GPQASource | 90.1% | 87.6% | DeepSeek V4 Pro (Max) leads |
| GPQA-DSource | 90.1% | — | Not comparable |
| HLESource | 37.7% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 44.3% | 35.4% | DeepSeek V4 Pro (Max) leads |
| AA-GPQA DiamondSource | 88.8% | 87.9% | DeepSeek V4 Pro (Max) leads |
| AA-HLESource | 35.9% | 29.4% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience IndexSource | -10.0% | -8.1% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience AccuracySource | 43.3% | 34.3% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience Hallucination RateSource | 94.0% | 64.6% | Kimi K2.5 (Reasoning) leads |
| AA Openness IndexSource | 50.0% | — | Not comparable |
Math5 benchmarks
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.5% | 70.2% | DeepSeek V4 Pro (Max) leads |
Frequently Asked Questions (4)
Which is better, DeepSeek V4 Pro (Max) or Kimi K2.5 (Reasoning)?
DeepSeek V4 Pro (Max) and Kimi K2.5 (Reasoning) are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, DeepSeek V4 Pro (Max) or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 87.2 versus 60.1. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V4 Pro (Max) or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 70.9. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, DeepSeek V4 Pro (Max) or Kimi K2.5 (Reasoning)?
DeepSeek V4 Pro (Max) has the edge for agentic tasks in this comparison, averaging 74.5 versus 55. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
Choose a model with this week’s evidence
Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.
One email each week. Unsubscribe anytime.