Model comparison
DeepSeek V4 Pro Base vs Kimi K2.5 (Reasoning)
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro Base 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 Base and Kimi K2.5 (Reasoning) share 1 comparable benchmark result. 1 of 8 categories are comparable. 23 results are unique to DeepSeek V4 Pro Base; 26 to Kimi K2.5 (Reasoning).
Updated July 23, 2026- Shared results
- 1
- DeepSeek V4 Pro Base only
- 23
- Kimi K2.5 (Reasoning) only
- 26
- Comparable categories
- 1 / 8
Treat this as a split decision. DeepSeek V4 Pro Base makes more sense if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model; Kimi K2.5 (Reasoning) is the better fit if knowledge is the priority or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 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 Base 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 the reasoning model in the pair, while DeepSeek V4 Pro Base 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. DeepSeek V4 Pro Base 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 Base | Δ | Kimi K2.5 (Reasoning) |
|---|---|---|---|
| Knowledge | DeepSeek V4 Pro Base66.4 | Margin→ 20.8 | Kimi K2.5 (Reasoning)87.2 |
| Agentic | DeepSeek V4 Pro BaseNot measured | MarginNo overlap | Kimi K2.5 (Reasoning)55.0 |
| Coding | DeepSeek V4 Pro BaseNot measured | MarginNo overlap | Kimi K2.5 (Reasoning)76.8 |
| Reasoning | DeepSeek V4 Pro Base51.5 | MarginNo overlap | Kimi K2.5 (Reasoning)Not measured |
| Multimodal | DeepSeek V4 Pro BaseNot 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 ↗
MMLU-Pro
KnowledgeA 73.5%B 87.1%Winner: Kimi K2.5 (Reasoning)Δ 13.6MMLU-Pro: DeepSeek V4 Pro Base scored 73.5%; Kimi K2.5 (Reasoning) scored 87.1%. Kimi K2.5 (Reasoning) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro Base | Kimi K2.5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro BaseNot available | Kimi K2.5 (Reasoning)$0.6 input / $3 output | A complete price comparison is not available. |
| Generation speedtokens per second | DeepSeek V4 Pro BaseNot available | Kimi K2.5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Pro BaseNot available | Kimi K2.5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro Base1M | Kimi K2.5 (Reasoning)128K | DeepSeek V4 Pro Base lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | DeepSeek V4 Pro Base | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 50.8% | Not comparable |
| BrowseCompSource | — | 60.6% | Not comparable |
| APEX-Agents-AASource | — | 11.5% | Not comparable |
| τ²-bench resultsSource | — | 95.9% | Not comparable |
| Gert LabsSource | — | 32.58% | Not comparable |
| AA Agentic IndexSource | — | 21.7% | Not comparable |
| GDPval-AASource | — | 25.4% | Not comparable |
| GDPval-AASource | — | 1009 | Not comparable |
Coding6 benchmarks
Reasoning8 benchmarks
| Benchmark | DeepSeek V4 Pro Base | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| BBHSource | 87.5% | — | Not comparable |
| DROPSource | 88.7% | — | Not comparable |
| HellaSwagSource | 88.0% | — | Not comparable |
| WinoGrandeSource | 81.5% | — | Not comparable |
| CLUEWSCSource | 85.2% | — | Not comparable |
| LongBench v2Source | 51.5% | — | Not comparable |
| AA-LCRSource | — | 65.3% | Not comparable |
| CritPtSource | — | 3.1% | Not comparable |
KnowledgeKimi K2.5 (Reasoning) wins19 benchmarks
| Benchmark | DeepSeek V4 Pro Base | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AGIEvalSource | 83.1% | — | Not comparable |
| MMLUSource | 90.1% | — | Not comparable |
| MMLU-ReduxSource | 90.8% | — | Not comparable |
| MMLU-ProSource | 73.5% | 87.1% | Kimi K2.5 (Reasoning) leads |
| MMMLUSource | 90.3% | — | Not comparable |
| C-EvalSource | 93.1% | — | Not comparable |
| CMMLUSource | 90.8% | — | Not comparable |
| MultiLoKoSource | 51.1% | — | Not comparable |
| SimpleQASource | 55.2% | — | Not comparable |
| SuperGPQASource | 53.9% | — | Not comparable |
| FACTS ParametricSource | 62.6% | — | Not comparable |
| TriviaQASource | 85.6% | — | Not comparable |
| GPQASource | — | 87.6% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 35.4% | Not comparable |
| AA-GPQA DiamondSource | — | 87.9% | Not comparable |
| AA-HLESource | — | 29.4% | Not comparable |
| AA-Omniscience IndexSource | — | -8.1% | Not comparable |
| AA-Omniscience AccuracySource | — | 34.3% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 64.6% | Not comparable |
Math4 benchmarks
Multilingual1 benchmarks
| Benchmark | DeepSeek V4 Pro Base | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| MGSMSource | 84.4% | — | Not comparable |
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Pro Base | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 70.2% | Not comparable |
Frequently Asked Questions (2)
Which is better, DeepSeek V4 Pro Base or Kimi K2.5 (Reasoning)?
DeepSeek V4 Pro Base 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 Base or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 87.2 versus 66.4. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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