Model comparison
DeepSeek V4 Pro vs Kimi K2.5 (Reasoning)
Head-to-head evidence from 6 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro #46 (Supported); 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 and Kimi K2.5 (Reasoning) share 6 comparable benchmark results. 3 of 8 categories are comparable. 17 results are unique to DeepSeek V4 Pro; 21 to Kimi K2.5 (Reasoning).
Updated July 23, 2026- Shared results
- 6
- DeepSeek V4 Pro only
- 17
- Kimi K2.5 (Reasoning) only
- 21
- Comparable categories
- 3 / 8
Pick DeepSeek V4 Pro if you want the stronger benchmark profile. Kimi K2.5 (Reasoning) only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 6 shared benchmark results across 4 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 has the cleaner BenchAlign overall profile here, landing at 60.66 versus 59.35. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
DeepSeek V4 Pro's sharpest advantage is in agentic, where it averages 59.1 against 55. The single biggest benchmark swing on the page is GPQA, 72.9% to 87.6%. Kimi K2.5 (Reasoning) does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
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. That is roughly 3.4x on output cost alone. Kimi K2.5 (Reasoning) is the reasoning model in the pair, while DeepSeek V4 Pro 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 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 | Δ | Kimi K2.5 (Reasoning) |
|---|---|---|---|
| Knowledge | DeepSeek V4 Pro41.3 | Margin→ 45.9 | Kimi K2.5 (Reasoning)87.2 |
| Coding | DeepSeek V4 Pro65.3 | Margin→ 11.5 | Kimi K2.5 (Reasoning)76.8 |
| Agentic | DeepSeek V4 Pro59.1 | Margin← 4.1 | Kimi K2.5 (Reasoning)55.0 |
| Math | DeepSeek V4 Pro31.7 | MarginNo overlap | Kimi K2.5 (Reasoning)Not measured |
| Multimodal | DeepSeek V4 ProNot 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 ↗
GPQA
KnowledgeA 72.9%B 87.6%Winner: Kimi K2.5 (Reasoning)Δ 14.7GPQA: DeepSeek V4 Pro scored 72.9%; Kimi K2.5 (Reasoning) scored 87.6%. Kimi K2.5 (Reasoning) wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 59.1%B 50.8%Winner: DeepSeek V4 ProΔ 8.3Terminal-Bench 2.0: DeepSeek V4 Pro scored 59.1%; Kimi K2.5 (Reasoning) scored 50.8%. DeepSeek V4 Pro wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 82.9%B 87.1%Winner: Kimi K2.5 (Reasoning)Δ 4.2MMLU-Pro: DeepSeek V4 Pro scored 82.9%; Kimi K2.5 (Reasoning) scored 87.1%. Kimi K2.5 (Reasoning) wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.6%B 76.8%Winner: Kimi K2.5 (Reasoning)Δ 3.2SWE-bench Verified: DeepSeek V4 Pro scored 73.6%; Kimi K2.5 (Reasoning) scored 76.8%. 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 | Kimi K2.5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro$0.435 input / $0.87 output | Kimi K2.5 (Reasoning)$0.6 input / $3 output | DeepSeek V4 Pro has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 ProNot available | Kimi K2.5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 ProNot available | Kimi K2.5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro1M | Kimi K2.5 (Reasoning)128K | DeepSeek V4 Pro lists the larger context window. |
Benchmark Deep Dive
AgenticDeepSeek V4 Pro wins12 benchmarks
| Benchmark | DeepSeek V4 Pro | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 59.1% | 50.8% | DeepSeek V4 Pro leads |
| MCP AtlasSource | 69.4% | — | Not comparable |
| ToolathlonSource | 46.3% | — | Not comparable |
| Claw-EvalSource | 59.8% | — | Not comparable |
| Gert LabsSource | 50.28% | 32.58% | DeepSeek V4 Pro leads |
| ResearchClawBenchSource | 17.1% | — | Not comparable |
| BrowseCompSource | — | 60.6% | Not comparable |
| APEX-Agents-AASource | — | 11.5% | Not comparable |
| τ²-bench resultsSource | — | 95.9% | Not comparable |
| AA Agentic IndexSource | — | 21.7% | Not comparable |
| GDPval-AASource | — | 25.4% | Not comparable |
| GDPval-AASource | — | 1009 | Not comparable |
CodingKimi K2.5 (Reasoning) wins7 benchmarks
| Benchmark | DeepSeek V4 Pro | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.6% | 76.8% | Kimi K2.5 (Reasoning) leads |
| SWE-bench ProSource | 52.1% | — | Not comparable |
| SWE MultilingualSource | 69.8% | — | Not comparable |
| Terminal-Bench 2.0Source | 59.1% | — | Not comparable |
| Vibe Code BenchSource | — | 17.54% | Not comparable |
| AA-SciCodeSource | — | 49.0% | Not comparable |
| AA Coding IndexSource | — | 46.8% | Not comparable |
Reasoning4 benchmarks
KnowledgeKimi K2.5 (Reasoning) wins12 benchmarks
| Benchmark | DeepSeek V4 Pro | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| MMLU-ProSource | 82.9% | 87.1% | Kimi K2.5 (Reasoning) leads |
| SimpleQASource | 45% | — | Not comparable |
| Chinese-SimpleQASource | 75.8% | — | Not comparable |
| GPQASource | 72.9% | 87.6% | Kimi K2.5 (Reasoning) leads |
| GPQA-DSource | 72.9% | — | Not comparable |
| HLESource | 7.7% | — | 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 |
Math5 benchmarks
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Pro | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 70.2% | Not comparable |
Frequently Asked Questions (4)
Which is better, DeepSeek V4 Pro or Kimi K2.5 (Reasoning)?
DeepSeek V4 Pro is ahead on BenchLM's BenchAlign leaderboard, 60.66 to 59.35. The biggest single separator in this matchup is GPQA, where the scores are 72.9% and 87.6%.
Which is better for knowledge tasks, DeepSeek V4 Pro or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 87.2 versus 41.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V4 Pro or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 65.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, DeepSeek V4 Pro or Kimi K2.5 (Reasoning)?
DeepSeek V4 Pro has the edge for agentic tasks in this comparison, averaging 59.1 versus 55. Inside this category, Gert Labs is the benchmark that creates the most daylight between them.
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