Model comparison
DeepSeek V3.2 vs Kimi K2.5 (Reasoning)
Head-to-head evidence from 13 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.2 #82 (Supported); Kimi K2.5 (Reasoning) #57 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and Kimi K2.5 (Reasoning) share 13 comparable benchmark results. 1 of 8 categories are comparable. 6 results are unique to DeepSeek V3.2; 14 to Kimi K2.5 (Reasoning).
Updated July 20, 2026- Shared results
- 13
- DeepSeek V3.2 only
- 6
- Kimi K2.5 (Reasoning) only
- 14
- Comparable categories
- 1 / 8
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 13 shared benchmark results across 6 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Kimi K2.5 (Reasoning) is clearly ahead on the BenchAlign aggregate, 59.35 to 55.4. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in coding, where it averages 76.8 against 60.9.
Kimi K2.5 (Reasoning) is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 7.1x on output cost alone. Kimi K2.5 (Reasoning) is the reasoning model in the pair, while DeepSeek V3.2 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.
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 V3.2 | Δ | Kimi K2.5 (Reasoning) |
|---|---|---|---|
| Coding | DeepSeek V3.260.9 | Margin→ 15.9 | Kimi K2.5 (Reasoning)76.8 |
| Agentic | DeepSeek V3.2Not measured | MarginNo overlap | Kimi K2.5 (Reasoning)55.0 |
| Knowledge | DeepSeek V3.2Not measured | MarginNo overlap | Kimi K2.5 (Reasoning)87.2 |
| Math | DeepSeek V3.217.1 | MarginNo overlap | Kimi K2.5 (Reasoning)Not measured |
| Multimodal | DeepSeek V3.2Not measured | MarginNo overlap | Kimi K2.5 (Reasoning)78.5 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | Kimi K2.5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | Kimi K2.5 (Reasoning)$0.6 input / $3 output | DeepSeek V3.2 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | Kimi K2.5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | Kimi K2.5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | Kimi K2.5 (Reasoning)128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic10 benchmarks
| Benchmark | DeepSeek V3.2 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | — | Not comparable |
| VITA-BenchSource | 18.5% | — | Not comparable |
| τ²-bench resultsSource | 78.9% | 95.9% | Kimi K2.5 (Reasoning) leads |
| Gert LabsSource | 29.57% | 32.58% | Kimi K2.5 (Reasoning) leads |
| Terminal-Bench 2.0Source | — | 50.8% | Not comparable |
| BrowseCompSource | — | 60.6% | Not comparable |
| APEX-Agents-AASource | — | 11.5% | Not comparable |
| AA Agentic IndexSource | — | 21.7% | Not comparable |
| GDPval-AASource | — | 25.4% | Not comparable |
| GDPval-AASource | — | 1009 | Not comparable |
CodingKimi K2.5 (Reasoning) wins6 benchmarks
| Benchmark | DeepSeek V3.2 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| SWE-RebenchSource | 60.9% | — | Not comparable |
| React Native EvalsSource | 71.5% | — | Not comparable |
| AA-SciCodeSource | 38.7% | 49.0% | Kimi K2.5 (Reasoning) leads |
| SWE-bench VerifiedSource | — | 76.8% | Not comparable |
| Vibe Code BenchSource | — | 17.54% | Not comparable |
| AA Coding IndexSource | — | 46.8% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | DeepSeek V3.2 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 35.4% | Kimi K2.5 (Reasoning) leads |
| AA-GPQA DiamondSource | 75.1% | 87.9% | Kimi K2.5 (Reasoning) leads |
| AA-HLESource | 10.5% | 29.4% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience IndexSource | -46.7% | -8.1% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience AccuracySource | 24.2% | 34.3% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 64.6% | Kimi K2.5 (Reasoning) leads |
| GPQASource | — | 87.6% | Not comparable |
| MMLU-ProSource | — | 87.1% | Not comparable |
Math3 benchmarks
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 70.2% | Kimi K2.5 (Reasoning) leads |
Frequently Asked Questions (2)
Which is better, DeepSeek V3.2 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) is ahead on BenchLM's BenchAlign leaderboard, 59.35 to 55.4.
Which is better for coding, DeepSeek V3.2 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 60.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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