Model comparison
DeepSeekMath V2 vs Kimi K2.5 (Reasoning)
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeekMath V2 #119 (Estimated); Kimi K2.5 (Reasoning) #57 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeekMath V2 and Kimi K2.5 (Reasoning) share 0 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to DeepSeekMath V2; 27 to Kimi K2.5 (Reasoning).
Updated July 21, 2026- Shared results
- 0
- DeepSeekMath V2 only
- 0
- Kimi K2.5 (Reasoning) only
- 27
- Comparable categories
- 0 / 8
Benchmark data for DeepSeekMath V2 and Kimi K2.5 (Reasoning) is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 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 does not have sourced benchmark coverage for DeepSeekMath V2 yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
Kimi K2.5 (Reasoning) is priced at $0.60 input / $3.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for DeepSeekMath V2.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeekMath V2 | Kimi K2.5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeekMath V2$0 input / $0 output | Kimi K2.5 (Reasoning)$0.6 input / $3 output | DeepSeekMath V2 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeekMath V2Not available | Kimi K2.5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeekMath V2Not available | Kimi K2.5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeekMath V2128K | Kimi K2.5 (Reasoning)128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | DeepSeekMath V2 | 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 |
Coding4 benchmarks
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | DeepSeekMath V2 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| GPQASource | — | 87.6% | Not comparable |
| MMLU-ProSource | — | 87.1% | 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 |
Math1 benchmarks
| Benchmark | DeepSeekMath V2 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AIME 2025Source | — | 96.1% | Not comparable |
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeekMath V2 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 70.2% | Not comparable |
Frequently Asked Questions (3)
Can I compare DeepSeekMath V2 and Kimi K2.5 (Reasoning) 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 DeepSeekMath V2 and Kimi K2.5 (Reasoning) today?
DeepSeekMath V2: $0.00 input / $0.00 output per 1M tokens Kimi K2.5 (Reasoning): $0.60 input / $3.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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