Model comparison
Kimi K2.5 (Reasoning) vs Laguna S 2.1
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: Kimi K2.5 (Reasoning) #57 (Estimated); Laguna S 2.1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K2.5 (Reasoning) and Laguna S 2.1 share 1 comparable benchmark result. 2 of 8 categories are comparable. 26 results are unique to Kimi K2.5 (Reasoning); 5 to Laguna S 2.1.
Updated July 21, 2026- Shared results
- 1
- Kimi K2.5 (Reasoning) only
- 26
- Laguna S 2.1 only
- 5
- Comparable categories
- 2 / 8
Treat this as a split decision. Kimi K2.5 (Reasoning) makes more sense if coding is the priority; Laguna S 2.1 is the better fit if agentic is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 2 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) and Laguna S 2.1 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 $0.10 input / $0.20 output per 1M tokens for Laguna S 2.1. That is roughly 15.0x on output cost alone. Laguna S 2.1 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 | Kimi K2.5 (Reasoning) | Δ | Laguna S 2.1 |
|---|---|---|---|
| Coding | Kimi K2.5 (Reasoning)76.8 | Margin← 17.4 | Laguna S 2.159.4 |
| Agentic | Kimi K2.5 (Reasoning)55.0 | Margin→ 15.2 | Laguna S 2.170.2 |
| Knowledge | Kimi K2.5 (Reasoning)87.2 | MarginNo overlap | Laguna S 2.1Not measured |
| Multimodal | Kimi K2.5 (Reasoning)78.5 | MarginNo overlap | Laguna S 2.1Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 50.8%B 70.2%Winner: Laguna S 2.1Δ 19.4Terminal-Bench 2.0: Kimi K2.5 (Reasoning) scored 50.8%; Laguna S 2.1 scored 70.2%. Laguna S 2.1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2.5 (Reasoning) | Laguna S 2.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2.5 (Reasoning)$0.6 input / $3 output | Laguna S 2.1$0.1 input / $0.2 output | Laguna S 2.1 has the lower combined listed price. |
| Generation speedtokens per second | Kimi K2.5 (Reasoning)Not available | Laguna S 2.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K2.5 (Reasoning)Not available | Laguna S 2.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K2.5 (Reasoning)128K | Laguna S 2.11M | Laguna S 2.1 lists the larger context window. |
Benchmark Deep Dive
AgenticLaguna S 2.1 wins9 benchmarks
| Benchmark | Kimi K2.5 (Reasoning) | Laguna S 2.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 50.8% | 70.2% | Laguna S 2.1 leads |
| 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 |
| Toolathlon-VerifiedSource | — | 49.7% | Not comparable |
CodingKimi K2.5 (Reasoning) wins8 benchmarks
| Benchmark | Kimi K2.5 (Reasoning) | Laguna S 2.1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 76.8% | — | Not comparable |
| Vibe Code BenchSource | 17.54% | — | Not comparable |
| AA-SciCodeSource | 49.0% | — | Not comparable |
| AA Coding IndexSource | 46.8% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 70.2% | Not comparable |
| SWE MultilingualSource | — | 78.5% | Not comparable |
| SWE-bench ProSource | — | 59.4% | Not comparable |
| deepSweSource | — | 40.4% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | Kimi K2.5 (Reasoning) | Laguna S 2.1 | 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 | Kimi K2.5 (Reasoning) | Laguna S 2.1 | Result |
|---|---|---|---|
| AIME 2025Source | 96.1% | — | Not comparable |
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | Kimi K2.5 (Reasoning) | Laguna S 2.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 70.2% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, Kimi K2.5 (Reasoning) or Laguna S 2.1?
Kimi K2.5 (Reasoning) and Laguna S 2.1 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 coding, Kimi K2.5 (Reasoning) or Laguna S 2.1?
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 59.4. Laguna S 2.1 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, Kimi K2.5 (Reasoning) or Laguna S 2.1?
Laguna S 2.1 has the edge for agentic tasks in this comparison, averaging 70.2 versus 55. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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