Model comparison
Kimi K2.5 vs Laguna S 2.1
Head-to-head evidence from 3 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Kimi K2.5 #54 (Supported); 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 and Laguna S 2.1 share 3 comparable benchmark results. 2 of 8 categories are comparable. 60 results are unique to Kimi K2.5; 3 to Laguna S 2.1.
Updated July 21, 2026- Shared results
- 3
- Kimi K2.5 only
- 60
- Laguna S 2.1 only
- 3
- Comparable categories
- 2 / 8
Treat this as a split decision. Kimi K2.5 makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; 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 3 shared benchmark results across 2 evidence categories; 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 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 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 is the reasoning model in the pair, while Kimi K2.5 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. Laguna S 2.1 gives you the larger context window at 1M, compared with 256K for Kimi K2.5.
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 | Δ | Laguna S 2.1 |
|---|---|---|---|
| Agentic | Kimi K2.555.0 | Margin→ 15.2 | Laguna S 2.170.2 |
| Coding | Kimi K2.559.4 | MarginTie | Laguna S 2.159.4 |
| Reasoning | Kimi K2.561.0 | MarginNo overlap | Laguna S 2.1Not measured |
| Knowledge | Kimi K2.556.9 | MarginNo overlap | Laguna S 2.1Not measured |
| Math | Kimi K2.560.6 | MarginNo overlap | Laguna S 2.1Not measured |
| Multilingual | Kimi K2.582.3 | MarginNo overlap | Laguna S 2.1Not measured |
| Multimodal | Kimi K2.578.5 | MarginNo overlap | Laguna S 2.1Not measured |
| Inst. Following | Kimi K2.593.9 | 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 scored 50.8%; Laguna S 2.1 scored 70.2%. Laguna S 2.1 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 50.7%B 59.4%Winner: Laguna S 2.1Δ 8.7SWE-bench Pro: Kimi K2.5 scored 50.7%; Laguna S 2.1 scored 59.4%. 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 | Laguna S 2.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2.5$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 K2.545 tok/s | Laguna S 2.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K2.52.38 s | Laguna S 2.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K2.5256K | Laguna S 2.11M | Laguna S 2.1 lists the larger context window. |
Benchmark Deep Dive
AgenticLaguna S 2.1 wins20 benchmarks
| Benchmark | Kimi K2.5 | Laguna S 2.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 50.8% | 70.2% | Laguna S 2.1 leads |
| BrowseCompSource | 60.6% | — | Not comparable |
| Claw-EvalSource | 52.3% | — | Not comparable |
| QwenClawBenchSource | 54.3% | — | Not comparable |
| τ³-bench resultsSource | 65.7% | — | Not comparable |
| DeepSearchQASource | 77.1% | — | Not comparable |
| DeepPlanningSource | 14.4% | — | Not comparable |
| ToolathlonSource | 27.8% | — | Not comparable |
| MCP AtlasSource | 29.5% | — | Not comparable |
| MCP-TasksSource | 59.1% | — | Not comparable |
| WideResearchSource | 72.7% | — | Not comparable |
| τ²-bench resultsSource | 95.9% | — | Not comparable |
| APEX-Agents-AASource | 11.5% | — | Not comparable |
| Gert LabsSource | 45.88% | — | Not comparable |
| ResearchClawBenchSource | 14.0% | — | Not comparable |
| JobBenchSource | 8.7% | — | 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 |
CodingTie12 benchmarks
| Benchmark | Kimi K2.5 | Laguna S 2.1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 76.8% | — | Not comparable |
| SWE-bench Verified*Source | 70.8% | — | Not comparable |
| LiveCodeBench v6Source | 85.0% | — | Not comparable |
| SWE-bench ProSource | 50.7% | 59.4% | Laguna S 2.1 leads |
| SWE MultilingualSource | 73% | 78.5% | Laguna S 2.1 leads |
| SWE-RebenchSource | 58.5% | — | Not comparable |
| React Native EvalsSource | 77.2% | — | Not comparable |
| SciCodeSource | 48.7% | — | Not comparable |
| AA-SciCodeSource | 49.0% | — | Not comparable |
| AA Coding IndexSource | 46.8% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 70.2% | Not comparable |
| deepSweSource | — | 40.4% | Not comparable |
Reasoning3 benchmarks
Knowledge12 benchmarks
| Benchmark | Kimi K2.5 | Laguna S 2.1 | Result |
|---|---|---|---|
| GPQASource | 87.6% | — | Not comparable |
| GPQA-DSource | 87.6% | — | Not comparable |
| SuperGPQASource | 69.2% | — | Not comparable |
| MMLU-ProSource | 87.1% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 87.1% | — | Not comparable |
| HLESource | 30.1% | — | 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 |
Math9 benchmarks
| Benchmark | Kimi K2.5 | Laguna S 2.1 | Result |
|---|---|---|---|
| AIME 2025Source | 96.1% | — | Not comparable |
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 96.3% | — | Not comparable |
| HMMT Feb 2025Source | 95.4% | — | Not comparable |
| HMMT Nov 2025Source | 91.1% | — | Not comparable |
| HMMT Feb 2026Source | 87.1% | — | Not comparable |
| MMAnswerBenchSource | 81.8% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 27.900% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 4.200% | — | Not comparable |
Multilingual2 benchmarks
Multimodal6 benchmarks
Frequently Asked Questions (3)
Which is better, Kimi K2.5 or Laguna S 2.1?
Kimi K2.5 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 or Laguna S 2.1?
Kimi K2.5 and Laguna S 2.1 are effectively tied for coding here, both landing at 59.4 on average.
Which is better for agentic tasks, Kimi K2.5 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.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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