Model comparison
Kimi K3 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 K3 #4 (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 K3 and Laguna S 2.1 share 3 comparable benchmark results. 1 of 8 categories are comparable. 54 results are unique to Kimi K3; 3 to Laguna S 2.1.
Updated July 21, 2026- Shared results
- 3
- Kimi K3 only
- 54
- Laguna S 2.1 only
- 3
- Comparable categories
- 1 / 8
Treat this as a split decision. Kimi K3 makes more sense if agentic is the priority or you need the larger 1.05M context window; Laguna S 2.1 is the better fit if you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 2 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 K3 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 K3 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.10 input / $0.20 output per 1M tokens for Laguna S 2.1. That is roughly 75.0x on output cost alone. Kimi K3 gives you the larger context window at 1.05M, compared with 1M for Laguna S 2.1.
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 K3 | Δ | Laguna S 2.1 |
|---|---|---|---|
| Agentic | Kimi K389.5 | Margin← 19.3 | Laguna S 2.170.2 |
| Coding | Kimi K3Not measured | MarginNo overlap | Laguna S 2.159.4 |
| Knowledge | Kimi K361.0 | MarginNo overlap | Laguna S 2.1Not measured |
| Multimodal | Kimi K378.5 | 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 88.3%B 70.2%Winner: Kimi K3Δ 18.1Terminal-Bench 2.0: Kimi K3 scored 88.3%; Laguna S 2.1 scored 70.2%. Kimi K3 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K3 | Laguna S 2.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K3$3 input / $15 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 K3Not available | Laguna S 2.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K3Not available | Laguna S 2.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K31.05M | Laguna S 2.11M | Kimi K3 lists the larger context window. |
Benchmark Deep Dive
AgenticKimi K3 wins21 benchmarks
| Benchmark | Kimi K3 | Laguna S 2.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 88.3% | 70.2% | Kimi K3 leads |
| BrowseCompSource | 91.2% | — | Not comparable |
| DeepSearchQASource | 95.0% | — | Not comparable |
| Toolathlon-VerifiedSource | 73.2% | 49.7% | Kimi K3 leads |
| MCP AtlasSource | 84.2% | — | Not comparable |
| AutomationBenchSource | 30.8% | — | Not comparable |
| JobBenchSource | 52.9% | — | Not comparable |
| APEX-AgentsSource | 37.6% | — | Not comparable |
| SpreadsheetBench 2Source | 34.8% | — | Not comparable |
| DECK-BenchSource | 73.5% | — | Not comparable |
| AA Agentic IndexSource | 50.1% | — | Not comparable |
| GDPval-AASource | 59.0% | — | Not comparable |
| GDPval-AASource | 1679 | — | Not comparable |
| AA BriefcaseSource | 1543 | — | Not comparable |
| AA AutomationBenchSource | 52.7% | — | Not comparable |
| AA EnterpriseOps-GymSource | 45.3% | — | Not comparable |
| AA Harvey LABSource | 94.6% | — | Not comparable |
| AA Tau3 BankingSource | 33.4% | — | Not comparable |
| aaTerminalBench21Source | 85% | — | Not comparable |
| APEX-Agents-AASource | 41.3% | — | Not comparable |
| AA ITBenchSource | 47.7% | — | Not comparable |
Coding12 benchmarks
| Benchmark | Kimi K3 | Laguna S 2.1 | Result |
|---|---|---|---|
| deepSweSource | 67.5% | 40.4% | Kimi K3 leads |
| FrontierSWESource | 81.2% | — | Not comparable |
| ProgramBenchSource | 77.8% | — | Not comparable |
| Kimi Code Bench v2Source | 72.9% | — | Not comparable |
| sweMarathonSource | 42% | — | Not comparable |
| PostTrain BenchSource | 36.6% | — | Not comparable |
| MLS-Bench LiteSource | 48.3% | — | Not comparable |
| AA Coding IndexSource | 76.2% | — | Not comparable |
| AA-SciCodeSource | 58.7% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 70.2% | Not comparable |
| SWE MultilingualSource | — | 78.5% | Not comparable |
| SWE-bench ProSource | — | 59.4% | Not comparable |
Reasoning2 benchmarks
Knowledge10 benchmarks
| Benchmark | Kimi K3 | Laguna S 2.1 | Result |
|---|---|---|---|
| GPQASource | 93.5% | — | Not comparable |
| GPQA-DSource | 93.5% | — | Not comparable |
| HLESource | 56% | — | Not comparable |
| HLE w/o toolsSource | 43.5% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 57.1% | — | Not comparable |
| AA-GPQA DiamondSource | 93.5% | — | Not comparable |
| AA-HLESource | 44.3% | — | Not comparable |
| AA-Omniscience IndexSource | 18.4% | — | Not comparable |
| AA-Omniscience AccuracySource | 46.0% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 50.9% | — | Not comparable |
Multimodal15 benchmarks
| Benchmark | Kimi K3 | Laguna S 2.1 | Result |
|---|---|---|---|
| OfficeQA ProSource | 63.3% | — | Not comparable |
| MMMU-ProSource | 81.6% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 83.4% | — | Not comparable |
| CharXiv w/o toolsSource | 84.8% | — | Not comparable |
| CharXivSource | 91.3% | — | Not comparable |
| MathVisionSource | 94.3% | — | Not comparable |
| MathVision w/ PythonSource | 97.8% | — | Not comparable |
| BabyVision w/ PythonSource | 85.7% | — | Not comparable |
| ZeroBenchSource | 23.0% | — | Not comparable |
| ZeroBench w/ PythonSource | 41.0% | — | Not comparable |
| WorldVQA ForceAnswerSource | 51.0% | — | Not comparable |
| OmniDocBenchSource | 91.1% | — | Not comparable |
| PerceptionBenchSource | 58.5% | — | Not comparable |
| AA-MMMU-ProSource | 80.5% | — | Not comparable |
| Design Arena WebsiteSource | 1386 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, Kimi K3 or Laguna S 2.1?
Kimi K3 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 agentic tasks, Kimi K3 or Laguna S 2.1?
Kimi K3 has the edge for agentic tasks in this comparison, averaging 89.5 versus 70.2. Inside this category, Toolathlon-Verified is the benchmark that creates the most daylight between them.
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