Model comparison
Laguna S 2.1 vs Qwen3.5-27B
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: Laguna S 2.1 unranked (Not scored); Qwen3.5-27B #45 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Laguna S 2.1 and Qwen3.5-27B share 1 comparable benchmark result. 2 of 8 categories are comparable. 5 results are unique to Laguna S 2.1; 27 to Qwen3.5-27B.
Updated July 21, 2026- Shared results
- 1
- Laguna S 2.1 only
- 5
- Qwen3.5-27B only
- 27
- Comparable categories
- 2 / 8
Treat this as a split decision. Laguna S 2.1 makes more sense if agentic is the priority or you need the larger 1M context window; Qwen3.5-27B is the better fit if coding 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
Laguna S 2.1 and Qwen3.5-27B 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.
Laguna S 2.1 is also the more expensive model on tokens at $0.10 input / $0.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-27B. That is roughly Infinityx on output cost alone. Laguna S 2.1 gives you the larger context window at 1M, compared with 262K for Qwen3.5-27B.
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 | Laguna S 2.1 | Δ | Qwen3.5-27B |
|---|---|---|---|
| Agentic | Laguna S 2.170.2 | Margin← 18.2 | Qwen3.5-27B52.0 |
| Coding | Laguna S 2.159.4 | Margin→ 5.5 | Qwen3.5-27B64.9 |
| Reasoning | Laguna S 2.1Not measured | MarginNo overlap | Qwen3.5-27B60.6 |
| Knowledge | Laguna S 2.1Not measured | MarginNo overlap | Qwen3.5-27B82.7 |
| Multilingual | Laguna S 2.1Not measured | MarginNo overlap | Qwen3.5-27B82.2 |
| Inst. Following | Laguna S 2.1Not measured | MarginNo overlap | Qwen3.5-27B95.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 70.2%B 41.6%Winner: Laguna S 2.1Δ 28.6Terminal-Bench 2.0: Laguna S 2.1 scored 70.2%; Qwen3.5-27B scored 41.6%. 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 | Laguna S 2.1 | Qwen3.5-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Laguna S 2.1$0.1 input / $0.2 output | Qwen3.5-27B$0 input / $0 output | Qwen3.5-27B has the lower combined listed price. |
| Generation speedtokens per second | Laguna S 2.1Not available | Qwen3.5-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Laguna S 2.1Not available | Qwen3.5-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Laguna S 2.11M | Qwen3.5-27B262K | Laguna S 2.1 lists the larger context window. |
Benchmark Deep Dive
AgenticLaguna S 2.1 wins6 benchmarks
CodingQwen3.5-27B wins7 benchmarks
| Benchmark | Laguna S 2.1 | Qwen3.5-27B | Result |
|---|---|---|---|
| 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 |
| SWE-bench VerifiedSource | — | 72.4% | Not comparable |
| SWE-RebenchSource | — | 58.9% | Not comparable |
| AA-SciCodeSource | — | 39.5% | Not comparable |
Reasoning3 benchmarks
Knowledge9 benchmarks
| Benchmark | Laguna S 2.1 | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProSource | — | 86.1% | Not comparable |
| SuperGPQASource | — | 65.6% | Not comparable |
| GPQASource | — | 85.5% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 33.8% | Not comparable |
| AA-GPQA DiamondSource | — | 85.8% | Not comparable |
| AA-HLESource | — | 22.2% | Not comparable |
| AA-Omniscience IndexSource | — | -42.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 21.0% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 79.7% | Not comparable |
Multilingual1 benchmarks
| Benchmark | Laguna S 2.1 | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 82.2% | Not comparable |
Multimodal5 benchmarks
Frequently Asked Questions (3)
Which is better, Laguna S 2.1 or Qwen3.5-27B?
Laguna S 2.1 and Qwen3.5-27B 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, Laguna S 2.1 or Qwen3.5-27B?
Qwen3.5-27B has the edge for coding in this comparison, averaging 64.9 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, Laguna S 2.1 or Qwen3.5-27B?
Laguna S 2.1 has the edge for agentic tasks in this comparison, averaging 70.2 versus 52. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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