Model comparison
Laguna S 2.1 vs Qwen3.6-27B
Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Laguna S 2.1 unranked (Not scored); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Laguna S 2.1 and Qwen3.6-27B share 4 comparable benchmark results. 2 of 8 categories are comparable. 2 results are unique to Laguna S 2.1; 50 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 4
- Laguna S 2.1 only
- 2
- Qwen3.6-27B only
- 50
- 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; Qwen3.6-27B 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 4 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
Laguna S 2.1 and Qwen3.6-27B 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 Qwen3.6-27B. That is roughly Infinityx on output cost alone. Laguna S 2.1 gives you the larger context window at 1M, compared with 262K for Qwen3.6-27B.
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 | Qwen3.6-27B |
|---|---|---|---|
| Coding | Laguna S 2.159.4 | Margin→ 18.1 | Qwen3.6-27B77.5 |
| Agentic | Laguna S 2.170.2 | Margin← 10.9 | Qwen3.6-27B59.3 |
| Knowledge | Laguna S 2.1Not measured | MarginNo overlap | Qwen3.6-27B53.3 |
| Math | Laguna S 2.1Not measured | MarginNo overlap | Qwen3.6-27B89.2 |
| Multimodal | Laguna S 2.1Not measured | MarginNo overlap | Qwen3.6-27B76.7 |
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 59.3%Winner: Laguna S 2.1Δ 10.9Terminal-Bench 2.0: Laguna S 2.1 scored 70.2%; Qwen3.6-27B scored 59.3%. Laguna S 2.1 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 59.4%B 53.5%Winner: Laguna S 2.1Δ 5.9SWE-bench Pro: Laguna S 2.1 scored 59.4%; Qwen3.6-27B scored 53.5%. 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 | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Laguna S 2.1$0.1 input / $0.2 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | Laguna S 2.1Not available | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Laguna S 2.1Not available | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Laguna S 2.11M | Qwen3.6-27B262K | Laguna S 2.1 lists the larger context window. |
Benchmark Deep Dive
AgenticLaguna S 2.1 wins11 benchmarks
| Benchmark | Laguna S 2.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 70.2% | 59.3% | Laguna S 2.1 leads |
| Toolathlon-VerifiedSource | 49.7% | — | Not comparable |
| Claw-EvalSource | — | 72.4% | Not comparable |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
| AA Agentic IndexSource | — | 27.0% | Not comparable |
| τ²-bench resultsSource | — | 94.2% | Not comparable |
| GDPval-AASource | — | 32.0% | Not comparable |
| GDPval-AASource | — | 1140 | Not comparable |
| Gert LabsSource | — | 54.84% | Not comparable |
CodingQwen3.6-27B wins9 benchmarks
| Benchmark | Laguna S 2.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 70.2% | 59.3% | Laguna S 2.1 leads |
| SWE MultilingualSource | 78.5% | 71.3% | Laguna S 2.1 leads |
| SWE-bench ProSource | 59.4% | 53.5% | Laguna S 2.1 leads |
| deepSweSource | 40.4% | — | Not comparable |
| SWE-bench VerifiedSource | — | 77.2% | Not comparable |
| LiveCodeBenchSource | — | 83.9% | Not comparable |
| NL2RepoSource | — | 36.2% | Not comparable |
| AA Coding IndexSource | — | 53.7% | Not comparable |
| AA-SciCodeSource | — | 39.8% | Not comparable |
Reasoning2 benchmarks
Knowledge12 benchmarks
| Benchmark | Laguna S 2.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| MMLU-ProSource | — | 86.2% | Not comparable |
| MMLU-ReduxSource | — | 93.5% | Not comparable |
| SuperGPQASource | — | 66% | Not comparable |
| C-EvalSource | — | 91.4% | Not comparable |
| GPQASource | — | 87.8% | Not comparable |
| HLESource | — | 24% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 37.0% | Not comparable |
| AA-GPQA DiamondSource | — | 84.2% | Not comparable |
| AA-HLESource | — | 21.6% | Not comparable |
| AA-Omniscience IndexSource | — | -19.8% | Not comparable |
| AA-Omniscience AccuracySource | — | 19.2% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 48.3% | Not comparable |
Math5 benchmarks
Multimodal16 benchmarks
| Benchmark | Laguna S 2.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| MMMUSource | — | 82.9% | Not comparable |
| MMMU-ProSource | — | 75.8% | Not comparable |
| RealWorldQASource | — | 84.1% | Not comparable |
| DynaMathSource | — | 85.6% | Not comparable |
| MStarSource | — | 81.4% | Not comparable |
| SimpleVQASource | — | 56.1% | Not comparable |
| CharXivSource | — | 78.4% | Not comparable |
| CC-OCRSource | — | 81.2% | Not comparable |
| CountBenchSource | — | 97.8% | Not comparable |
| RefCOCO (avg)Source | — | 92.5% | Not comparable |
| ERQASource | — | 62.5% | Not comparable |
| Video-MME (with subtitle)Source | — | 87.7% | Not comparable |
| VideoMMMUSource | — | 84.4% | Not comparable |
| MLVU (M-Avg)Source | — | 86.6% | Not comparable |
| V*Source | — | 94.7% | Not comparable |
| AA-MMMU-ProSource | — | 74.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Laguna S 2.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 67.6% | Not comparable |
Frequently Asked Questions (3)
Which is better, Laguna S 2.1 or Qwen3.6-27B?
Laguna S 2.1 and Qwen3.6-27B 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 Qwen3.6-27B?
Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 59.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Laguna S 2.1 or Qwen3.6-27B?
Laguna S 2.1 has the edge for agentic tasks in this comparison, averaging 70.2 versus 59.3. 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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