Model comparison
Laguna S 2.1 vs MiniMax M2.7
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: Laguna S 2.1 unranked (Not scored); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Laguna S 2.1 and MiniMax M2.7 share 3 comparable benchmark results. 2 of 8 categories are comparable. 3 results are unique to Laguna S 2.1; 32 to MiniMax M2.7.
Updated July 21, 2026- Shared results
- 3
- Laguna S 2.1 only
- 3
- MiniMax M2.7 only
- 32
- Comparable categories
- 2 / 8
Treat this as a split decision. Laguna S 2.1 makes more sense if agentic is the priority or you want the cheaper token bill; MiniMax M2.7 is the better fit if you would rather avoid the extra latency and token burn of a reasoning model.
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
Laguna S 2.1 and MiniMax M2.7 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.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.10 input / $0.20 output per 1M tokens for Laguna S 2.1. That is roughly 6.0x on output cost alone. Laguna S 2.1 is the reasoning model in the pair, while MiniMax M2.7 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 200K for MiniMax M2.7.
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 | MiniMax M2.7 |
|---|---|---|---|
| Agentic | Laguna S 2.170.2 | Margin← 13.2 | MiniMax M2.757.0 |
| Coding | Laguna S 2.159.4 | Margin← 6.1 | MiniMax M2.753.3 |
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 57%Winner: Laguna S 2.1Δ 13.2Terminal-Bench 2.0: Laguna S 2.1 scored 70.2%; MiniMax M2.7 scored 57%. Laguna S 2.1 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 59.4%B 56.2%Winner: Laguna S 2.1Δ 3.2SWE-bench Pro: Laguna S 2.1 scored 59.4%; MiniMax M2.7 scored 56.2%. 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 | Laguna S 2.1 | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Laguna S 2.1$0.1 input / $0.2 output | MiniMax M2.7$0.3 input / $1.2 output | Laguna S 2.1 has the lower combined listed price. |
| Generation speedtokens per second | Laguna S 2.1Not available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Laguna S 2.1Not available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Laguna S 2.11M | MiniMax M2.7200K | Laguna S 2.1 lists the larger context window. |
Benchmark Deep Dive
AgenticLaguna S 2.1 wins12 benchmarks
| Benchmark | Laguna S 2.1 | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 70.2% | 57% | Laguna S 2.1 leads |
| Toolathlon-VerifiedSource | 49.7% | — | Not comparable |
| τ²-bench resultsSource | — | 84.8% | Not comparable |
| ToolathlonSource | — | 46.3% | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
| AA Agentic IndexSource | — | 25.6% | Not comparable |
| APEX-Agents-AASource | — | 10.6% | Not comparable |
| GDPval-AASource | — | 32.9% | Not comparable |
| GDPval-AASource | — | 1158 | Not comparable |
| Gert LabsSource | — | 40.40% | Not comparable |
CodingLaguna S 2.1 wins13 benchmarks
| Benchmark | Laguna S 2.1 | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 70.2% | — | Not comparable |
| SWE MultilingualSource | 78.5% | 76.5% | Laguna S 2.1 leads |
| SWE-bench ProSource | 59.4% | 56.2% | Laguna S 2.1 leads |
| deepSweSource | 40.4% | — | Not comparable |
| SWE-bench Verified*Source | — | 75.4% | Not comparable |
| SWE-RebenchSource | — | 51.9% | Not comparable |
| Multi-SWE BenchSource | — | 52.7% | Not comparable |
| VIBE-ProSource | — | 55.6% | Not comparable |
| NL2RepoSource | — | 39.8% | Not comparable |
| Vibe Code BenchSource | — | 27.04% | Not comparable |
| React Native EvalsSource | — | 71.4% | Not comparable |
| AA Coding IndexSource | — | 52.6% | Not comparable |
| AA-SciCodeSource | — | 47.0% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | Laguna S 2.1 | MiniMax M2.7 | Result |
|---|---|---|---|
| GPQA-DSource | — | 87.0% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 38.1% | Not comparable |
| AA-GPQA DiamondSource | — | 87.4% | Not comparable |
| AA-HLESource | — | 28.1% | Not comparable |
| AA-Omniscience IndexSource | — | 0.7% | Not comparable |
| AA-Omniscience AccuracySource | — | 26.1% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 34.4% | Not comparable |
Math1 benchmarks
| Benchmark | Laguna S 2.1 | MiniMax M2.7 | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | — | 80.0% | Not comparable |
Multimodal1 benchmarks
| Benchmark | Laguna S 2.1 | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1275 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Laguna S 2.1 | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 75.7% | Not comparable |
Frequently Asked Questions (3)
Which is better, Laguna S 2.1 or MiniMax M2.7?
Laguna S 2.1 and MiniMax M2.7 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 MiniMax M2.7?
Laguna S 2.1 has the edge for coding in this comparison, averaging 59.4 versus 53.3. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Laguna S 2.1 or MiniMax M2.7?
Laguna S 2.1 has the edge for agentic tasks in this comparison, averaging 70.2 versus 57. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
Choose a model with this week’s evidence
Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.
One email each week. Unsubscribe anytime.