Model comparison
MAI-Thinking-1 vs MiniMax M2.7
Head-to-head evidence from 3 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: MAI-Thinking-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. MAI-Thinking-1 and MiniMax M2.7 share 3 comparable benchmark results. 2 of 8 categories are comparable. 10 results are unique to MAI-Thinking-1; 32 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 3
- MAI-Thinking-1 only
- 10
- MiniMax M2.7 only
- 32
- Comparable categories
- 2 / 8
Treat this as a split decision. MAI-Thinking-1 makes more sense if coding is the priority or you need the larger 256K context window; MiniMax M2.7 is the better fit if agentic is the priority or 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 3 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
MAI-Thinking-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.
MAI-Thinking-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. MAI-Thinking-1 gives you the larger context window at 256K, 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 | MAI-Thinking-1 | Δ | MiniMax M2.7 |
|---|---|---|---|
| Coding | MAI-Thinking-165.5 | Margin← 12.2 | MiniMax M2.753.3 |
| Agentic | MAI-Thinking-146.0 | Margin→ 11.0 | MiniMax M2.757.0 |
| Knowledge | MAI-Thinking-172.5 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | MAI-Thinking-189.7 | MarginNo overlap | MiniMax M2.7Not measured |
| Inst. Following | MAI-Thinking-185.0 | MarginNo overlap | MiniMax M2.7Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 46%B 57%Winner: MiniMax M2.7Δ 11Terminal-Bench 2.0: MAI-Thinking-1 scored 46%; MiniMax M2.7 scored 57%. MiniMax M2.7 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 52.8%B 56.2%Winner: MiniMax M2.7Δ 3.4SWE-bench Pro: MAI-Thinking-1 scored 52.8%; MiniMax M2.7 scored 56.2%. MiniMax M2.7 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MAI-Thinking-1 | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MAI-Thinking-1Not available | MiniMax M2.7$0.3 input / $1.2 output | A complete price comparison is not available. |
| Generation speedtokens per second | MAI-Thinking-1Not available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MAI-Thinking-1Not available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MAI-Thinking-1256K | MiniMax M2.7200K | MAI-Thinking-1 lists the larger context window. |
Benchmark Deep Dive
AgenticMiniMax M2.7 wins11 benchmarks
| Benchmark | MAI-Thinking-1 | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 46% | 57% | MiniMax M2.7 leads |
| 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 |
CodingMAI-Thinking-1 wins13 benchmarks
| Benchmark | MAI-Thinking-1 | MiniMax M2.7 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.5% | — | Not comparable |
| SWE-bench ProSource | 52.8% | 56.2% | MiniMax M2.7 leads |
| Terminal-Bench 2.0Source | 46.0% | — | Not comparable |
| SWE-bench Verified*Source | — | 75.4% | Not comparable |
| SWE-RebenchSource | — | 51.9% | Not comparable |
| SWE MultilingualSource | — | 76.5% | 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 |
Reasoning3 benchmarks
Knowledge11 benchmarks
| Benchmark | MAI-Thinking-1 | MiniMax M2.7 | Result |
|---|---|---|---|
| GPQASource | 84.2% | — | Not comparable |
| GPQA-DSource | 84.2% | 87.0% | MiniMax M2.7 leads |
| MMLU-ProSource | 85% | — | Not comparable |
| SimpleQASource | 31% | — | 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 |
Math4 benchmarks
Multimodal1 benchmarks
| Benchmark | MAI-Thinking-1 | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1275 | Not comparable |
Frequently Asked Questions (3)
Which is better, MAI-Thinking-1 or MiniMax M2.7?
MAI-Thinking-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, MAI-Thinking-1 or MiniMax M2.7?
MAI-Thinking-1 has the edge for coding in this comparison, averaging 65.5 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, MAI-Thinking-1 or MiniMax M2.7?
MiniMax M2.7 has the edge for agentic tasks in this comparison, averaging 57 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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