Model comparison
Claude Opus 4.7 (Adaptive) vs MAI-Thinking-1
Head-to-head evidence from 6 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); MAI-Thinking-1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.7 (Adaptive) and MAI-Thinking-1 share 6 comparable benchmark results. 3 of 8 categories are comparable. 32 results are unique to Claude Opus 4.7 (Adaptive); 7 to MAI-Thinking-1.
Updated July 23, 2026- Shared results
- 6
- Claude Opus 4.7 (Adaptive) only
- 32
- MAI-Thinking-1 only
- 7
- Comparable categories
- 3 / 8
Treat this as a split decision. Claude Opus 4.7 (Adaptive) makes more sense if agentic is the priority or you need the larger 1M context window; MAI-Thinking-1 is the better fit if knowledge is the priority.
Confidence note. This is a partial-evidence comparison with 6 shared benchmark results across 3 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Claude Opus 4.7 (Adaptive) and MAI-Thinking-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.
Claude Opus 4.7 (Adaptive) gives you the larger context window at 1M, compared with 256K for MAI-Thinking-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 | Claude Opus 4.7 (Adaptive) | Δ | MAI-Thinking-1 |
|---|---|---|---|
| Agentic | Claude Opus 4.7 (Adaptive)75.1 | Margin← 29.1 | MAI-Thinking-146.0 |
| Coding | Claude Opus 4.7 (Adaptive)78.6 | Margin← 13.1 | MAI-Thinking-165.5 |
| Knowledge | Claude Opus 4.7 (Adaptive)60.0 | Margin→ 12.5 | MAI-Thinking-172.5 |
| Reasoning | Claude Opus 4.7 (Adaptive)75.8 | MarginNo overlap | MAI-Thinking-1Not measured |
| Math | Claude Opus 4.7 (Adaptive)Not measured | MarginNo overlap | MAI-Thinking-189.7 |
| Multimodal | Claude Opus 4.7 (Adaptive)65.1 | MarginNo overlap | MAI-Thinking-1Not measured |
| Inst. Following | Claude Opus 4.7 (Adaptive)Not measured | MarginNo overlap | MAI-Thinking-185.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 69.4%B 46%Winner: Claude Opus 4.7 (Adaptive)Δ 23.4Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; MAI-Thinking-1 scored 46%. Claude Opus 4.7 (Adaptive) wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 87.6%B 73.5%Winner: Claude Opus 4.7 (Adaptive)Δ 14.1SWE-bench Verified: Claude Opus 4.7 (Adaptive) scored 87.6%; MAI-Thinking-1 scored 73.5%. Claude Opus 4.7 (Adaptive) wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 64.3%B 52.8%Winner: Claude Opus 4.7 (Adaptive)Δ 11.5SWE-bench Pro: Claude Opus 4.7 (Adaptive) scored 64.3%; MAI-Thinking-1 scored 52.8%. Claude Opus 4.7 (Adaptive) wins this benchmark. - Source ↗
GPQA
KnowledgeA 94.2%B 84.2%Winner: Claude Opus 4.7 (Adaptive)Δ 10GPQA: Claude Opus 4.7 (Adaptive) scored 94.2%; MAI-Thinking-1 scored 84.2%. Claude Opus 4.7 (Adaptive) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.7 (Adaptive) | MAI-Thinking-1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.7 (Adaptive)$5 input / $25 output | MAI-Thinking-1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | Claude Opus 4.7 (Adaptive)Not available | MAI-Thinking-1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.7 (Adaptive)Not available | MAI-Thinking-1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.7 (Adaptive)1M | MAI-Thinking-1256K | Claude Opus 4.7 (Adaptive) lists the larger context window. |
Benchmark Deep Dive
AgenticClaude Opus 4.7 (Adaptive) wins12 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | MAI-Thinking-1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 69.4% | 46% | Claude Opus 4.7 (Adaptive) leads |
| BrowseCompSource | 79.3% | — | Not comparable |
| MCP AtlasSource | 77.3% | — | Not comparable |
| OSWorld-VerifiedSource | 78% | — | Not comparable |
| CyberGymSource | 73.1% | — | Not comparable |
| AA Agentic IndexSource | 44.4% | — | Not comparable |
| τ²-bench resultsSource | 88.6% | — | Not comparable |
| GDPval-AASource | 49.8% | — | Not comparable |
| GDPval-AASource | 1495 | — | Not comparable |
| OSWorld 2.0Source | 18.2% | — | Not comparable |
| JobBenchSource | 45.9% | — | Not comparable |
| AA ITBenchSource | 46.7% | — | Not comparable |
CodingClaude Opus 4.7 (Adaptive) wins5 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | MAI-Thinking-1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 87.6% | 73.5% | Claude Opus 4.7 (Adaptive) leads |
| SWE-bench ProSource | 64.3% | 52.8% | Claude Opus 4.7 (Adaptive) leads |
| Terminal-Bench 2.0Source | 69.4% | 46.0% | Claude Opus 4.7 (Adaptive) leads |
| AA Coding IndexSource | 73.6% | — | Not comparable |
| AA-SciCodeSource | 54.5% | — | Not comparable |
Reasoning5 benchmarks
KnowledgeMAI-Thinking-1 wins12 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | MAI-Thinking-1 | Result |
|---|---|---|---|
| GPQASource | 94.2% | 84.2% | Claude Opus 4.7 (Adaptive) leads |
| GPQA-DSource | 94.2% | 84.2% | Claude Opus 4.7 (Adaptive) leads |
| HLESource | 54.7% | — | Not comparable |
| HLE w/o toolsSource | 46.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 53.5% | — | Not comparable |
| AA-GPQA DiamondSource | 91.4% | — | Not comparable |
| AA-HLESource | 39.6% | — | Not comparable |
| AA-Omniscience IndexSource | 26.2% | — | Not comparable |
| AA-Omniscience AccuracySource | 45.8% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 36.2% | — | Not comparable |
| MMLU-ProSource | — | 85% | Not comparable |
| SimpleQASource | — | 31% | Not comparable |
Math4 benchmarks
Multimodal5 benchmarks
Frequently Asked Questions (4)
Which is better, Claude Opus 4.7 (Adaptive) or MAI-Thinking-1?
Claude Opus 4.7 (Adaptive) and MAI-Thinking-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 knowledge tasks, Claude Opus 4.7 (Adaptive) or MAI-Thinking-1?
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 72.5 versus 60. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, Claude Opus 4.7 (Adaptive) or MAI-Thinking-1?
Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 65.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or MAI-Thinking-1?
Claude Opus 4.7 (Adaptive) has the edge for agentic tasks in this comparison, averaging 75.1 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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