Model comparison
Gemini 2.5 Pro vs MAI-Thinking-1
Head-to-head evidence from 2 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 2.5 Pro #70 (Supported); MAI-Thinking-1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 2.5 Pro and MAI-Thinking-1 share 2 comparable benchmark results. 3 of 8 categories are comparable. 22 results are unique to Gemini 2.5 Pro; 11 to MAI-Thinking-1.
Updated July 23, 2026- Shared results
- 2
- Gemini 2.5 Pro only
- 22
- MAI-Thinking-1 only
- 11
- Comparable categories
- 3 / 8
Treat this as a split decision. Gemini 2.5 Pro makes more sense if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model; MAI-Thinking-1 is the better fit if mathematics is the priority or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 2 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
Gemini 2.5 Pro 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.
MAI-Thinking-1 is the reasoning model in the pair, while Gemini 2.5 Pro 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. Gemini 2.5 Pro 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 | Gemini 2.5 Pro | Δ | MAI-Thinking-1 |
|---|---|---|---|
| Math | Gemini 2.5 Pro11.6 | Margin→ 78.1 | MAI-Thinking-189.7 |
| Knowledge | Gemini 2.5 Pro27.4 | Margin→ 45.1 | MAI-Thinking-172.5 |
| Coding | Gemini 2.5 Pro63.8 | Margin→ 1.7 | MAI-Thinking-165.5 |
| Agentic | Gemini 2.5 ProNot measured | MarginNo overlap | MAI-Thinking-146.0 |
| Inst. Following | Gemini 2.5 ProNot measured | MarginNo overlap | MAI-Thinking-185.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 63.8%B 73.5%Winner: MAI-Thinking-1Δ 9.7SWE-bench Verified: Gemini 2.5 Pro scored 63.8%; MAI-Thinking-1 scored 73.5%. MAI-Thinking-1 wins this benchmark. - Source ↗
GPQA
KnowledgeA 83%B 84.2%Winner: MAI-Thinking-1Δ 1.2GPQA: Gemini 2.5 Pro scored 83%; MAI-Thinking-1 scored 84.2%. MAI-Thinking-1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemini 2.5 Pro | MAI-Thinking-1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 2.5 Pro$1.25 input / $10 output | MAI-Thinking-1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | Gemini 2.5 Pro117 tok/s | MAI-Thinking-1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 2.5 Pro21.19 s | MAI-Thinking-1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 2.5 Pro1M | MAI-Thinking-1256K | Gemini 2.5 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic6 benchmarks
CodingMAI-Thinking-1 wins6 benchmarks
| Benchmark | Gemini 2.5 Pro | MAI-Thinking-1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 63.8% | 73.5% | MAI-Thinking-1 leads |
| Vibe Code BenchSource | 0.40% | — | Not comparable |
| AA Coding IndexSource | 33.3% | — | Not comparable |
| AA-SciCodeSource | 42.8% | — | Not comparable |
| SWE-bench ProSource | — | 52.8% | Not comparable |
| Terminal-Bench 2.0Source | — | 46.0% | Not comparable |
Reasoning3 benchmarks
KnowledgeMAI-Thinking-1 wins11 benchmarks
| Benchmark | Gemini 2.5 Pro | MAI-Thinking-1 | Result |
|---|---|---|---|
| GPQASource | 83% | 84.2% | MAI-Thinking-1 leads |
| HLESource | 18.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 25.8% | — | Not comparable |
| AA-GPQA DiamondSource | 84.4% | — | Not comparable |
| AA-HLESource | 21.1% | — | Not comparable |
| AA-Omniscience IndexSource | -14.3% | — | Not comparable |
| AA-Omniscience AccuracySource | 39.0% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 87.4% | — | Not comparable |
| GPQA-DSource | — | 84.2% | Not comparable |
| MMLU-ProSource | — | 85% | Not comparable |
| SimpleQASource | — | 31% | Not comparable |
MathMAI-Thinking-1 wins5 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (4)
Which is better, Gemini 2.5 Pro or MAI-Thinking-1?
Gemini 2.5 Pro 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, Gemini 2.5 Pro or MAI-Thinking-1?
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 72.5 versus 27.4. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, Gemini 2.5 Pro or MAI-Thinking-1?
MAI-Thinking-1 has the edge for coding in this comparison, averaging 65.5 versus 63.8. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for math, Gemini 2.5 Pro or MAI-Thinking-1?
MAI-Thinking-1 has the edge for math in this comparison, averaging 89.7 versus 11.6. Gemini 2.5 Pro stays close enough that the answer can still flip depending on your workload.
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