Model comparison
Gemini 2.5 Flash vs MAI-Thinking-1
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 2.5 Flash #130 (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 Flash and MAI-Thinking-1 share 0 comparable benchmark results. 1 of 8 categories are comparable. 15 results are unique to Gemini 2.5 Flash; 13 to MAI-Thinking-1.
Updated July 23, 2026- Shared results
- 0
- Gemini 2.5 Flash only
- 15
- MAI-Thinking-1 only
- 13
- Comparable categories
- 1 / 8
Treat this as a split decision. Gemini 2.5 Flash 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 0 shared benchmark results across 0 evidence categories; 1 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 Flash 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 Flash 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 Flash gives you the larger context window at 1M, compared with 256K for MAI-Thinking-1.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemini 2.5 Flash | MAI-Thinking-1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 2.5 Flash$0.3 input / $2.5 output | MAI-Thinking-1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | Gemini 2.5 Flash221 tok/s | MAI-Thinking-1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 2.5 Flash0.50 s | MAI-Thinking-1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 2.5 Flash1M | MAI-Thinking-1256K | Gemini 2.5 Flash lists the larger context window. |
Benchmark Deep Dive
Agentic2 benchmarks
Coding4 benchmarks
Reasoning3 benchmarks
Knowledge10 benchmarks
| Benchmark | Gemini 2.5 Flash | MAI-Thinking-1 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.1% | — | Not comparable |
| AA-GPQA DiamondSource | 68.3% | — | Not comparable |
| AA-HLESource | 5.1% | — | Not comparable |
| AA-Omniscience IndexSource | -42.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.5% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 93.3% | — | Not comparable |
| GPQASource | — | 84.2% | 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 (2)
Which is better, Gemini 2.5 Flash or MAI-Thinking-1?
Gemini 2.5 Flash 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 math, Gemini 2.5 Flash or MAI-Thinking-1?
MAI-Thinking-1 has the edge for math in this comparison, averaging 89.7 versus 4.7. Gemini 2.5 Flash stays close enough that the answer can still flip depending on your workload.
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