Model comparison
Gemini 3.5 Flash vs MAI-Thinking-1
Head-to-head evidence from 6 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 3.5 Flash #33 (Estimated); MAI-Thinking-1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 3.5 Flash and MAI-Thinking-1 share 6 comparable benchmark results. 5 of 8 categories are comparable. 40 results are unique to Gemini 3.5 Flash; 7 to MAI-Thinking-1.
Updated July 23, 2026- Shared results
- 6
- Gemini 3.5 Flash only
- 40
- MAI-Thinking-1 only
- 7
- Comparable categories
- 5 / 8
Treat this as a split decision. Gemini 3.5 Flash makes more sense if agentic is the priority or you need the larger 1M context window; MAI-Thinking-1 is the better fit if mathematics is the priority.
Confidence note. This is a partial-evidence comparison with 6 shared benchmark results across 4 evidence categories; 5 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Gemini 3.5 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.
Gemini 3.5 Flash 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 3.5 Flash | Δ | MAI-Thinking-1 |
|---|---|---|---|
| Math | Gemini 3.5 Flash32.9 | Margin→ 56.8 | MAI-Thinking-189.7 |
| Agentic | Gemini 3.5 Flash77.2 | Margin← 31.2 | MAI-Thinking-146.0 |
| Knowledge | Gemini 3.5 Flash47.2 | Margin→ 25.3 | MAI-Thinking-172.5 |
| Coding | Gemini 3.5 Flash53.9 | Margin→ 11.6 | MAI-Thinking-165.5 |
| Inst. Following | Gemini 3.5 Flash76.3 | Margin→ 8.7 | MAI-Thinking-185.0 |
| Reasoning | Gemini 3.5 Flash74.7 | MarginNo overlap | MAI-Thinking-1Not measured |
| Multimodal | Gemini 3.5 Flash83.8 | MarginNo overlap | MAI-Thinking-1Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 76.2%B 46%Winner: Gemini 3.5 FlashΔ 30.2Terminal-Bench 2.0: Gemini 3.5 Flash scored 76.2%; MAI-Thinking-1 scored 46%. Gemini 3.5 Flash wins this benchmark. - Source ↗
IFBench
Inst. FollowingA 76.3%B 85%Winner: MAI-Thinking-1Δ 8.7IFBench: Gemini 3.5 Flash scored 76.3%; MAI-Thinking-1 scored 85%. MAI-Thinking-1 wins this benchmark. - Source ↗
GPQA
KnowledgeA 92.2%B 84.2%Winner: Gemini 3.5 FlashΔ 8GPQA: Gemini 3.5 Flash scored 92.2%; MAI-Thinking-1 scored 84.2%. Gemini 3.5 Flash wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.1%B 52.8%Winner: Gemini 3.5 FlashΔ 2.3SWE-bench Pro: Gemini 3.5 Flash scored 55.1%; MAI-Thinking-1 scored 52.8%. Gemini 3.5 Flash wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemini 3.5 Flash | MAI-Thinking-1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 3.5 Flash$1.5 input / $9 output | MAI-Thinking-1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | Gemini 3.5 Flash284.2 tok/s | MAI-Thinking-1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 3.5 Flash18.55 s | MAI-Thinking-1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 3.5 Flash1M | MAI-Thinking-1256K | Gemini 3.5 Flash lists the larger context window. |
Benchmark Deep Dive
AgenticGemini 3.5 Flash wins15 benchmarks
| Benchmark | Gemini 3.5 Flash | MAI-Thinking-1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 76.2% | 46% | Gemini 3.5 Flash leads |
| MCP AtlasSource | 83.6% | — | Not comparable |
| ToolathlonSource | 56.5% | — | Not comparable |
| OSWorld-VerifiedSource | 78.4% | — | Not comparable |
| Finance Agent v2Source | 57.9% | — | Not comparable |
| GDPval-AASource | 1349 | — | Not comparable |
| τ²-bench resultsSource | 95.3% | — | Not comparable |
| GDPval-AASource | 42.4% | — | Not comparable |
| AA Agentic IndexSource | 37.5% | — | Not comparable |
| APEX-Agents-AASource | 47.1% | — | Not comparable |
| Gert LabsSource | 61.85% | — | Not comparable |
| ResearchClawBenchSource | 18.0% | — | Not comparable |
| AA AutomationBenchSource | 42.6% | — | Not comparable |
| AA EnterpriseOps-GymSource | 50.1% | — | Not comparable |
| terminalBenchHardSource | 40.9% | — | Not comparable |
CodingMAI-Thinking-1 wins9 benchmarks
| Benchmark | Gemini 3.5 Flash | MAI-Thinking-1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 76.2% | 46.0% | Gemini 3.5 Flash leads |
| SWE-bench ProSource | 55.1% | 52.8% | Gemini 3.5 Flash leads |
| SciCodeSource | 53.1% | — | Not comparable |
| Vibe Code BenchSource | 48.68% | — | Not comparable |
| cursorBench31Source | 49.8% | — | Not comparable |
| cursorBench32Source | 48.8% | — | Not comparable |
| AA Coding IndexSource | 70.1% | — | Not comparable |
| AA-SciCodeSource | 53.1% | — | Not comparable |
| SWE-bench VerifiedSource | — | 73.5% | Not comparable |
Reasoning6 benchmarks
KnowledgeMAI-Thinking-1 wins11 benchmarks
| Benchmark | Gemini 3.5 Flash | MAI-Thinking-1 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 50.2% | — | Not comparable |
| GPQASource | 92.2% | 84.2% | Gemini 3.5 Flash leads |
| GPQA-DSource | 92.7% | 84.2% | Gemini 3.5 Flash leads |
| HLESource | 40.2% | — | Not comparable |
| AA-Omniscience AccuracySource | 51.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 60.7% | — | Not comparable |
| AA-GPQA DiamondSource | 92.2% | — | Not comparable |
| AA-HLESource | 41.0% | — | Not comparable |
| AA-Omniscience IndexSource | 22.7% | — | Not comparable |
| MMLU-ProSource | — | 85% | Not comparable |
| SimpleQASource | — | 31% | Not comparable |
MathMAI-Thinking-1 wins5 benchmarks
Multimodal5 benchmarks
Frequently Asked Questions (6)
Which is better, Gemini 3.5 Flash or MAI-Thinking-1?
Gemini 3.5 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 knowledge tasks, Gemini 3.5 Flash or MAI-Thinking-1?
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 72.5 versus 47.2. Inside this category, GPQA-D is the benchmark that creates the most daylight between them.
Which is better for coding, Gemini 3.5 Flash or MAI-Thinking-1?
MAI-Thinking-1 has the edge for coding in this comparison, averaging 65.5 versus 53.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for math, Gemini 3.5 Flash or MAI-Thinking-1?
MAI-Thinking-1 has the edge for math in this comparison, averaging 89.7 versus 32.9. Gemini 3.5 Flash stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, Gemini 3.5 Flash or MAI-Thinking-1?
Gemini 3.5 Flash has the edge for agentic tasks in this comparison, averaging 77.2 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for instruction following, Gemini 3.5 Flash or MAI-Thinking-1?
MAI-Thinking-1 has the edge for instruction following in this comparison, averaging 85 versus 76.3. Inside this category, IFBench is the benchmark that creates the most daylight between them.
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