Gemini 3.5 Flash vs MiniMax M3

On shared benchmark coverage, SWE-bench Verified has Gemini 3.5 Flash at 78 and MiniMax M3 at 80.5, with MiniMax M3 ahead by 2.5 points; SWE-bench Pro has Gemini 3.5 Flash at 55.1 and MiniMax M3 at 59, with MiniMax M3 ahead by 3.9 points; Google-Proof Q&A has Gemini 3.5 Flash at 92.2 and MiniMax M3 at 92.9, with MiniMax M3 ahead by 0.7 points. The largest visible gap is 3.9 points on SWE-bench Pro, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint is close: both models cover vision, multimodal input, reasoning mode, function calling, and tool use. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.

For cost, Gemini 3.5 Flash lists $1.50/1M input and $9/1M output tokens on the cheapest tracked provider, while MiniMax M3 lists tiered pricing: <=512K input tokens (standard) is $0.30/1M input and $1.20/1M output; >512K input tokens is $0.60/1M input and $2.40/1M output. A 70/30 input-output blend puts MiniMax M3 lower by about $3.18 per million blended tokens. For tiered rows, this cheapest-track view can understate interactive or fast-lane spend, so compare the tier you will actually use. Availability is 4 providers versus 2, so concentration risk also matters.

Choose Gemini 3.5 Flash when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose MiniMax M3 when coding workflow support and lower cheapest-tier input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.