MiniMax M2.7 vs Gemini Deep Research Max
At a Glance
| Compare |  MiniMax M2.7MiniMax |  Gemini Deep Research MaxGoogle DeepMind |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.25Deepinfra ↗ · Sep 3, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $1.00Deepinfra ↗ · Sep 3, 2026 | Not reported |
| Context windowMaximum documented tokens | 205K | 1,049K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | MiniMax-M2.7 | Gemini Deep Research Max |
|---|---|---|
| Developer | MiniMax | Google DeepMind |
| Family | Minimax M2 7 | Gemini Agents |
| Model | MiniMax-M2.7 | Gemini Deep Research Max |
| Version | MiniMax-M2.7 | Gemini Deep Research Max |
| Lifecycle | active | preview |
| Released | 2026-03-18 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video, Audio, Document |
| Output modalities | Text | Text, Image |
| Context window | 205K | 1,049K |
| Total parameters | 228.7B | Unknown |
| Active parameters | Unknown | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | chat, generation, tools | generation, reasoning, research, tools |
MiniMax M2.7 Capabilities
Gemini Deep Research Max Capabilities
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Primary Evidence
Sources and Freshness
Questions
MiniMax M2.7 vs Gemini Deep Research Max FAQs
Is MiniMax M2.7 or Gemini Deep Research Max better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both MiniMax M2.7 and Gemini Deep Research Max, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, MiniMax M2.7 or Gemini Deep Research Max?+
Only MiniMax M2.7 has a directly sourced input price: $0.25 per million tokens. Only MiniMax M2.7 has a directly sourced output price: $1.00 per million tokens.
Which has a larger context window, MiniMax M2.7 or Gemini Deep Research Max?+
Gemini Deep Research Max has the larger sourced context window. MiniMax M2.7 supports 205K and Gemini Deep Research Max supports 1,049K.
Which performs better in benchmarks, MiniMax M2.7 or Gemini Deep Research Max?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can MiniMax M2.7 or Gemini Deep Research Max be self-hosted?+
MiniMax M2.7 is the only model in this pair currently marked as self-hostable. MiniMax M2.7 is open weight; Gemini Deep Research Max is not marked open weight.
Can MiniMax M2.7 and Gemini Deep Research Max understand images?+
MiniMax M2.7 is not documented with image input; Gemini Deep Research Max is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, MiniMax M2.7 or Gemini Deep Research Max?+
Neither has a larger sourced maximum output. MiniMax M2.7 is — and Gemini Deep Research Max is 66K.
Do MiniMax M2.7 and Gemini Deep Research Max support reasoning and tool use?+
MiniMax M2.7: tool calling. Gemini Deep Research Max: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, MiniMax M2.7 or Gemini Deep Research Max?+
MiniMax M2.7 has 5 sourced provider routes; Gemini Deep Research Max has 2, so MiniMax M2.7 has broader tracked availability.
Which offers better value, MiniMax M2.7 or Gemini Deep Research Max?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.