MiniMax M2.7 vs Gemini Deep Research
At a Glance
| Compare |  MiniMax M2.7MiniMax |  Gemini Deep ResearchGoogle 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 |
|---|---|---|
| Developer | MiniMax | Google DeepMind |
| Family | Minimax M2 7 | Gemini Agents |
| Model | MiniMax-M2.7 | Gemini Deep Research |
| Version | MiniMax-M2.7 | Gemini Deep Research |
| 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 Capabilities
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MiniMax-M2.7MiniMax | vs | MiniMax-M3MiniMax | family variantstext |
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Primary Evidence
Sources and Freshness
Questions
MiniMax M2.7 vs Gemini Deep Research FAQs
Is MiniMax M2.7 or Gemini Deep Research better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both MiniMax M2.7 and Gemini Deep Research, 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?+
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?+
Gemini Deep Research has the larger sourced context window. MiniMax M2.7 supports 205K and Gemini Deep Research supports 1,049K.
Which performs better in benchmarks, MiniMax M2.7 or Gemini Deep Research?+
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 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 is not marked open weight.
Can MiniMax M2.7 and Gemini Deep Research understand images?+
MiniMax M2.7 is not documented with image input; Gemini Deep Research 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?+
Neither has a larger sourced maximum output. MiniMax M2.7 is — and Gemini Deep Research is 66K.
Do MiniMax M2.7 and Gemini Deep Research support reasoning and tool use?+
MiniMax M2.7: tool calling. Gemini Deep Research: 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?+
MiniMax M2.7 has 5 sourced provider routes; Gemini Deep Research has 2, so MiniMax M2.7 has broader tracked availability.
Which offers better value, MiniMax M2.7 or Gemini Deep Research?+
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.