Gemini Deep Research vs Kimi K2 Thinking
At a Glance
| Compare |  Gemini Deep ResearchGoogle DeepMind |  Kimi K2 ThinkingMoonshot AI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.60Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $2.50Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,049K | 262K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 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 | Gemini Deep Research | Kimi-K2-Thinking |
|---|---|---|
| Developer | Google DeepMind | Moonshot AI |
| Family | Gemini Agents | Kimi K2 Thinking |
| Model | Gemini Deep Research | Kimi-K2-Thinking |
| Version | Gemini Deep Research | Kimi-K2-Thinking |
| Lifecycle | preview | active |
| Released | Unknown | 2025-11-06 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text |
| Output modalities | Text, Image | Text |
| Context window | 1,049K | 262K |
| Total parameters | Unknown | 1T |
| Active parameters | Unknown | 32B |
| License | Unknown | other |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | generation, reasoning, research, tools | chat, generation, reasoning, tools |
Gemini Deep Research Capabilities
Kimi K2 Thinking Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini Deep Research vs Kimi K2 Thinking FAQs
Is Gemini Deep Research or Kimi K2 Thinking better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini Deep Research and Kimi K2 Thinking, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini Deep Research or Kimi K2 Thinking?+
Only Kimi K2 Thinking has a directly sourced input price: $0.60 per million tokens. Only Kimi K2 Thinking has a directly sourced output price: $2.50 per million tokens.
Which has a larger context window, Gemini Deep Research or Kimi K2 Thinking?+
Gemini Deep Research has the larger sourced context window. Gemini Deep Research supports 1,049K and Kimi K2 Thinking supports 262K.
Which performs better in benchmarks, Gemini Deep Research or Kimi K2 Thinking?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini Deep Research or Kimi K2 Thinking be self-hosted?+
Kimi K2 Thinking is the only model in this pair currently marked as self-hostable. Gemini Deep Research is not marked open weight; Kimi K2 Thinking is open weight.
Can Gemini Deep Research and Kimi K2 Thinking understand images?+
Gemini Deep Research is documented with image input; Kimi K2 Thinking is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini Deep Research or Kimi K2 Thinking?+
Kimi K2 Thinking has the larger sourced maximum output: Gemini Deep Research supports 66K and Kimi K2 Thinking supports 131K output tokens.
Do Gemini Deep Research and Kimi K2 Thinking support reasoning and tool use?+
Gemini Deep Research: reasoning, tool calling, and image input. Kimi K2 Thinking: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini Deep Research or Kimi K2 Thinking?+
Gemini Deep Research has 2 sourced provider routes; Kimi K2 Thinking has 2, a tie.
Which offers better value, Gemini Deep Research or Kimi K2 Thinking?+
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.