DeepSeek V4.1 Flash vs Gemini Deep Research
At a Glance
| Compare |  DeepSeek V4.1 FlashDeepSeek |  Gemini Deep ResearchGoogle DeepMind |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | #5 of 44$0.022 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.15DeepSeek ↗ · Sep 10, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.60Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | 1,049K |
| Model facts checked | Sep 10, 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 | DeepSeek-V4.1-Flash | Gemini Deep Research |
|---|---|---|
| Developer | DeepSeek | Google DeepMind |
| Family | Deepseek V4 1 | Gemini Agents |
| Model | DeepSeek-V4.1-Flash | Gemini Deep Research |
| Version | DeepSeek-V4.1-Flash | Gemini Deep Research |
| Lifecycle | active | preview |
| Released | 2026-09-10 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Audio, Document |
| Output modalities | Text | Text, Image |
| Context window | 1,049K | 1,049K |
| Total parameters | 763.2B | Unknown |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Together Ai (Standard) | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | agents, chat, fim, generation, reasoning, responses, structured_outputs, tools, vision | generation, reasoning, research, tools |
| Architecture design | Causal Encoder-Decoder (20 encoder + 20 decoder layers) | Unknown |
| Backbone parameters | 552000000000 parameters | Unknown |
| Active parameters during decode | 16000000000 parameters | Unknown |
| Active parameters during prefill | 8000000000 parameters | Unknown |
| Pre-training corpus | 45000000000000 tokens | Unknown |
| Reasoning effort range | 1–100 | Unknown |
| Routed experts per MoE layer | 384 experts | Unknown |
| Routed experts per token | 6 experts | Unknown |
| Transformer layers | 40 layers | Unknown |
DeepSeek V4.1 Flash Capabilities
Gemini Deep Research Capabilities
Related Comparisons
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|---|---|---|---|
DeepSeek-V4-FlashDeepSeek | vs | DeepSeek-V4.1-FlashDeepSeek | family variantstext |
DeepSeek-V4.1-FlashDeepSeek | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs | Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
DeepSeek-V4.1-FlashDeepSeek | vs | Grok 4.6xAI | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  GLM-5.3Z.ai | cross-developer peerstext |
DeepSeek-V4.1-FlashDeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4.1 Flash vs Gemini Deep Research FAQs
Is DeepSeek V4.1 Flash or Gemini Deep Research better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4.1 Flash and Gemini Deep Research, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4.1 Flash or Gemini Deep Research?+
Only DeepSeek V4.1 Flash has a directly sourced input price: $0.15 per million tokens. Only DeepSeek V4.1 Flash has a directly sourced output price: $0.60 per million tokens.
Which has a larger context window, DeepSeek V4.1 Flash or Gemini Deep Research?+
Neither model has a larger sourced context window in this comparison. DeepSeek V4.1 Flash is 1,049K and Gemini Deep Research is 1,049K.
Which performs better in benchmarks, DeepSeek V4.1 Flash 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 DeepSeek V4.1 Flash or Gemini Deep Research be self-hosted?+
DeepSeek V4.1 Flash is the only model in this pair currently marked as self-hostable. DeepSeek V4.1 Flash is open weight; Gemini Deep Research is not marked open weight.
Can DeepSeek V4.1 Flash and Gemini Deep Research understand images?+
DeepSeek V4.1 Flash is 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, DeepSeek V4.1 Flash or Gemini Deep Research?+
DeepSeek V4.1 Flash has the larger sourced maximum output: DeepSeek V4.1 Flash supports 393K and Gemini Deep Research supports 66K output tokens.
Do DeepSeek V4.1 Flash and Gemini Deep Research support reasoning and tool use?+
DeepSeek V4.1 Flash: reasoning, tool calling, and image input. Gemini Deep Research: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4.1 Flash or Gemini Deep Research?+
DeepSeek V4.1 Flash has 3 sourced provider routes; Gemini Deep Research has 2, so DeepSeek V4.1 Flash has broader tracked availability.
Which offers better value, DeepSeek V4.1 Flash 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.