DeepSeek V4 Flash Base vs Gemini 3.5 Flash
At a Glance
| Compare | DeepSeek V4 Flash BaseDeepSeek | Gemini 3.5 FlashGoogle DeepMind |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $1.50Google AI ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $9.00Google AI ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 1,049K | 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 | DeepSeek-V4-Flash-Base | Gemini 3.5 Flash |
|---|---|---|
| Developer | DeepSeek | Google DeepMind |
| Family | Deepseek V4 Flash Base | Gemini 3 |
| Model | DeepSeek-V4-Flash-Base | Gemini 3.5 Flash |
| Version | DeepSeek-V4-Flash-Base | Gemini 3.5 Flash |
| Lifecycle | active | active |
| Released | 2026-04-24 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video, Audio, Document |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,049K |
| Total parameters | 292B | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | Yes | No |
| API available | Unknown | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | generation | chat, generation, reasoning, tools |
DeepSeek V4 Flash Base Capabilities
Gemini 3.5 Flash Capabilities
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Flash Base vs Gemini 3.5 Flash FAQs
Is DeepSeek V4 Flash Base or Gemini 3.5 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Flash Base and Gemini 3.5 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Flash Base or Gemini 3.5 Flash?+
Only Gemini 3.5 Flash has a directly sourced input price: $1.50 per million tokens. Only Gemini 3.5 Flash has a directly sourced output price: $9.00 per million tokens.
Which has a larger context window, DeepSeek V4 Flash Base or Gemini 3.5 Flash?+
Both models have the same sourced context window: 1,048,576 tokens each.
Which performs better in benchmarks, DeepSeek V4 Flash Base or Gemini 3.5 Flash?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V4 Flash Base or Gemini 3.5 Flash be self-hosted?+
DeepSeek V4 Flash Base is documented as self-hostable; Gemini 3.5 Flash is documented as not self-hostable. DeepSeek V4 Flash Base is open weight; Gemini 3.5 Flash is not marked open weight.
Can DeepSeek V4 Flash Base and Gemini 3.5 Flash understand images?+
DeepSeek V4 Flash Base is not documented with image input; Gemini 3.5 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4 Flash Base or Gemini 3.5 Flash?+
Gemini 3.5 Flash has a sourced maximum output of 65,536 tokens; DeepSeek V4 Flash Base's maximum output is not reported. A larger limit cannot be determined.
Do DeepSeek V4 Flash Base and Gemini 3.5 Flash support reasoning and tool use?+
DeepSeek V4 Flash Base: none of these features are definitively sourced. Gemini 3.5 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Flash Base or Gemini 3.5 Flash?+
DeepSeek V4 Flash Base has 0 sourced provider routes; Gemini 3.5 Flash has 2, so Gemini 3.5 Flash has broader tracked availability.
Which offers better value, DeepSeek V4 Flash Base or Gemini 3.5 Flash?+
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.