DeepSeek V4.1 Flash vs Gemini 3.8 Flash Cyber
At a Glance
| Compare |  DeepSeek V4.1 FlashDeepSeek |  Gemini 3.8 Flash CyberGoogle 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 21, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | Not reported |
| Model facts checked | Sep 10, 2026View model evidence → | Sep 2, 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 3.8 Flash Cyber |
|---|---|---|
| Developer | DeepSeek | Google DeepMind |
| Family | Deepseek V4 1 | Gemini 3 |
| Model | DeepSeek-V4.1-Flash | Gemini 3.8 Flash Cyber |
| Version | DeepSeek-V4.1-Flash | Gemini 3.8 Flash Cyber |
| Lifecycle | active | active |
| Released | 2026-09-10 | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | Unknown |
| Total parameters | 763.2B | Unknown |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | No |
| Self-hostable | Yes | No |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Together Ai (Standard) | Unknown |
| Capabilities | agents, chat, fim, generation, reasoning, responses, structured_outputs, tools, vision | automated-patching, cybersecurity, reasoning, vulnerability-detection |
| 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 3.8 Flash Cyber Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
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 3.8 Flash Cyber FAQs
Is DeepSeek V4.1 Flash or Gemini 3.8 Flash Cyber better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4.1 Flash and Gemini 3.8 Flash Cyber, 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 3.8 Flash Cyber?+
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 3.8 Flash Cyber?+
Neither model has a larger sourced context window in this comparison. DeepSeek V4.1 Flash is 1,049K and Gemini 3.8 Flash Cyber is —.
Which performs better in benchmarks, DeepSeek V4.1 Flash or Gemini 3.8 Flash Cyber?+
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 3.8 Flash Cyber 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 3.8 Flash Cyber is not marked open weight.
Can DeepSeek V4.1 Flash and Gemini 3.8 Flash Cyber understand images?+
DeepSeek V4.1 Flash is documented with image input; Gemini 3.8 Flash Cyber is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4.1 Flash or Gemini 3.8 Flash Cyber?+
Neither has a larger sourced maximum output. DeepSeek V4.1 Flash is 393K and Gemini 3.8 Flash Cyber is —.
Do DeepSeek V4.1 Flash and Gemini 3.8 Flash Cyber support reasoning and tool use?+
DeepSeek V4.1 Flash: reasoning, tool calling, and image input. Gemini 3.8 Flash Cyber: reasoning. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4.1 Flash or Gemini 3.8 Flash Cyber?+
DeepSeek V4.1 Flash has 3 sourced provider routes; Gemini 3.8 Flash Cyber has 0, so DeepSeek V4.1 Flash has broader tracked availability.
Which offers better value, DeepSeek V4.1 Flash or Gemini 3.8 Flash Cyber?+
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.