LLMversus

DeepSeek V3 vs Gemini 2.0 Flash: Pricing, Benchmarks & Verdict (2026)

Pricing verified Apr 20, 2026By LLMversusUpdated June 14, 2026View methodology

⚡ Quick Answer

Gemini 2.0 Flash is significantly cheaper at $0.10/$0.40 per million tokens vs $0.27/$1.10. DeepSeek V3 is stronger for coding with a coding ELO of 1300 vs 1240. Gemini 2.0 Flash is faster at 160 tokens/sec vs 85 tokens/sec. DeepSeek V3 ranks higher overall with an Arena ELO of 1280 vs 1260. Gemini 2.0 Flash offers a larger 1049K context window vs 128K.

Updated: April 20, 2026 · ✓ Pricing verified

Side-by-Side Comparison

FeatureDeepSeek V3Gemini 2.0 Flash
ProviderDeepSeekGoogle
Input Price / 1M tokens$0.259$0.100
Output Price / 1M tokens$0.420$0.400
Context Window
128K
1.048576M
Max Output Tokens
8,192
8,192
Arena ELO
1,280
1,260
Coding ELO
1,300
1,240
TTFT (ms)
220
120
Tokens/sec
85
160
MultimodalNoYes
JSON ModeYesYes
Function CallingYesYes
VisionNoYes
When to Use DeepSeek V3

Choose DeepSeek V3 when you need: excellent coding performance for its price, open-source and self-hostable, very competitive benchmarks, extremely affordable. It excels at coding, general-purpose, cost-sensitive, open-source tasks.

Strengths:

  • Excellent coding performance for its price
  • Open-source and self-hostable
  • Very competitive benchmarks
  • Extremely affordable

Best for:

codinggeneral-purposecost-sensitiveopen-source
When to Use Gemini 2.0 Flash

Choose Gemini 2.0 Flash when you need: extremely fast inference, 1m context window at very low cost, strong multimodal support, great for real-time applications. It excels at chatbots, high-volume, cost-sensitive, multimodal tasks. It is also the more cost-effective option between the two. Its 1049K context window is larger, making it better for long-document processing.

Strengths:

  • Extremely fast inference
  • 1M context window at very low cost
  • Strong multimodal support
  • Great for real-time applications

Best for:

chatbotshigh-volumecost-sensitivemultimodal

Frequently Asked Questions

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