Skip to main content
idapt
HomeCodeAI ModelsPricing
Sign inStart free
  • Home
  • Pricing
  • AI Models
  • Image models
  • Voice models
  • Video models
  • Rankings
  • New models
  • Model status
  • Multi-Model Chat
  • Voice Mode
  • Voice HUD
  • Web Search
  • Image Generation
  • Video Generation
  • Audio Generation
  • Transcription
  • Drive
  • Credentials
  • Sharing
  • Workspaces
  • Tasks
  • Memory
  • Agents
  • Subagents
  • Automations
  • Skills
  • idapt Code
  • Code Execution
  • Computers
  • Computer Use
  • Computer Assist · Soon
  • Containers · Soon
  • Cloud Computers
  • Local AI
  • AI Gateway
  • API & SDK
  • CLI
  • MCP
  • Tunnels
  • All features →
  • LLM cost calculator
  • Token counter
  • Context window checker
  • Can I run it
  • Model picker quiz
  • Savings finder
  • Video cost estimator
  • Text to speech cost
  • Transcription cost
  • API endpoint tester
  • All free tools →
  • Blog
  • Use cases
  • Comparisons
  • Best of
  • Skills
  • Learn
  • Changelog
  • Help center
  • FAQ
  • Privacy
  • Compare all models
  • Support
  • idapt Code
  • Developers
  • Quickstarts
  • API reference
  • API pricing
  • CLI
  • MCP
  • Downloads
  • Desktop
  • Badges and embeds
© idapt[email protected]TermsPrivacy PolicyLegal noticeReport content
X (Twitter)

Qwen3.5 397B A17B vs Gemini 3.5 Flash-Lite

At a glance

Qwen's largest MoE model with 397B total parameters, 17B active.

262K ctx$0.39/$2.34 per M

Google's high-efficiency model for focused subagent work and multi-agent workflows.

1.0M ctx$0.30/$2.50 per M
Agent Demos
All agent demos

No shared agent demos for these models yet.

AI Output
All AI outputs

No captured outputs for these models yet.

Benchmarks
See rankings
No benchmarks available

This model hasn't been benchmarked yet.

No benchmarks available

This model hasn't been benchmarked yet.

Specs
262K
Context
66K
Max output
Feb 2026
Released
1.0M
Context
66K
Max output
Jul 2026
Released
Capabilities
Vision
Audio
Reasoning
Vision
Audio
Reasoning
Pricing
Live pricing, shown before you run
Input$0.39/M tokens
Output6%$2.34/M tokens
Live pricing, shown before you run
Input23%$0.30/M tokens
Output$2.50/M tokens
Reasoning$2.50/M tokens
Cache read$0.03/M tokens
Cache write$0.08/M tokens
Image input$0.30/M tokens
Audio input$0.30/M tokens
Web search$0.01/search
Chat with Qwen3.5 397B A17BGo to model
Chat with Gemini 3.5 Flash-LiteGo to model
Frequently Asked Questions: Qwen3.5 397B A17B vs Gemini 3.5 Flash-Lite▼

Which is better, Qwen3.5 397B A17B or Gemini 3.5 Flash-Lite?

▼

Both Qwen3.5 397B A17B and Gemini 3.5 Flash-Lite are capable AI models. The best choice depends on your specific use case: consider pricing, context window, speed, and which capabilities (vision, reasoning, audio) you need.

Which is cheaper, Qwen3.5 397B A17B or Gemini 3.5 Flash-Lite?

▼

Gemini 3.5 Flash-Lite has a lower blended cost. Qwen3.5 397B A17B: $0.39 input / $2.34 output. Gemini 3.5 Flash-Lite: $0.30 input / $2.50 output.

Which has a larger context window, Qwen3.5 397B A17B or Gemini 3.5 Flash-Lite?

▼

Gemini 3.5 Flash-Lite has a larger context window: Qwen3.5 397B A17B supports 262K tokens vs Gemini 3.5 Flash-Lite at 1M tokens.

Do both Qwen3.5 397B A17B and Gemini 3.5 Flash-Lite support vision?

▼

Yes, both Qwen3.5 397B A17B and Gemini 3.5 Flash-Lite support vision/image input.

What are the key differences between Qwen3.5 397B A17B and Gemini 3.5 Flash-Lite?

▼

Qwen3.5 397B A17B and Gemini 3.5 Flash-Lite have similar overall capabilities. The main differences lie in pricing, context window size, and provider-specific strengths. Use this comparison page to review all metrics side by side.

Which model should I choose for my use case?

▼

If cost is your priority, choose the cheaper option. If you need the highest intelligence for complex tasks, pick the higher-scoring model. For long documents or codebases, choose the larger context window. You can try both Qwen3.5 397B A17B and Gemini 3.5 Flash-Lite for free on idapt.app to see which performs better for your specific needs.

Open in App