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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Gemini 3.8 FlashGoogleRemove
  2. Claude Fable 5.1AnthropicRemove
  3. Qwen3 Coder NextAlibabaRemove
gemini-3.8-flash vs claude-fable-5.1 vs qwen3-coder-next
AttributeGemini 3.8 Flashgemini-3.8-flashClaude Fable 5.1claude-fable-5.1Qwen3 Coder Nextqwen3-coder-next
Pricing
Input$0.75 / 1M$10.00 / 1M$0.175 / 1M
Output$3.75 / 1M$50.00 / 1M$1.40 / 1M
Cache Write (5m)$0.75 / 1M$12.50 / 1M$0.175 / 1M
Cache Write (1h)$0.75 / 1M$20.00 / 1M$0.175 / 1M
Cache Read$0.75 / 1M$1.00 / 1M$0.175 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262.1K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleAnthropicAlibaba
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.Claude Fable 5.1 is an upgraded version of Fable 5, delivering broad improvements with particularly strong gains in agentic coding, long-running workflows, and professional knowledge work. It excels at large code refactors, front-end and visual code generation, financial analysis, and complex analytical tasks. Compared with Fable 5, it also produces more concise plans and summaries while maintaining strong performance across extended tasks, making it a natural upgrade for existing Fable workflows and a strong option alongside Opus 5 for reasoning-intensive applications.Qwen3-Coder-Next is an open-weight causal language model purpose-built for coding agents and local development workflows. It employs a sparse Mixture-of-Experts (MoE) architecture with 80B total parameters and only 3B activated per token, achieving performance comparable to models with 10–20× higher active compute. This efficiency makes it especially well suited for cost-sensitive, always-on agent deployments. Trained with a strong agentic focus, Qwen3-Coder-Next performs reliably on long-horizon coding tasks, complex tool interactions, and robust recovery from execution failures. With a native 256K context window, it integrates smoothly into real-world CLI and IDE environments and aligns well with common agent scaffolding used by modern coding tools. The model operates exclusively in non-thinking mode and does not emit <think> blocks, simplifying production integration for coding agents.