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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 3.7 FlashGoogleRemove
  2. Claude Fable 5.1AnthropicRemove
  3. Qwen3.8 2.4T A95BAlibabaRemove
gemini-3.7-flash vs claude-fable-5.1 vs qwen3.8-2.4t-a95b
AttributeGemini 3.7 Flashgemini-3.7-flashClaude Fable 5.1claude-fable-5.1Qwen3.8 2.4T A95Bqwen3.8-2.4t-a95b
Pricing
Input$0.375 / 1M$10.00 / 1M$1.80 / 1M
Output$1.88 / 1M$50.00 / 1M$5.40 / 1M
Cache Write (5m)$0.375 / 1M$12.50 / 1M$1.80 / 1M
Cache Write (1h)$0.375 / 1M$20.00 / 1M$1.80 / 1M
Cache Read$0.375 / 1M$1.00 / 1M$1.80 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262K
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 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning.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.Qwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.