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Compare models

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

  1. Qwen3.8 27BAlibabaRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Sonar ReasoningPerplexityRemove
  4. GLM 5.3Z.AIRemove

4 is the maximum. Remove one to add another.

qwen3.8-27b vs deepseek-v4.1-flash vs sonar-reasoning vs glm-5.3
AttributeQwen3.8 27Bqwen3.8-27bDeepSeek V4.1 Flashdeepseek-v4.1-flashSonar Reasoningsonar-reasoningGLM 5.3glm-5.3
Pricing
Input$0.45 / 1M$0.30 / 1M$1.00 / 1M$1.40 / 1M
Output$3.20 / 1M$1.20 / 1M$5.00 / 1M$4.40 / 1M
Cache Write (5m)$0.45 / 1M$0.30 / 1M$1.00 / 1M$1.40 / 1M
Cache Write (1h)$0.45 / 1M$0.30 / 1M$1.00 / 1M$1.40 / 1M
Cache Read$0.45 / 1M$0.30 / 1M$1.00 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context262K1M127K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoNo
Function CallingYesYesNoYes
JSON ModeYesYesNoYes
StreamingYesYesNoYes
Catalogue
ProviderAlibabaDeepSeekPerplexityZ.AI
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryQwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.