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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. Qwen3.8 27BAlibabaRemove
  2. Transcribe 1 ProFish AudioRemove
  3. Gemini 4 ArgonGoogleRemove
  4. DeepSeek V4.1 FlashDeepSeekRemove

4 is the maximum. Remove one to add another.

qwen3.8-27b vs transcribe-1-pro vs gemini-4-argon vs deepseek-v4.1-flash
AttributeQwen3.8 27Bqwen3.8-27bTranscribe 1 Protranscribe-1-proGemini 4 Argongemini-4-argonDeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input$0.45 / 1M— Not priced per input token$2.00 / 1M$0.30 / 1M
Output$3.20 / 1M— Not priced per output token$10.00 / 1M$1.20 / 1M
Cache Write (5m)$0.45 / 1MNot applicable$2.00 / 1M$0.30 / 1M
Cache Write (1h)$0.45 / 1MNot applicable$2.00 / 1M$0.30 / 1M
Cache Read$0.45 / 1MNot applicable$2.00 / 1M$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context262KN/A1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderAlibabaFish AudioGoogleDeepSeek
Categorychatvoicechatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released—2026-09-24——
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.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.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.