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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. DeepSeek V4.1 Flash 1017DeepSeekRemove
  3. Perceptron Mk1.5PerceptronRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

qwen3.8-27b vs deepseek-v4.1-flash-1017 vs perceptron-mk1.5 vs gemini-4-argon
AttributeQwen3.8 27Bqwen3.8-27bDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017Perceptron Mk1.5perceptron-mk1.5Gemini 4 Argongemini-4-argon
Pricing
Input$0.45 / 1M$0.10 / 1M$0.15 / 1M$2.00 / 1M
Output$3.20 / 1M$0.50 / 1M$1.50 / 1M$10.00 / 1M
Cache Write (5m)$0.45 / 1M$0.10 / 1M$0.15 / 1M$2.00 / 1M
Cache Write (1h)$0.45 / 1M$0.10 / 1M$0.15 / 1M$2.00 / 1M
Cache Read$0.45 / 1M$0.10 / 1M$0.15 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context262K1M36.9K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoYes
Function CallingYesYesNoYes
JSON ModeYesYesNoYes
StreamingYesYesYesYes
Catalogue
ProviderAlibabaDeepSeekPerceptronGoogle
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released——2026-09-25—
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 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.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.