Skip to content

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. Gemini 3.5 TranscribeGoogleRemove
  2. Qwen3.8 2.4T A95BAlibabaRemove
  3. Gemini 4 ArgonGoogleRemove
gemini-3.5-transcribe vs qwen3.8-2.4t-a95b vs gemini-4-argon
AttributeGemini 3.5 Transcribegemini-3.5-transcribeQwen3.8 2.4T A95Bqwen3.8-2.4t-a95bGemini 4 Argongemini-4-argon
Pricing
Input— Not priced per input token$1.80 / 1M$2.00 / 1M
Output— Not priced per output token$5.40 / 1M$10.00 / 1M
Cache Write (5m)Not applicable$1.80 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$1.80 / 1M$2.00 / 1M
Cache ReadNot applicable$1.80 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K262K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleAlibabaGoogle
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-25——
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.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.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.