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. Qwen3 VL 235B A22B InstructAlibabaRemove
  2. DeepSeek V4.1 Flash 1017DeepSeekRemove
qwen3-vl-235b-a22b-instruct vs deepseek-v4.1-flash-1017
AttributeQwen3 VL 235B A22B Instructqwen3-vl-235b-a22b-instructDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017
Pricing
Input$0.30 / 1M$0.10 / 1M
Output$1.50 / 1M$0.50 / 1M
Cache Write (5m)$0.30 / 1M$0.10 / 1M
Cache Write (1h)$0.30 / 1M$0.10 / 1M
Cache Read$0.30 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context131.1K1M
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderAlibabaDeepSeek
Categorychatchat
Charge typePay As You GoPay As You Go
Released——
Description
SummaryQwen3-VL-235B-A22B Instruct is an open-weight multimodal model that combines strong language generation with image and video understanding, aimed at general vision-language tasks like VQA, document parsing, chart/table extraction, and multilingual OCR. It features robust perception, spatial grounding, and long-context visual comprehension, and supports agent-style workflows such as multi-image dialogue, video timeline alignment, GUI control, and visual-to-code assistance. With competitive benchmark performance and strong text-only ability, it's well suited for production uses across document AI, OCR, UI assistance, spatial reasoning, and vision-language research.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.