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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 VL 8B ThinkingAlibabaRemove
  2. DeepSeek V4.1 Flash 1017DeepSeekRemove
  3. Muse Spark OpenMetaRemove
qwen3-vl-8b-thinking vs deepseek-v4.1-flash-1017 vs muse-spark-open
AttributeQwen3 VL 8B Thinkingqwen3-vl-8b-thinkingDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017Muse Spark Openmuse-spark-open
Pricing
Input$0.18 / 1M$0.10 / 1M$1.25 / 1M
Output$2.10 / 1M$0.50 / 1M$4.25 / 1M
Cache Write (5m)$0.18 / 1M$0.10 / 1M$1.25 / 1M
Cache Write (1h)$0.18 / 1M$0.10 / 1M$1.25 / 1M
Cache Read$0.18 / 1M$0.10 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context256K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAlibabaDeepSeekMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryQwen3-VL-8B-Thinking is the reasoning-focused version of the Qwen3-VL-8B multimodal model, built for advanced visual and textual reasoning across images, documents, and video. It adds deeper vision-language fusion and deliberate reasoning paths, supports very long context (256K–1M tokens), and excels at STEM problem solving, causal analysis, and multi-step video understanding — while retaining strong OCR, multilingual capability, and high-quality text generation.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.Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.