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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. Claude Opus 4.5AnthropicRemove
  2. DeepSeek V4.1 Flash 1017DeepSeekRemove
claude-opus-4-5-20251101 vs deepseek-v4.1-flash-1017
AttributeClaude Opus 4.5claude-opus-4-5-20251101DeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017
Pricing
Input$5.00 / 1M$0.10 / 1M
Output$25.00 / 1M$0.50 / 1M
Cache Write (5m)$6.25 / 1M$0.10 / 1M
Cache Write (1h)$10.00 / 1M$0.10 / 1M
Cache Read$0.50 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context200K1M
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderAnthropicDeepSeek
Categorychatchat
Charge typePay As You GoPay As You Go
Released——
Description
SummaryClaude Opus 4.5 is Anthropic's frontier reasoning model, built for complex engineering, agent workflows, and long computer-use tasks. It offers strong multimodal skills, better security against prompt injection, and flexible effort controls — including a Verbosity setting to trade speed vs. depth and token use. With advanced tool use, long-context handling, and support for coordinated multi-agent setups, it excels at research, debugging, multi-step planning, and UI/spreadsheet automation while improving reliability, alignment, and efficiency over earlier Opus versions.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.