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DeepSeek v4.1 Flash Benchmark: Testing a 1M Context LLM Route

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DeepSeek v4.1 Flash Benchmark: Testing a 1M Context LLM Route

Large context window models have become essential for building AI agents, document pipelines and code analysis tools. While exploring options for my internal workloads, I set up a direct route for DeepSeek v4.1 Flash and ran a simple benchmark to record latency, success rate and operational cost. Iā€™m sharing these observations for anyone else evaluating long-context LLMs.

Model Overview

DeepSeek v4.1 Flash supports a 1M-token context window with an unusually high maximum generation limit of 393K tokens. It includes vision understanding, function calling and streaming responses, and follows the OpenAI-compatible request schema, which makes it easy to drop into existing projects without major code changes.

  • Context window: 1M tokens
  • Max output generation: 393K tokens
  • Features: Chat, Vision, Function Calling, Streaming
  • API format: OpenAI compatible

Benchmark Results

This test ran on a dedicated direct route, measuring stable runtime performance under normal usage.

  • P95 Latency: ~1351 ms
  • Route Success Rate: 100%

Reference Cost Data

Important note: This is internal operational cost for my benchmark setup only. This is NOT a commercial offer or pricing for sale.

Type USD (per 1M tokens)
Input / Prompt $0.204619
Output / Completion $0.818476

Route Identifier

If you want to reproduce or test this specific route setup:
cp-chat/deepseek-v4.1-flash/se-bdijhs/ru-drhf8e

Good Fit Workloads

This model shines for tasks that require loading huge amounts of context and generating very long outputs:

  • AI agent pipelines that keep extended conversation memory
  • Parsing and summarizing hundreds of pages of PDF documents
  • Reading and auditing large code repositories
  • Long-running knowledge-base chatbots

Caveats & Limitations

  • Latency numbers are tied to this specific route and network path; results will differ on other providers or regions.
  • Cost is for this configuration only and should be treated as a reference point.
  • Actual token consumption depends heavily on prompt size, generation length and sampling settings.

Disclosure

This route was built for my own internal AI projects. Iā€™m sharing benchmark metrics and observations as a community resource. This post is not a sales promotion.

If you have questions about the testing methodology, feel free to leave a comment.

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