Faster agentic coding with image input and 1M context
Run instantly. Pay only for what you use.
DeepSeek V4.1 Flash 1017 is DeepSeek's October update to V4.1 Flash, released under MIT on the same architecture: a 552B-parameter MoE activating 16B per token, with a 1M-token context window and native image input.
DeepSeek reports gains on every benchmark it published against V4.1 Flash: 42.3 on Terminal-Bench 4.0 (up from 31.2), 77.0 on DeepSWE 1.1 (up from 74.2) and 60.5 on Automation-Bench (up from 54.8), while finishing tasks faster with shorter outputs.
It keeps the same prompt format, function calling, JSON output and 1–100 reasoning-effort dial, so a V4.1 Flash integration switches by changing the model name.
42.3on Terminal-Bench 4.0
Up from 31.2 for DeepSeek V4.1 Flash, from MIT-licensed open weights.
77.0on DeepSWE 1.1
Up from 74.2 for V4.1 Flash on real software-engineering issues.
1Mtoken context window
The same architecture and prompt format as V4.1 Flash, so integrations switch by model name.
DeepSeek V4.1 Flash 1017 keeps everything V4.1 Flash does and gets there faster: the same long context, image input and reasoning dial, with higher scores and shorter outputs on agentic work.
Holds a plan across long tool-call chains: 42.3 on Terminal-Bench 4.0, 77.0 on DeepSWE 1.1 and 29.1 on ProgramBench, up from 31.2, 74.2 and 20.3 for V4.1 Flash.
Reads screenshots, charts, and scanned pages directly: 83.0 on Chartography and 92.5 on BabyVision with tools, with no separate vision model in the loop.
An integer from 1 to 100 (with low, high, and max presets) sets thinking depth per request, and effort none skips reasoning entirely — pay for depth only where a request needs it.
Strict function calling and JSON mode at every reasoning effort — tool arguments and typed pipelines that parse the first time.
Long-running coding agents
42.3 on Terminal-Bench 4.0 and 77.0 on DeepSWE 1.1, up from 31.2 and 74.2 for V4.1 Flash, with shorter outputs per task.
Agents grounded in screenshots and charts
83.0 on Chartography and 92.5 on BabyVision with tools, up from 78.9 and 89.6 for V4.1 Flash.
Browser and workflow automation
60.5 on Automation-Bench and 72.1 on Apex Bench 1.1, up from 54.8 and 64.6 for V4.1 Flash.
Whole-repo and long-document analysis
The 1M-token window holds a full repository or document set in one prompt.
Fact recall without retrieval
Standalone knowledge recall is limited for a sparse-activation model — pair it with RAG for factual workloads.
Frontier-grade open-ended reasoning
42.6 on Humanity’s Last Exam without tools trails the largest frontier models. Give research-style questions retrieval and tools.
Audio or video inputs
Inputs are text and images only. Use GLM 5.3 Flash for video, and transcribe audio upstream before sending it to the model.
42.3Terminal-Bench 4.0
Agentic coding and shell tasks run end-to-end in a real terminal.
77.0DeepSWE 1.1
Resolves real software-engineering issues across long tool-call chains.
60.5Automation-Bench
Tool-driven automation across real applications and workflows.
Code examples
curl -X POST "https://api.entrim.ai/v1/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $ENTRIM_API_KEY" \ -d '{ "model": "deepseek-ai/DeepSeek-V4.1-Flash-1017", "messages": [ { "role": "user", "content": "Why do they call it a building if it is already built?" } ] }'SDKs & docs
Use familiar SDK patterns with quickstart examples for common production setups.
OpenAI-compatible
Keep the request format your team already knows.
No cold starts
Production requests are served without model spin-up delays.
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