Skip to content
CrucibleMark

DeepSeek V4.1 Flash

DeepSeek V4.1 Flash is the compact variant of the DeepSeek V4.1 family, with 552 billion total parameters and 16 billion active parameters per token. The model is released as Open Weights under the MIT license with image and text processing capabilities. Its 1-million-token context window is designed for long agentic workflows, but due to its size requires server infrastructure for local deployment.

DeepSeek Version 4.1 Commercial use permitted MoE 552 B (16 B active) 1000 K Context 09/2026 $0.15 / $0.6 per 1M

  • Open Weights
  • Server
  • OpenRouter
  • Text
  • Vision
  • Agentic Orchestrator
  • Long Context
  • Batch

Sovereign Risk: HIGH Although DeepSeek V4.1 Flash is released as an Open Weights model under the permissive MIT license, its developer DeepSeek is a China-based company. Chinese jurisdiction carries a high sovereignty and compliance risk, particularly when using cloud services subject to Chinese law.

Tool-use profile: 6 assets in detail

Comparison of asset performance (P1/P2/Combined) against the fleet average

Asset performance (radar)

Score Breakdown vs. Fleet Average

Tool-use details

Asset performance, reliability, and runtime profile

Name P1 P2 Combined
EU License Research 100 60 76
HTTP Fetch & Extract 80 60 70
Tool Failure Handling (404) 80 80 80
Web Search & Tool Selection 100 80 91
URL Construction & Fetch 80 80 80
Multilingual Search & Synthesis 100 60 80

CrucibleMark evaluates tool use across 6 independent tests. Click a test name for details.

Reliability

  • Tool Call Valid: No
  • Retry: Not required
  • Hallucination: Not detected

Reliability measures how consistently a model actually executes tool calls: Tool Call Valid schema and format accepted, Retry Required successful only after retry, Hallucination Flag fabricated tools or parameters detected. All three green means production-ready.

Runtime profile

Call 1
12.9
First Request
MCP
1.43
Protocol Latency
Synthesis
112.25
Response Generation
Total
759.49
Sum of All Phases
Token
18752
Input + Output
Cost
$0
Cost per Run

The runtime profile shows the latency and cost metrics for the model run: Call 1 First Request, MCP Protocol Latency, Synthesis Response Generation, Total sum of all phases, supplemented by Token input and output and Cost cost per run.

Tool-use review

· Agentic Orchestrator · Long Context

Deployment Verdict

Conditional deploy, because tool execution is strong, but tool calls were not consistently valid and synthesis quality is too uneven for production decision pipelines. The combined finding is good, but not stable enough to be trusted in unsupervised end-to-end pipelines.

Tool Execution Profile

DeepSeek V4.1 Flash demonstrates genuine orchestration capability rather than mere pattern-following. In the Web Search & Tool Selection test — which requires choosing between search and direct fetch without an explicit hint — it reliably identifies the need for web_search. That is a strong signal for dynamic MCP pipelines. In the URL Construction & Fetch test, which evaluates deriving a target URL from internal knowledge and then fetching it, it remains usable but not deterministic enough. P1 is strong overall, yet the finding “Tool-Call valid: false” is relevant for production: the model plans and selects correctly in most cases, but does not produce every execution in a protocol-clean manner. Since no retry was required, this looks more like precision loss in the call than a fundamental comprehension problem.

Synthesis Fidelity

How well does it consolidate tool results? Only adequately — not reliably precise. P2 scores remain at 60 in EU License Research, HTTP Fetch & Extract, and Multilingual Search & Synthesis, falling short of execution quality. In practical terms: it finds the data, but consolidation smooths over nuances, lacks structure, or does not prioritize facts cleanly enough for compliance, research, or architecture decisions.

Does it stay within the tool result or fall back on training data? In the honeypot EU License Research test — which checks whether current license restrictions are answered from web sources rather than pre-trained knowledge — it stays on the safe side. No hallucination detected. The trust signal is good. It does not answer the task from assumed world knowledge, even if the summary itself remains too coarse.

Error Resilience

Acceptable for production. In the 404 test, which measures how the model handles a failing tool call, it communicates the error transparently and does not hallucinate page content. This is exactly the behavior a tool pipeline requires: a visible failure rather than a fabricated fallback response.

Sovereignty Profile

Locally deployable and fundamentally attractive for sovereign setups, since Open Weights and an MIT license allow genuine self-hosting. In practice, however, this is constrained by the Server class. The model sits 0.89 points below the fleet average of 68.17. Added to this is a high provenance risk in cloud use due to Chinese jurisdiction. For sovereign deployments, only genuine local operation therefore counts.

Conclusion & Recommendation

Suitable for MCP-assisted research, routing, and agent pipelines where tool selection matters more than perfect final consolidation and where a downstream review step safeguards response quality. Not the first choice for compliance outputs, executive briefings, or other paths where the summary itself must already be decision-ready. If you deploy it, do so as an orchestrator with tight output validation — not as the final authority.

This analysis was generated automatically based on benchmark data. Model used: GPT-5.4 by OpenAI. The raw data and full methodology are documented in the GitHub project.