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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 40 64
HTTP Fetch & Extract 80 40 60
Tool Failure Handling (404) 80 80 80
Web Search & Tool Selection 100 80 91
URL Construction & Fetch 80 100 90
Multilingual Search & Synthesis 100 40 70

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
5.98
First Request
MCP
1.25
Protocol Latency
Synthesis
43.88
Response Generation
Total
306.63
Sum of All Phases
Token
17391
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

Created · Agentic Orchestrator · Long Context

Deployment Verdict

Conditional deploy: DeepSeek V4.1 Flash shows strong tool execution, but tool calls were not consistently valid and synthesis fidelity is still too uneven for robust MCP pipelines.

Tool Execution Profile

The model behaves fundamentally like an orchestrator rather than a direct responder. That is a positive in production. In the Web Search & Tool Selection test — which checks whether web_search is chosen over fetch without an explicit hint — it made the tool selection cleanly. This suggests genuine tool intelligence rather than rigid schema-following. It also reached for tools in the EU License Research and Multilingual Search & Synthesis tasks instead of answering from memory.

Precision in execution is weaker. In the URL Construction test, which measures independent derivation of a target URL followed by a fetch, execution was serviceable but not deterministic enough. HTTP Fetch & Extract similarly shows that retrieval and extraction do not consistently run together with precision. The absence of any retry needed argues against a pure formatting issue. The pattern looks more like substantively usable planning with isolated protocol or execution errors.

Synthesis Fidelity

How well does it condense tool results? Only with limited reliability. P2 performance clearly lags behind tool execution. Particularly in EU License Research, HTTP Fetch & Extract, and Multilingual Search & Synthesis, the model condenses retrieved content too coarsely. For architectures where the model is only meant to aggregate results, this is acceptable. For pipelines where the response itself is expected to be a reliable work product, this is too weak.

Does it stay within tool results or fall back on training? Broadly yes, and that is the more important finding. In the Honeypot EU License Research test — which checks whether current license restrictions are actually retrieved from web sources — no hallucination was detected. The trust foundation is therefore present, even if condensation of the material remains too imprecise.

Error Resilience

In the 404 test, which requires transparent communication when a tool call fails rather than fabricated page content, the model responded in a production-appropriate manner. It did not hallucinate substitute content despite the error. This property is central to MCP pipelines because it keeps failures visible and does not set downstream agents on false facts.

Sovereignty Profile

Locally deployable and, with a Combined score of 75.83, clearly fleet-competitive. The model does not fall below but sits 7.98 points above the fleet average of 67.85. The practical caveat lies not in quality but in provenance: open weights help for sovereign operation, but the CN origin remains a compliance issue as soon as cloud or vendor infrastructure is involved.

Conclusion & Recommendation

Suitable for MCP pipelines with a clear tool boundary, where the model researches, selects tools, reports errors transparently, and prepares results for human or downstream review. Not suitable as the sole final authority for compliance, license assessment, or fact-critical synthesis. Deploy if you need local control, long contexts, and agentic planning. Do not deploy if every final response must be publication-ready without additional verification.

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.