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CrucibleMark

DeepSeek-V4.1-Flash (EXL3) (Thinking)

Eight of 552 billion backbone parameters active during reads, sixteen during writes: DeepSeek-V4.1-Flash shifts compute to where agents touch it most. This card describes a community quant as an EXL3 variant of the official MIT-licensed model from September 10, 2026 — multimodal for text and image, one million tokens of context, operable on shared server memory thanks to quantization.

DeepSeek Version 4.1-Flash Commercial use permitted MoE 763 B (16 B active) 1024 K Context locally tested

  • Open Weights
  • Server
  • vLLM
  • Text
  • Vision
  • Long Context
  • Speculative Decoding
  • Community-Quantisierung
  • Batch

Sovereign Risk: MEDIUM The model was developed by DeepSeek, a company based in China. The Chinese jurisdiction is subject to laws that may allow state access to data, which poses a high risk for cloud services. However, since this is an Open Weights model under an MIT license intended for local operation, the risk to the end user is significantly reduced. In a purely local deployment, no data is transmitted to the vendor or third parties. The ‘medium’ risk rating reflects the potential influence of the origin jurisdiction on training data and model development, while the direct data exfiltration risk in local use is assessed as low. Additionally, this is a community quant (re-quantization by a third party), not an official DeepSeek release; the provenance of the quant must be considered independently of the MIT-licensed original weights.

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 60 70
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
7.19
First Request
MCP
1.21
Protocol Latency
Synthesis
63.1
Response Generation
Total
429.02
Sum of All Phases
Token
20923
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

· Long Context · Speculative Decoding · Community-Quantisierung

Deployment Verdict

Conditional deploy: tool use is strong, but MCP calls are not consistently valid and the synthesis of tool results remains too imprecise for production-grade decisions.

Tool Execution Profile

The model demonstrates genuine tool intelligence. In the Web Search & Tool Selection test — which checks whether the model selects web_search over fetch without being prompted — it reliably identifies the correct access path. This argues against rigid pattern behavior and in favor of usable orchestration competence. EU License Research and Multilingual Search & Synthesis also run cleanly at P1 level.

Execution is weaker when it comes to direct target addressing. In the URL Construction test, it constructs the target URL adequately but not precisely enough for deterministic pipelines. The fact that Tool-Call valid overall is false is the actual production caveat: planning logic is strong, protocol adherence is not consistent throughout. For MCP pipelines with strict schema parsing, a tight call-validation layer before execution is therefore required. Retry was not necessary, so the issue lies more in call accuracy than in fundamental misunderstanding.

Synthesis Fidelity

How well does it synthesize? Only adequately. The P2 performance shows that the model consolidates tool results usably, but not with the precision one should expect for compliance, research, or fact pipelines. This is visible consistently across HTTP Fetch & Extract, URL Construction & Fetch, and EU License Research, each landing at 60 in synthesis. It extracts enough for progress, but not enough for reliable final answers without downstream verification.

Does it stay within the tool result? Mostly yes. In the honeypot EU License Research test — which checks whether current license restrictions are retrieved from web sources rather than answered from training — no hallucination was detected. This is the more important trust signal. It demonstrates discipline toward external evidence, even when the final summary is not formulated sharply enough.

Error Resilience

Acceptable for production. In the 404 test, which measures transparent handling of failing tool calls, the model did not fabricate fallback content. P2=80 matters more here than stylistic concerns: it reports the error rather than hallucinating page content. This preserves pipeline integrity.

Operational Profile

Call 1: 7.19s. Call 2: 63.10s. MCP latency: 1.21s. Total: 429.02s. For a Flash model, this is slow overall. Cost per run: local. Economical to operate in terms of cost; justifiable on time only when local execution and a large context window matter more than throughput.

Conclusion & Recommendation

Suitable for locally operated agent pipelines involving research, tool selection, and human or programmatic post-verification of final synthesis. Not suitable as an unsupervised final decision-maker in compliance, policy, or extraction workflows where the answer must be reliably formulated directly from tool results. If you are looking for a local Open Weights model for MCP orchestration, it is usable. If you want to hand infrastructure over to a model entirely without tight guardrails, not yet.

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.