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CrucibleMark

DeepSeek V3.2

DeepSeek V3.2 is designed as a Frontier model for language, code, and reasoning, using the same MoE architecture as its predecessor with 671 billion total and 37 billion active parameters. The model operates with a 128,000-token context window, is available as an Open Weights variant for local deployment, and is accessible via cloud API at low prices. The Chinese jurisdiction makes an assessment of cloud deployment necessary.

DeepSeek Version v3.2 Commercial use permitted MoE 671 B (37 B active) 128 K Context 01/2025 $0.14 / $0.28 per 1M

  • Open Weights
  • Frontier
  • OR
  • Text
  • Real-Time

Sovereign Risk: HIGH DeepSeek is a Chinese company and is subject to China’s National Security Law (NSL), which may allow state access to data and models. The BSI issued a warning on 02/04/2025 against using the DeepSeek cloud service; when running the Open Weights variant exclusively on-premises without any data transfer to China, the cloud-specific risk scenario is reduced.

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 80 100 90
Tool Failure Handling (404) 40 40 37
Web Search & Tool Selection 100 40 73
URL Construction & Fetch 80 60 70
Multilingual Search & Synthesis 100 15 57.5

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

Reliability

  • Tool Call Valid: No
  • Retry: Not required
  • Hallucination: 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
2.8
First Request
MCP
0.88
Protocol Latency
Synthesis
11.27
Response Generation
Total
89.72
Sum of All Phases
Token
8648
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

Deployment Verdict

Conditional deploy, because tool execution is broadly viable, but synthesis fidelity — with hallucination signal and an invalid tool call — falls short of what’s needed to run the model unsupervised in critical MCP pipelines.

Tool Execution Profile

DeepSeek V3.2 demonstrates genuine tool intelligence, not just rigid call patterns. In the Web Search & Tool Selection test, which checks the ability to choose between search and direct retrieval without an explicit hint, it selects the right tool confidently. That speaks to usable planning capability in dynamic pipelines. In the URL Construction & Fetch test, which measures the derivation of a target URL from the model’s own knowledge, it performs adequately but not deterministically enough for fragile production paths. The P1 score of 83.33 is therefore solid but not clean enough to infer strict protocol adherence. Critically, at least one tool call was formally invalid. This is not a minor quality detail — it is an integration risk for MCP orchestration, because a single malformed call can halt entire chains. On the positive side, no retry was required. This looks more like isolated protocol imprecision than a fundamental comprehension failure.

Synthesis Fidelity

How well does it consolidate tool results? Only to a limited degree. The P2 performance of 52.50 reveals a clear pattern: strong extraction in HTTP Fetch & Extract, but weak consolidation whenever multiple sources, language switches, or ambiguous error conditions converge. The Multilingual Search & Synthesis test in particular — which measures cross-border research and German-language summarization — comes in at P2=15, which is clearly too weak for productive knowledge pipelines.

Does it stay within tool output or fall back on training data? In the Honeypot EU License Research test, which checks whether current license restrictions genuinely originate from web sources, the model fundamentally stays on the tool path and does not hallucinate. That is the most important positive trust finding. At the same time, the global hallucination signal remains a security risk: once a model in a tool pipeline presents fabricated facts as retrieved content, the infrastructure loses its evidentiary character.

Error Resilience

In the 404 test, which measures transparent handling of failed tool calls, DeepSeek V3.2 does not fabricate page content. That is the minimum production requirement, and it was met. However, the P2=40 indicates that error communication is not precise enough. For users, this means: vague or incomplete error framing rather than clear operational diagnostics.

Operational Profile

Total 89.72s. Call 1 2.80s, MCP latency 0.88s, Call 2 11.27s. Slow for the level of performance delivered. Cost per run is local, making it financially attractive, but the runtime is difficult to justify for interactive or high-volume pipelines.

Conclusion & Recommendation

Suitable for locally operated research and retrieval pipelines with a human in the loop, especially where tool selection matters more than clean final consolidation. Not suitable for compliance, regulatory documentation, customer-facing response chains, or autonomous agent paths where every synthesis must hold up as verifiable tool evidence. If you deploy it, do so behind strict tool call validation, response schema checking, and a second verification step for the final summary.

This analysis was generated automatically based on benchmark data. Model used: GPT 4.5 by OpenAI. The raw data and full methodology are documented in the GitHub project.