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CrucibleMark

Hermes 4 14B (Abliterated)

Hermes 4 14B Abliterated is a locally deployable Open Weights variant by NousResearch based on Qwen 3 14B, with safety mechanisms deliberately removed. With 14 billion parameters and a 128,000-token context window, the model targets unfiltered response behavior, creative writing workflows, and scenarios requiring maximum openness. Fully commercially usable under the Apache 2.0 license.

NousResearch Version 4.0 Commercial use permitted Dense 14 B (14 B active) 128 K Context 09/2024 locally tested

  • Open Weights
  • Desktop
  • llama.cpp
  • Text
  • Instruction-Tuned
  • Uncensored
  • Batch

Sovereign Risk: MEDIUM NousResearch is a US-based company; the CLOUD Act is only relevant when using the API, not when running the Open Weights variant locally. Abliteration only increases behavioral openness, not the provenance risk of the 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 40 64
HTTP Fetch & Extract 80 15 47.5
Tool Failure Handling (404) 80 80 80
Web Search & Tool Selection 100 35 70.8
URL Construction & Fetch 80 80 80
Multilingual Search & Synthesis 100 50 75

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

Reliability

  • Tool Call Valid: Yes
  • 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
2.83
First Request
MCP
1.14
Protocol Latency
Synthesis
15.29
Response Generation
Total
115.54
Sum of All Phases
Token
7079
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 · Instruction-Tuned · Uncensored

Deployment Verdict

Conditional deploy, because tool execution is reliable and the overall score of 69.54 falls within a usable range, but the detected hallucination clearly limits the pipeline trust threshold.

Tool Execution Profile

Hermes 4 14B can formally be handed off to an MCP tool chain. Tool calls were valid, no retry was required, and the model’s tool selection shows genuine situational adaptation rather than pure schema-following. On the Web Search & Tool Selection test — which checks whether web_search is chosen over fetch without a hint — it scored P1 100. That speaks to usable tool intelligence in open retrieval situations. On the URL Construction & Fetch test — which measures correct derivation of a target URL from prior knowledge — it landed at P1 80. It can often construct known paths adequately, but not with enough precision for strictly deterministic fetch pipelines. Overall, protocol adherence is strong. The weakness lies not in the call itself, but in what is done with the results after the call.

Synthesis Fidelity

How well does it condense tool results? Only limitedly reliable. P2 50 reveals a clear pattern: the model retrieves information but condenses it inconsistently. Particularly weak is HTTP Fetch & Extract — which measures precise extraction from real page content — at P2 15. Web Search & Tool Selection also remains too coarse in evaluation after correct retrieval, at P2 35. For production pipelines, this means: retrieval succeeds more often than clean, fact-bound synthesis.

Does it stay within the tool result or fall back on training? On the Honeypot EU License Research test — which checks whether current license restrictions are answered from web sources rather than model memory — it formally stayed on the tool path. P2 40 and Content Verification State A indicate only limited reliable synthesis. Since one hallucination was detected overall, this is not merely a quality shortcoming but a security risk: once a model outputs fabricated facts as a tool result, the entire tool infrastructure loses its audit trail.

Error Resilience

The model’s behavior under tool failures is production-appropriate. On the Tool Failure Handling (404) test — which measures transparency when calls fail rather than fabricated fallback content — it scored P2 80. It communicated the error without inventing page content. That is acceptable for real MCP pipelines and considerably more important than linguistic elegance.

Sovereignty Profile

Operable locally without external data transfer, making it attractive for sovereign deployments. With a Sovereignty Gap of -1.37 points below the fleet average of 67.84, it remains fleet-competitive. The cost is not tool usage, but the limited synthesis discipline of the abliterated variant.

Conclusion & Recommendation

Suitable for local, sovereign tool pipelines with human-in-the-loop, for research pre-stages, error diagnosis, and agentic workflows where the tooling does the heavy lifting and downstream validation exists. Not suitable for compliance, regulatory outputs, precise extraction pipelines, or autonomous systems that pass tool results downstream without review. If you are looking for an open local model for MCP orchestration, it is a usable operator. If you need reliable fact-bound synthesis, it is the wrong final stage.

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.