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There is no universally best model for a security operations center (SOC). Choose the model and reasoning setting that clears your minimum investigative-quality bar while staying within your limits for cost, analysis time, consistency, and usable answers. A Cisco Talos evaluation of 66 model-and-reasoning combinations illustrates why: more reasoning could cost more without improving scores, and some runs failed to produce valid reports.
Why SOC model selection is a constrained decision
A leaderboard can identify a high score, but it cannot tell you whether a model is affordable at your alert volume, fast enough for your response process, or dependable enough to return usable analysis. David J. Bianco of Cisco Talos frames the operational question as: “Which model and reasoning setting gives me enough investigative quality, at a cost, speed, consistency, and failure rate my workflow can tolerate?”
That means evaluating a model as part of a workflow, not in isolation. The prompt, analyst role, tools, and expected output all affect whether its answer can be used. A model that performs well once may still be a poor fit if its results vary sharply between runs or it sometimes refuses or returns invalid output.
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Talos tested 66 model-and-reasoning combinations from Anthropic and OpenAI on a tool-assisted log-review task. Reviewers used common Unix command-line tools to decide whether a dataset was real or synthetic. The dataset was synthetic, but reviewers were told it might be real.
#1 Best Overall
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The test corpus was generated with EvidenceForge, Talos’s open-source synthetic telemetry generator, frozen at version 1.12.0. It represented a shared six-hour enterprise scenario with 80,054 simulated log records across 20 source formats. The corpus comprised 88 files totaling 48.0 MB (45.8 MiB), including Zeek network telemetry, Cisco ASA and Snort perimeter records, Windows and Linux endpoint data, web and proxy logs, and a small set of email artifacts. Models were not given the scenario definitions, generator information, ground truth, or other EvidenceForge metadata. Read the Cisco Talos evaluation.
How the scoring worked
Each condition was tested with four independently prompted personas: Threat Hunter, Detection Engineer, Network Forensics Analyst, and Host/Endpoint Detection and Response (EDR) Analyst. Five rounds were planned for each condition, but a panel counted only when all four reviewers produced valid reports. The panel score was the mean of the four persona scores; the condition’s reported score was the median of its complete panel scores.
This setup matters when interpreting both quality and reliability. A condition could have good scores in completed panels yet still provide too few complete panels for a dependable comparison if many attempts returned invalid output.
Rank #2
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What the results show about quality, speed, and cost
Among the reported conditions, GPT-5.6 Sol Ultra had the highest median score: 96.25 across five of five complete panels. Its observed panel-score range was 95.00–98.00; a panel took 33.72 minutes on average and had an estimated API-equivalent cost of $55.48. GPT-5.6 Sol XHigh scored 92.75, with 24.66 minutes and $38.55 per panel. By contrast, GPT-5.6 Luna Low scored 58.25, taking 3.24 minutes at $0.39 per panel. These are results from Talos’s 2026 evaluation, not current account quotes or predictions for other tasks.
| Condition | Median score | Time per panel | Estimated cost per panel | Complete panels |
|---|---|---|---|---|
| GPT-5.6 Sol Ultra | 96.25 | 33.72 minutes | $55.48 | 5 of 5 |
| GPT-5.6 Sol XHigh | 92.75 | 24.66 minutes | $38.55 | Not stated for this result |
| GPT-5.6 Luna Low | 58.25 | 3.24 minutes | $0.39 | Not stated for this result |
Talos estimated per-panel cost using a public list-price rate card frozen before testing began. It is an API-equivalent estimate, not a current quote or universal account cost; rates may have changed. The measured task was a particular synthetic scenario, so its duration and cost should not be extrapolated directly to a different corpus, prompt, tool setup, or production workload.
Reasoning effort is not a dependable quality dial
Cost generally rose with reasoning effort, but scores did not move reliably in the same direction. GPT-5.6 Sol Max scored 90.00, below XHigh’s 92.75. Luna’s scores declined as effort rose. Claude Opus 4.8 gained eight points from Medium to High, then lost 9.5 points from High to XHigh. Bianco’s concise takeaway is that “Reasoning effort was not a universal quality dial.”
Rank #3
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- Complete Accessories: We provide the accessories you need, including 2 x Pallets, 145 x M5*10 Cross Head Screws, 4 x Casters, 4 x M10*50 Expansion Screws,10 x M6*12 Cage Nuts, 1 x Grounding Wire, 1 x User Manual.
- Wide Application: The server rack wall mount maximizes the use of available space, suitable for retail venues, classrooms, offices, and other places where space is limited.
Do not assume that selecting a higher effort level will improve your results enough to justify its added latency or cost. Test the exact model and setting you intend to use, particularly when a workflow depends on a predictable balance between investigation quality and response time.
Consistency and usable answers belong in the decision
A median can conceal a weak result in one role or a failed attempt. Talos found differences by analyst persona: the Threat Hunter had a median score of 43, Network Forensics and Host/EDR each had 35, and Detection Engineer had 31. The largest typical within-condition, within-round difference was five points between Threat Hunter and Detection Engineer. This is a reminder that an aggregate score may not reflect every task your SOC needs covered.
Failure to return a valid report was also operationally significant. For Claude Sonnet 4.6, 10 of 27 High attempts and 15 of 29 Max attempts returned invalid output. High produced only two of five complete panels, while Max produced none. Anthropic Fable was excluded after safeguards blocked 21 of 31 early attempts, including all eight Max attempts.
Rank #4
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Bianco writes, “Consistency should be a major decision factor.” In practice, record both score variation and usable-answer rate: an analysis that is refused or formatted so it cannot enter the next workflow step may leave the analyst without usable help, regardless of the quality of successful responses.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical method for choosing a model and setting
Talos used a Pareto frontier across score, cost, time, and downside consistency. A candidate is dominated when another option is at least as good across the measures and better on one or more; dominated candidates can be set aside before applying an organization’s own limits. Usable-answer rate should also be tracked, even though failure rate was not itself one of the study’s frontier axes.
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- Set operational thresholds. Define the minimum investigative score, maximum acceptable downside spread, per-task cost ceiling, and maximum wait time your workflow can tolerate. Specify what counts as a usable answer.
- Test representative work. Use cases that reflect your actual logs, tools, analyst tasks, and intended production prompts rather than relying on a single generic benchmark.
- Run repeated trials. For every candidate model and reasoning setting, capture quality, cost, elapsed time, score consistency, and whether the output is usable. Repeated runs reveal whether a strong result is dependable.
- Remove dominated or out-of-bounds candidates. Use the Pareto comparison to identify options with no meaningful advantage, then eliminate any remaining candidate that misses one of your operational thresholds.
- Choose among the survivors based on workflow priorities. If several options qualify, weigh the trade-offs that matter most to your team, such as lower latency for triage or stronger investigative quality for complex cases.
- Re-evaluate when conditions change. Repeat the assessment when prompts, tools, workloads, model behavior, or costs change; treat the prompt and analyst role as part of the system being tested.
How to apply the findings without overgeneralizing
Talos’s evaluation is useful as an example of how to compare models, not as a forecast for every SOC. It tested one synthetic six-hour scenario with five planned rounds per condition, and complete-panel results depended on all four personas producing valid reports. Your own telemetry, tool access, task mix, and production constraints may produce different rankings.
The practical lesson is to choose by threshold and evidence, not by model name or reasoning label alone. Establish the minimum quality you need, measure the costs and delays you can accept, check downside consistency and answer usability, then select from the candidates that satisfy those requirements in your environment.
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