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There is no universal best model for a security operations center (SOC). Choose the model and reasoning setting that meet your minimum investigative-quality bar while staying within your limits for cost, turnaround time, consistency, and unusable or refused answers. Cisco Talos’s 2026 evaluation of 66 model-and-reasoning combinations shows why this is an operational trade-off, not a leaderboard contest.
What should you compare when choosing an AI SOC model?
David J. Bianco of Cisco Talos frames the decision this way: “Which model and reasoning setting gives me enough investigative quality, at a cost, speed, consistency, and failure rate my workflow can tolerate?” Those dimensions are related, but a high score alone does not capture whether a model is practical in your environment.
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- Investigative quality: How well does the model analyze evidence and produce a useful report?
- Time: How long does the work take at the panel or task scale relevant to your workflow?
- Cost: What does each analysis cost at your actual usage and account rates?
- Consistency: How much does performance vary between repeated runs or between analyst roles?
- Usable-answer rate: How often does the system return analysis in a form your workflow can use, rather than refusing or producing invalid output?
Talos’s finding is especially important for teams tempted to treat a reasoning setting as a quality slider: “Reasoning effort was not a universal quality dial.” More effort sometimes improved results, but not reliably.
What did the Talos evaluation test?
Talos evaluated 66 combinations of models and reasoning settings from Anthropic and OpenAI on a tool-assisted log-review task. Reviewers had to determine whether a dataset was real or synthetic. It was synthetic, but they were told it might be real.
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The test corpus came from EvidenceForge, Talos’s open-source synthetic telemetry generator, frozen at version 1.12.0. Its six-hour enterprise scenario contained 80,054 simulated log records in 20 source formats, packaged as 88 files totaling 48.0 MB (45.8 MiB). The data included 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. The models were not given the scenario definitions, generator information, ground truth, or other EvidenceForge metadata.
Each condition used four independently prompted personas: Threat Hunter, Detection Engineer, Network Forensics Analyst, and Host/Endpoint Detection and Response (EDR) Analyst. Talos planned five rounds per condition, but counted a panel only when all four reviewers produced valid reports. A panel score was the mean of the four persona scores; the condition score was the median of its complete panel scores.
What do the results show about quality, time, and cost?
The results offer concrete examples of different trade-offs. The figures below are Talos’s observed results for this particular synthetic scenario and test setup, not a forecast for another SOC.
| Model and reasoning setting | Median score | Time per panel | Cost per panel | Observed range or completion |
|---|---|---|---|---|
| GPT-5.6 Sol Ultra | 96.25 | 33.72 minutes | $55.48 | 95.00–98.00; 5/5 complete panels |
| GPT-5.6 Sol XHigh | 92.75 | 24.66 minutes | $38.55 | Not stated in the cited result |
| GPT-5.6 Luna Low | 58.25 | 3.24 minutes | $0.39 | Not stated in the cited result |
Talos estimated per-panel cost using a public list-price rate card frozen before testing began. These are API-equivalent estimates, not current quotes or universal account costs; rates may have changed. Your actual cost depends on the models, usage, and pricing available to your organization.
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More reasoning effort did not guarantee a better score
Cost generally rose with reasoning effort, but scores did not consistently rise alongside it. GPT-5.6 Sol Max scored 90.00, below XHigh’s 92.75. Luna’s scores declined as effort increased. Claude Opus 4.8 gained eight points from Medium to High, then lost 9.5 points from High to XHigh. The useful lesson is not that one setting is always preferable; it is that each candidate needs evaluation against your own requirements.
Persona changes the picture
Across the evaluated personas, the Threat Hunter median score was 43, Network Forensics and Host/EDR each scored 35, and Detection Engineer scored 31. The largest typical gap within a condition and round was five points between Threat Hunter and Detection Engineer. A single aggregate score can therefore hide uneven performance across the roles that contribute to an investigation.
Why must consistency and usable answers count?
A strong score is less useful if performance swings widely or the system often fails to deliver a valid report. Talos’s panel rule made this practical: a condition needed valid reports from all four personas for a panel to count. In other words, failures could affect whether the condition produced a complete panel at all.
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Bianco also writes, “Consistency should be a major decision factor.” For operational selection, look beyond a condition’s median to its downside: how far can a weak run or weak persona fall below the typical result?
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you select a model for your own SOC?
Use a constrained selection process rather than choosing the top score outright. Talos used a Pareto frontier across score, cost, time, and downside consistency: a candidate belongs on the frontier if another candidate does not beat it across those measures without a trade-off. Usable-answer rate should be tracked alongside those axes because refusals and invalid output can make a nominally strong option impractical.
- Set operational thresholds. Define the minimum acceptable investigative score, maximum downside spread, maximum per-task cost, maximum wait, and minimum usable-answer rate.
- Evaluate realistic candidates. Test the model and reasoning settings you can actually deploy, using the prompts, tools, and analyst roles intended for production.
- Run repeated cases. Use representative investigations and repeated runs; a single result cannot establish how consistent a candidate is.
- Record the full outcome. For each run, capture quality, cost, elapsed time, variation across roles or runs, and whether the answer was usable. Include refusals and invalid-format results in the usable-answer calculation.
- Remove candidates that miss a threshold. A high score does not compensate for exceeding a hard latency, cost, reliability, or quality limit.
- Choose among the survivors according to workflow priorities. If several candidates meet every threshold, decide which trade-off matters most for the task, such as lower cost for routine triage or stronger quality where the added delay is acceptable.
- Revisit the decision when conditions change. Reassess when prompts, workflows, model behavior, or costs change.
How far should you generalize from this benchmark?
Talos’s evaluation is useful as a selection method and an illustration of trade-offs, not proof of which model is best for every SOC. It tested one synthetic scenario over five planned rounds per condition, and complete-panel requirements meant some conditions had fewer complete observations. Your own evidence should come from representative cases and the production setup you intend to use.
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