Reactions from ranked influencers
5 posts
Based on internal evals: ▪️ Kimi K3 is top-tier at cybersecurity There is chatter on X that Moonshot benchmark-overfit. These are stealth evals. Model has raw IQ. ▪️ Sol is a leap ahead in cyber capability At a significantly higher cost, but quite remarkable still. ▪️ Fable refuses everything We couldn’t get it to complete the run at all. What’s interesting is that Sol in comparison was much more open to helping with defensive cyber hardening TL;DR: frontier, open-weight cybersecurity capability is here. Try it on http://deepsec.sh for defensive purposes.
We ran Kimi K3 on a private cybersecurity benchmark. TL;DR: Kimi K3 is the workhorse for cyber security tasks at great recall/precision/price. GPT 5.6 is best recall/precision but at 7x higher cost per run. For context, http://Deepsec.sh is an open-source cyber harness designed for finding vulnerabilities in large codebases. The eval runs deepsec on an undisclosed open-core application at a git sha before a large number of security issues were fixed. This is a secret eval that cannot be directly benchmark-maxxed. S-Tier: GPT 5.6 Sol: By far the most thorough analysis, but coming in at over 7x the price of the runner up. Best price/recall: Kimi K3. Next tier of recall at a good price Best price at good recall: GLM 5.2 (40% lower price than Kimi K3) GPT 5.5: Only recommended with subscription or high-discount API price. Similar recall to Kimi at much higher list price. Opus 4.8: Only recommended with subscription or high-discount API price. Similar recall to GLM 5.2 at much higher list price. Fable 5: 100% refusal rate. Cannot be used for security analysis. Sol on a large code base will quickly get into 6-figure pricing. This is still affordable relative to the risk of letting security issues unfixed or paying bug bounties. I'd recommend using Sol for a one-time baseline and then using Kimi K3 for continuous analysis. When using open-weight models, make sure to use an inference vendor that supports zero data retention.
Kimi is very decent on cybersecurity. GPT 5.5 level One more benchmark where it shows real strength.
We ran Kimi K3 on a private cybersecurity benchmark. TL;DR: Kimi K3 is the workhorse for cyber security tasks at great recall/precision/price. GPT 5.6 is best recall/precision but at 7x higher cost per run. For context, http://Deepsec.sh is an open-source cyber harness designed for finding vulnerabilities in large codebases. The eval runs deepsec on an undisclosed open-core application at a git sha before a large number of security issues were fixed. This is a secret eval that cannot be directly benchmark-maxxed. S-Tier: GPT 5.6 Sol: By far the most thorough analysis, but coming in at over 7x the price of the runner up. Best price/recall: Kimi K3. Next tier of recall at a good price Best price at good recall: GLM 5.2 (40% lower price than Kimi K3) GPT 5.5: Only recommended with subscription or high-discount API price. Similar recall to Kimi at much higher list price. Opus 4.8: Only recommended with subscription or high-discount API price. Similar recall to GLM 5.2 at much higher list price. Fable 5: 100% refusal rate. Cannot be used for security analysis. Sol on a large code base will quickly get into 6-figure pricing. This is still affordable relative to the risk of letting security issues unfixed or paying bug bounties. I'd recommend using Sol for a one-time baseline and then using Kimi K3 for continuous analysis. When using open-weight models, make sure to use an inference vendor that supports zero data retention.
We ran Kimi K3 on a private cybersecurity benchmark. TL;DR: Kimi K3 is the workhorse for cyber security tasks at great recall/precision/price. GPT 5.6 is best recall/precision but at 7x higher cost per run. For context, http://Deepsec.sh is an open-source cyber harness designed for finding vulnerabilities in large codebases. The eval runs deepsec on an undisclosed open-core application at a git sha before a large number of security issues were fixed. This is a secret eval that cannot be directly benchmark-maxxed. S-Tier: GPT 5.6 Sol: By far the most thorough analysis, but coming in at over 7x the price of the runner up. Best price/recall: Kimi K3. Next tier of recall at a good price Best price at good recall: GLM 5.2 (40% lower price than Kimi K3) GPT 5.5: Only recommended with subscription or high-discount API price. Similar recall to Kimi at much higher list price. Opus 4.8: Only recommended with subscription or high-discount API price. Similar recall to GLM 5.2 at much higher list price. Fable 5: 100% refusal rate. Cannot be used for security analysis. Sol on a large code base will quickly get into 6-figure pricing. This is still affordable relative to the risk of letting security issues unfixed or paying bug bounties. I'd recommend using Sol for a one-time baseline and then using Kimi K3 for continuous analysis. When using open-weight models, make sure to use an inference vendor that supports zero data retention.
chinese open source models are becoming the gold standard for security. no one solved for the equilibrium. relatedly, the social scientists at the labs suck. their work and indexes suck. embarrassing.
We ran Kimi K3 on a private cybersecurity benchmark. TL;DR: Kimi K3 is the workhorse for cyber security tasks at great recall/precision/price. GPT 5.6 is best recall/precision but at 7x higher cost per run. For context, http://Deepsec.sh is an open-source cyber harness designed for finding vulnerabilities in large codebases. The eval runs deepsec on an undisclosed open-core application at a git sha before a large number of security issues were fixed. This is a secret eval that cannot be directly benchmark-maxxed. S-Tier: GPT 5.6 Sol: By far the most thorough analysis, but coming in at over 7x the price of the runner up. Best price/recall: Kimi K3. Next tier of recall at a good price Best price at good recall: GLM 5.2 (40% lower price than Kimi K3) GPT 5.5: Only recommended with subscription or high-discount API price. Similar recall to Kimi at much higher list price. Opus 4.8: Only recommended with subscription or high-discount API price. Similar recall to GLM 5.2 at much higher list price. Fable 5: 100% refusal rate. Cannot be used for security analysis. Sol on a large code base will quickly get into 6-figure pricing. This is still affordable relative to the risk of letting security issues unfixed or paying bug bounties. I'd recommend using Sol for a one-time baseline and then using Kimi K3 for continuous analysis. When using open-weight models, make sure to use an inference vendor that supports zero data retention.
Interesting analysis
Based on internal evals: ▪️ Kimi K3 is top-tier at cybersecurity There is chatter on X that Moonshot benchmark-overfit. These are stealth evals. Model has raw IQ. ▪️ Sol is a leap ahead in cyber capability At a significantly higher cost, but quite remarkable still. ▪️ Fable refuses everything We couldn’t get it to complete the run at all. What’s interesting is that Sol in comparison was much more open to helping with defensive cyber hardening TL;DR: frontier, open-weight cybersecurity capability is here. Try it on http://deepsec.sh for defensive purposes.