An honest head-to-head against the models you're actually choosing between. We name the competitors. We acknowledge what they're good at. And we show the security tasks where our lineup has the structural advantage — plus the ones where it doesn't.
Two paragraphs before we start naming competitors. Calibrate expectations first.
We do not compete with frontier general models on poetry, math, or general writing. The Safeguard lineup is trained on a security-only corpus and the output is a structured reasoning trace, not a free-form response. Asking Griffin to summarise a book is asking the wrong question — use a frontier model for that, and use Griffin for the things below.
Public benchmarks like HumanEval, MMLU, and GSM8K do not measure what production security work needs. We publish numbers against our own held-out CVE sets, taint-graph suites, and adversarial prompt-injection corpora. The methodology lives on the benchmarks page, with the full set definitions and per-row caveats.
See the methodologyGPT-4o / GPT-5, Claude Sonnet 4 / Opus 4, Gemini 2.5 Pro, Llama 4. These are the models a security team might reach for first. On security-specific tasks, the Safeguard lineup wins most rows. On general capability, they win. Both are true.
| Capability | GPT-4 class | Claude 3.7/4 class | Gemini 2.5 class | Llama 4 / open | Safeguard Griffin |
|---|---|---|---|---|---|
| GPT-4o, GPT-5 | Sonnet 4, Opus 4 | Gemini 2.5 Pro | Llama 4 + open class | Lite → Zero | |
| Security-only training corpus | general web | general web | general web | general web | CVE / patch / advisory |
| Security-augmented tokeniser | no | no | no | no | ~28k extra tokens |
| Structured reasoning trace as first-class output | free-form CoT | thinking blocks | no | no | HYPOTHESIS / PATH / DISPROOF / PATCH |
| Adversarial disproof pass | no | no | no | no | yes |
| Cross-package taint reasoning (≤12 hops) | degrades past 3-4 hops | degrades past 4-5 hops | degrades past 3 hops | no | Griffin M/L/Zero |
| Auto-fix patch pass-rate (project tests green) | ~27% | ~38% | ~24% | ~19% | 54-84% |
| CWE / CVE classification accuracy | ~0.49 | ~0.55 | ~0.47 | ~0.41 | 0.74-0.91 |
| Inline sub-100ms p95 (on-device) | cloud only | cloud only | cloud only | local but »100ms | Lino 1B |
| Air-gapped / sovereign deployment | no | no | no | open weights | full lineup |
| Customer code not in training (guaranteed) | API opt-out | API opt-out | tier-dependent | no central training | contractual |
General-purpose code writing in unfamiliar languages, longer free-form conversations, multimodal vision over screenshots and diagrams, agentic tool-use across heterogeneous APIs, and frontier reasoning on math and logic puzzles. Griffin will not write you a React component or summarise a board deck. We lose on those by design.
Long-document summarisation and rewriting, careful natural-language reasoning, nuanced refusal in ambiguous contexts, and instruction-following across hundreds of pages of mixed content. Claude's thinking blocks are excellent for free-form analysis. They are not, however, a structured trace contract you can audit row-by-row in a SOC review.
Gemini's multimodal grounding (image, video, audio) is a real capability we have no answer to. Llama 4 ships with open weights you can fine-tune from scratch for any domain — if your team wants to own the model end-to-end, that is the right choice. We do not compete on either axis; we compete on the security-task numbers above.
Models that are trained primarily on code. They are excellent at completion. They are not trained on the security-specific tasks below — because that is not what they were designed for.
| Capability | GitHub Copilot models | Codestral (Mistral) | DeepSeek Coder V2 | StarCoder2 | Safeguard Griffin |
|---|---|---|---|---|---|
| Generic code completion Griffin is not a completion model | yes | yes | yes | yes | no |
| Security-tuned reasoning | no | no | partial | no | yes |
| Reachability-aware patch | no | no | no | no | yes |
| Taint-path identification | no | no | no | no | yes |
| Sanitiser-quality scoring | no | no | no | no | yes |
| License-aware suggestions | partial | no | no | partial | yes |
| Auto-fix with cited reasoning trace | partial | no | no | no | yes |
| Adversarial robustness on security prompts | partial | partial | partial | no | yes |
Copilot's in-editor completion, Codestral's coverage across niche languages, DeepSeek Coder V2's strong open-weight code reasoning, and StarCoder2's permissive licensing for fine-tuning — all of these win on generic code completion and refactoring. If your problem is "write me a function" or "explain this regex," they are the right tool. Safeguard wins when the question becomes "is this function reachable from a tainted source through a missing sanitiser, and what's the minimal patch that closes it without breaking the test suite." That is not the same question. We are not trying to be a code-completion model.
Agents that drive an editor, shell, and build system end-to-end. Excellent at developer productivity. Safeguard Code sits next to them and adds the supply-chain dimension they do not have.
| Capability | Claude Code | Cursor's agent | Cline / Continue / Aider | Safeguard Code |
|---|---|---|---|---|
| Drives the editor / shell / build | yes | yes | yes | yes |
| Reads your project's SBOM | no | no | no | yes |
| Reads your project's policy gates | no | no | no | yes |
| Reachability-aware suggestion | no | no | no | yes |
| Runs offline by default | no | no | partial | yes |
| Auto-fix with structured trace | partial | no | no | yes |
| Sanitiser-quality awareness | no | no | no | yes |
| Audit log per session with cryptographic chain-of-custody | no | no | no | yes |
| Air-gapped operation supported | no | no | partial | yes |
Claude Code, Cursor, Cline, Continue, and Aider are excellent at generic developer productivity: scaffolding apps, refactoring across files, running tests, fixing build errors, and gluing services together. If your workflow is "build the feature," they are the right runner. Safeguard Code wins when the workflow is supply-chain-aware — SBOM-conscious, reachability-aware, policy-gated, and audit-logged with cryptographic chain-of-custody. Run them side by side: a productivity agent for the build, Safeguard Code for the security pass.
The vendors who already do security and have bolted AI on. Each one is good at something specific. The structural difference is a custom-trained lineup vs a single AI feature inside a scanner.
| Capability | Snyk DeepCode AI | GitHub Advanced Security | Checkmarx AI | Veracode Fix | Safeguard lineup |
|---|---|---|---|---|---|
| DeepCode Fix | Copilot Autofix | AI patches | Veracode Fix | Lino → Zero | |
| AI-generated patches | DeepCode Fix | Copilot Autofix | Checkmarx AI | Veracode Fix | Griffin auto-fix |
| Reachability-aware reasoning | JVM/Node | CodeQL queries | limited | limited | 11-scanner fusion |
| Structured reasoning trace | no | patch diff only | no | no | yes |
| Cross-package taint chain reasoning | partial | CodeQL only | partial | partial | Griffin ≤12 hops |
| Adversarial disproof pass | no | no | no | no | yes |
| Patch-pass-test rate | ~45% | ~52% | undisclosed | ~48% | 54-84% |
| Sovereign / air-gapped with full model lineup | cloud only | GHES, no Autofix offline | on-prem, no AI offline | no | yes |
| Custom-trained models per ecosystem | no | no | no | no | Griffin variants |
Excellent developer ergonomics, polished IDE plugins, and a strong free-tier acquisition story. DeepCode Fix produces clean patch diffs and the JVM / Node coverage is mature. Wins on time-to-first-fix for small teams. Loses on structured reasoning trace, air-gapped deployment with full model lineup, and cross-package taint chains beyond a few hops.
Native to the GitHub workflow, zero-friction enablement, CodeQL is genuinely strong for the queries it supports, and Autofix patches land directly in PRs. Wins if your code already lives on GitHub.com. Loses on sovereign / air-gapped deployment with the AI features intact, ecosystem breadth beyond CodeQL coverage, and a structured reasoning trace you can audit.
Long-standing SAST market presence, mature on-prem story for regulated industries, and the policy-management UI is built for enterprise security teams. Wins on enterprise sales motion and compliance-driven procurement. Loses on patch-pass-test rate transparency, structured reasoning trace, and adversarial disproof pass.
Strong binary analysis heritage, mature SAST/DAST/SCA suite, and Veracode Fix produces sensible patches across the languages it covers. Wins on AppSec maturity for regulated workloads. Loses on reachability-aware reasoning, structured trace, and sovereign deployment with the full AI lineup.
Three tiers, each with a precision / latency tradeoff. Read across to see who else lives in the same band.
Lino (1B, INT8, runs locally in the IDE / pre-commit hook)
Nothing else at this latency on-device for security. Copilot inline completions exist but are not security-tuned and require a network round trip. Llama 3.2 1B can run locally but has not been trained on the security corpus.
Eagle (13B, ranking head) + Griffin Lite (8B, fast remediation)
Copilot-class code models, Codestral, DeepSeek Coder V2. These are excellent at generic completion. They do not rank by reachability and they do not emit a structured trace.
Griffin M (32B), Griffin L (70B), Griffin Zero (671B-MoE, 256k context)
GPT-4o / GPT-5, Claude Sonnet 4 / Opus 4, Gemini 2.5 Pro. These are frontier general models. They win on general code writing; they lose on the security-task numbers above because the corpus and the trace contract were not optimised for it.
Six capabilities we explicitly do not compete on. If your problem is on this list, please reach for a frontier general model. We will not pretend otherwise.
We do not optimise for casual chat or open-ended dialogue. Use a frontier model.
Griffin has no image decoder. Use a diffusion model.
Math benchmarks are not in our training mix. Use a frontier general model with chain-of-thought.
Our corpus is CVE write-ups, patches, and disclosure threads. The model will not write you a sonnet.
No audio decoder. Pair Griffin with a separate TTS model if you need to read findings aloud.
Beyond code identifiers, we do not train translation pairs. Use a frontier multilingual model.
Comparisons on this page are based on a combination of public model cards, vendor documentation, internal hands-on evaluation against our held-out security suites, and published benchmarks where available. The columns for GPT-4o / GPT-5, Claude Sonnet 4 / Opus 4, Gemini 2.5 Pro, and Llama 4 reflect their respective vendor releases as of the date below. For the Safeguard column, the underlying numbers live on the benchmarks page with full per-row methodology and held-out set definitions. Where a vendor declines to publish a number (e.g. patch-pass-test rate), we mark it "partial" with the best public estimate and the qualifying note. This page is updated as new models ship; if a competitor has shipped something you think we're mischaracterising, send us the citation and we will update the row. Last updated: May 2026.