Claude vs Codex vs OpenCode for source-code security review: choosing a runner and model

Key takeaways

• No single AI model wins across every repository requirement: frontier hosted models lead on deep multi-file reasoning, while OpenCode unlocks alternative cloud providers and air-gapped local inference.
• The review pipeline around the model matters as much as the raw model weights; unstructured prompts fail even on top-tier reasoning models.
• Dokima supports Claude and Codex subscriptions directly and integrates with OpenCode so teams can switch between Claude, Codex, Gemini, open-weights and private local endpoints without losing workspace state.
• Separating hunting stages from skeptical peer-review stages makes smaller or open-weights models far more reliable than running them in a single pass.

Which AI model is best for source-code security review?

Engineering leaders evaluating AI security review almost always ask the same initial question: “Should we run this with Claude, Codex, Gemini, or an open-weights model on our own hardware?”

In practice, there is no universal winner because source-code review balances three competing constraints:

1. Multi-file reasoning depth: Can the model trace untrusted input across routers, middleware, domain services and storage layers without losing the thread?
2. Data-handling and residency boundaries: Can the repository be sent to a commercial cloud API under enterprise zero-data-retention terms, or must it stay inside a private VPC or on-premises GPU server?
3. Predictable cost and capacity: Does the team want to use existing developer subscriptions, metered API billing, or fixed-cost local inference across continuous background sweeps?

Dokima decouples the security review methodology from the underlying model provider. You run the same local binary, the same 21-stage review schedule, and the same deterministic deduplication whether you configure Claude Code, OpenAI Codex CLI, or OpenCode as your runner.

How Claude, Codex and OpenCode compare in Dokima

Dokima invokes your chosen CLI runner in read-only analysis mode against your local repository workspace. Each runner path serves a distinct operational profile:

Because Dokima’s pricing is based on your team size rather than markup on tokens, model usage or hosting costs remain directly between you and your chosen provider (or your own infrastructure).

Where frontier hosted models pull ahead

When a repository has complex control flow, custom authentication schemes or subtle state transitions across dozens of files, frontier models accessed through Claude, Codex or OpenCode (such as Gemini Pro/Ultra class models) show a clear advantage in two specific areas:

1. Tracing implicit trust boundaries

Many high-severity vulnerabilities do not look like bugs inside the function where the damage occurs. A service method might update a billing record or export an archive assuming the caller already verified organisation membership. Frontier models are noticeably better at holding the architectural map in working memory while checking whether every HTTP, webhook or CLI entrypoint actually enforces that precondition.

2. Skeptical peer review and self-correction

In Dokima’s pipeline, candidate vulnerabilities are handed to a dedicated Candidate Finding Peer Reviewer stage that tries to disprove the finding before it is ever reported. Top-tier reasoning models excel at spotting when an initial hunter stage misread a framework guard, a database constraint or a type invariant.

Where OpenCode, Gemini and open-weights models shine

Relying exclusively on a single hosted provider is not always possible or desirable. OpenCode gives engineering teams three major capabilities inside Dokima:

• Alternative frontier ecosystems: Teams with corporate commitments to Google Cloud / Gemini or AWS Bedrock can point OpenCode at those endpoints without waiting for a separate vendor-specific security scanner.
• Private local inference: Defence, financial, healthcare and embedded-systems teams often prohibit source code from leaving their network. Running OpenCode against a local vLLM, SGLang or llama.cpp server keeps every prompt and file snippet on hardware you control. (See our guide on running AI code security review with local models.)
• High-frequency continuous sweeps: When running continuous background sweeps on active repositories, local or open-weights models allow teams to run exhaustive passes around the clock without incremental per-token charges.

Why the orchestration harness matters as much as the model

If you hand a 100,000-line repository to an open-weights model (or even a frontier model) in a single unstructured prompt, quality degrades rapidly. The model either hallucinates bugs to satisfy the prompt or drowns the user in surface-level warnings.

Dokima narrows the gap between models by structuring how work is parcelled out and verified:

1. Bounded component scope: The Component Decomposer breaks the repository into logical units first, so hunting stages inspect coherent subtrees rather than drowning in unrelated files.
2. Single-purpose hunting lenses: Instead of asking a model to check for every CWE at once, Dokima runs focused passes for injection, authorisation, trust boundaries, missing controls, secret exposure and resource abuse.
3. On-demand context gathering: When a peer-review stage needs to see how a helper is called, Dokima dispatches an Information Gatherer stage to fetch the exact files rather than forcing the model to guess.
4. Deterministic deduplication: Regardless of how a model phrases its prose, Dokima computes a canonical fp1-... fingerprint anchored to the weakness class and code location so switching runners or models does not flood your report with duplicates.

A structured pipeline turns a strong model into a disciplined security reviewer, and makes local or open-weights models practical on real production repositories.

Switching runners without losing history

Because all state lives in your workspace’s .dokima/ directory, you can test different runners on the same repository or change runners in dokima Settings (s) at any time. Previous findings, dispositions and architectural baselines remain intact.

Read our Getting started guide to see how Claude, Codex and OpenCode are configured, or start a 21-day free trial to benchmark your preferred model on your own codebase.