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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Yes, in a limited, measurable sense: Cantina says its open-weights model apex-flash-1 solved 40 of 60 security tasks in a company-designed evaluation, a 66.7% pass@1 result. That puts it between GLM-5.3-Flash at 36/60 and Claude Opus 5 High at 43/60 in the same test. The score is promising evidence for a focused security-research worker, not independent proof that open models can reliably find vulnerabilities across real-world software.
What does apex-flash-1’s 40-of-60 score mean?
Cantina Security reports that apex-flash-1 solved 40 of 60 tasks on the first reported attempt, or 66.7% pass@1. The 60 tasks were drawn from 20 held-out vulnerability cases; each case was represented in three task forms. The result is therefore a count of successful tasks in this particular evaluation, not a finding that the model can discover two-thirds of vulnerabilities in arbitrary software.
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The comparison figures are from the same Cantina evaluation. The company says each model ran the full set once and reports adjudicated first-draw results.
| Model | Tasks solved | Pass@1 | Estimated cost for the 60-task run |
|---|---|---|---|
| apex-flash-1 | 40/60 | 66.7% | $2.38 |
| GLM-5.3-Flash | 36/60 | 60.0% | $4.56 |
| Claude Opus 5 High | 43/60 | 71.7% | $74.68 |
These are Cantina’s figures, not an independently reproduced ranking. The cost column is the company’s estimate using provider pricing for this full 60-task run; it should not be treated as a stable per-finding price or a general operating-cost comparison. [Cantina model card; Cantina release]
#1 Best Overall
How was the security evaluation designed?
Cantina says the 20 held-out cases were turned into 60 tasks across three views, with targets running in isolated environments and verifiers checking the final target state. The different views test how a model works with varying amounts of information and access:
- Guided whitebox: source code plus detailed guidance.
- Focused whitebox: source code with limited direction.
- Focused blackbox: limited direction and access to a running target, but no source code.
This design asks models to do more than answer a security-knowledge question: in Cantina’s framing, the work involves reading code, using tools, pursuing an exploit and verifying its effect against a running target. But one run per model leaves results sensitive to the particular cases and attempt. The evaluation does not establish performance across unrelated codebases, vulnerability mixes, operators or agent setups. [Cantina model card; Cantina release]
Rank #2
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What is apex-flash-1, and how is it meant to be used?
Cantina released apex-flash-1 with Yeta Labs as an open-weights model post-trained from GLM-5.3-Flash for focused security investigations. The model card lists a 321-billion-parameter checkpoint, MIT licensing and BF16/F32 tensors. It documents serving routes through Transformers, vLLM, SGLang and Docker Model Runner. These details describe the checkpoint; the reported benchmark does not establish a particular hardware requirement or show that every serving setup performs identically. [Cantina model card; Cantina release]
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsCantina positions the model as a specialist worker directed by a larger agent, rather than a stand-alone orchestrator. The company says it trained with the Codex agent harness and recommends that harness for this checkpoint. That intended role matters when interpreting the score: it is a result for the model in an agent-driven workflow, not evidence that the model independently plans and manages an entire security engagement. [Cantina model card; Cantina release]
Rank #3
What kinds of vulnerabilities shaped its training?
Cantina says its initial training run created 150 tasks from 50 vulnerability cases. It reports using GRPO reinforcement learning, rank-256 LoRA across experts and routers, and selective full-parameter updates to 16 experts. Those are company-disclosed training details, not a guarantee that the model generalizes equally to every security domain. [Cantina release]
The disclosed distribution of those 50 training cases was heavily weighted toward authorization, identity and scope binding:
Rank #4
| Training-case area | Share of the 50 cases |
|---|---|
| Authorization, identity and scope binding | 72% |
| Accounting and numerical precision | 18% |
| Time validation and signature replay | 4% |
| Business rules and payment validation | 4% |
| Server-side request forgery (SSRF) | 2% |
These percentages describe the training cases, not the separate 20 held-out evaluation cases. They suggest the training emphasis was narrow: authorization and identity issues dominated, while several other vulnerability classes had much smaller shares. [Cantina release]
What the result establishes—and what it does not
The strongest defensible conclusion is specific: Cantina reports that apex-flash-1 solved 40 tasks in its held-out 60-task evaluation, finishing behind Claude Opus 5 High and ahead of GLM-5.3-Flash in that comparison. The running-target setup and outcome verifiers make the test more concrete than a quiz, but it remains company-designed and company-reported, with one run per model. The reviewed sources do not provide an independent reproduction of this exact test. [Cantina model card; Cantina release]
Best Value
It does not prove that open models generally perform security research reliably, that this model will find a comparable share of bugs in ordinary production code, or that its relative ranking will hold on another benchmark. Cantina says it plans to publish further held-out and public benchmark results as they are validated; those results are not part of the 40/60 finding. [Cantina release]
Which modalities were evaluated?
The model card describes an image-text-to-text architecture, but Cantina’s reported evaluation is text-based. Its performance on image and video inputs has not been evaluated, so the architecture label should not be read as evidence of tested visual security-research capability. [Cantina model card]
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