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Yes, in a limited, test-specific sense: Cantina reports that its open-weights model apex-flash-1 solved 40 of 60 security tasks drawn from 20 held-out vulnerability cases. That is a promising result for a model designed to investigate vulnerabilities, but it is not an independent demonstration of broad security-research ability. Cantina ran each model once on its own evaluation, and the result has not been independently reproduced in the sources reviewed here.
What does apex-flash-1’s 40-of-60 score mean?
The 40 solved tasks equal a 66.7% pass@1 score: Cantina’s reported share of tasks solved on the first evaluated run. The 60 tasks were built from 20 held-out vulnerability cases, with three task views for each case. A held-out set means those cases were not in the training set described in Cantina’s release; it does not mean the benchmark was independently designed or verified.
Cantina says isolated targets were used and verifiers checked the final target state. That makes the evaluation about whether an agent could produce a verified effect against a running target, rather than simply answer questions about vulnerabilities. However, the company says each model ran the full set once, so the score does not show how much results vary across repeated runs.
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In the same Cantina-reported evaluation, apex-flash-1 scored between GLM-5.3-Flash and Claude Opus 5 High. The cost column is Cantina’s estimate for running the complete 60-task set, calculated using provider pricing—not a stable price per finding or a general estimate for security work.
#1 Best Overall
| 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 comparable only within this particular test: the set, success definition, one-run protocol, and model roles used there. They should not be treated as a universal ranking of the models across other codebases, vulnerability types, or agent setups. Cantina presents the evaluation and cost figures in its model card and release.
What kinds of tasks did Cantina test?
Each held-out vulnerability case was represented through three views. The views vary how much source information and direction the agent receives:
- Guided whitebox: source code plus detailed guidance.
- Focused whitebox: source code with limited direction.
- Focused blackbox: limited direction and access to a running target, without source code.
The mix is useful for testing more than code-reading alone: some tasks provide source, while the blackbox view requires investigation through the running target. The benchmark still covers only the cases and task designs Cantina selected; it does not establish performance across security research as a whole.
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Cantina Security developed apex-flash-1 with Yeta Labs as an open-weights model post-trained from GLM-5.3-Flash for focused security investigations. Cantina describes the intended workflow as reading code, using tools, pursuing an exploit, and checking its effect against a running target.
Cantina positions the checkpoint as a worker directed by a larger agent, not a stand-alone orchestrator. Its model card recommends the Codex agent harness, which Cantina says it used during training. The checkpoint is listed as MIT-licensed, with 321B parameters and BF16/F32 tensors. The model card documents serving through Transformers, vLLM, SGLang, and Docker Model Runner; it does not establish a particular required hardware product. See the model card for checkpoint and serving details.
What did its training emphasize?
Cantina says its initial training run created 150 tasks from 50 vulnerability cases, each represented in the same three broad views: guided whitebox, focused whitebox, or focused blackbox. The company reports using GRPO reinforcement learning, rank-256 LoRA across experts and routers, and selective full-parameter updates to 16 experts.
The disclosed case mix was weighted heavily toward authorization and identity problems. These percentages describe the 50 training cases, not the 20 held-out evaluation cases:
| Training-case category | 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% |
| SSRF | 2% |
This distribution helps explain the model’s focus, but it also limits what can be inferred: the released breakdown does not show that the held-out test had the same category proportions. Cantina’s release describes its training approach and case mix.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does the result establish—and what remains unknown?
The result supports a narrow conclusion: on Cantina’s 60-task, held-out evaluation, apex-flash-1 solved 40 tasks in the reported first draw, below Claude Opus 5 High’s 43 and above GLM-5.3-Flash’s 36. Running targets and final-state verifiers make the reported task outcome concrete, but the evaluation remains company-designed and company-reported. The sources cited here do not provide an independent reproduction of this exact test.
- It does show: Cantina reports successful first-run task outcomes on a set of 20 held-out vulnerability cases, represented in three access and guidance views.
- It does not show: repeatability across multiple runs, performance across unrelated codebases or vulnerability distributions, or how results change with different operators and agent harnesses.
- It does not establish: image or video security-research performance. The model card describes an image-text-to-text architecture, but Cantina’s reported evaluation is text-based and those modalities were not evaluated.
Cantina says it plans to publish additional held-out and public benchmark results as they are validated. Until such results are available, the 40/60 figure is best read as an encouraging result for this specific test—not proof that open models generally conduct security research reliably.
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