When more than one lab tests the same batch, we line up their results side by side. When they match, it is the strongest signal you can get. When they do not, that is information too. Here is what these labs found — you decide.
3 independent labs tested this batch of CJC-1295 (no-DAC) from Paramount Peptides. Their purity results ranged from 98% to 99.8%.
The labs agree
Independent labs landed within 2 points of each other on purity. That is the strongest signal you can get on a batch.
On the actual amount per vial, the labs were nearly identical (11.6 to 12.7 mg).
Scores land close·Poor · 5.4
The two badges above are our blended RV-score view: whether the scores agree, and the overall quality tier. The plain read at the top leads with the labs actual purity numbers, and the full per-lab table is below.
CJC-1295 (no-DAC) from Paramount Peptides batch NDAC10N110624 · cross-tested by 3 independent labs
The blended RV scores land within 0.5 to 1.5 points of each other. The RV score mixes purity with other factors and can read as agreement even when raw purity does not — so weigh the raw purity range shown above.
✓ Batch identity basis: Same physical sample (Finnrick multi-lab program)
All records carry Finnrick (FNR-*) task IDs, meaning one physical vial was routed by Finnrick to multiple labs and each lab tested it independently. This is the strongest basis for 'same batch' — there's no batch heterogeneity between labs because there's no batch difference: it's the same vial.
🧞 Identity caveat — Distinct molecule from CJC-1295 DAC (DAC adds Drug Affinity Complex → days-long half-life). Bare 'CJC-1295' resolves here per peptide-research market convention: ≥99% of bare-term COAs are no-DAC (Mod GRF 1-29). Vendors advertising DAC form state so explicitly. If a record's DAC/no-DAC matters for downstream analysis, verify against the COA notes — do not blindly trust this resolution.
Same method, results still diverge — investigate. All labs reported HPLC but results diverge — batch heterogeneity, calibration drift, or sample handling worth investigating. When labs share the analytical approach but the numbers don't line up, the divergence is doing real work and deserves a closer look.
Why this matters: A single COA is one lab's answer from one method on one sample. Multiple labs reveal the pattern. When labs converge on the same answer, that's strong cross-validation. When they diverge — especially on content while agreeing on purity — the difference is often method-driven (different quantitation basis) but sometimes signals real product variation. ResearchVerify is the only platform that surfaces both cases automatically across thousands of cross-tests.