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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →In a reported comparison on 3,880 public-suite records, plain Gemma 4 26B scored 75.3% overall, versus 77.3% for Jev 1.13.0—a reported 2.1-percentage-point Jev lead. Their pooled yes/no accuracy was effectively tied, while Jev led by 4.5 points on multiple-choice questions. The results are useful for evaluating this particular L4 setup, not as a guarantee for other prompts, hardware, or workloads.
What the comparison found
The benchmark covered 3,880 human-labelled records across 13 subsets, grouped into yes/no, multiple-choice, and five-level rating tasks. The figures below are the comparison reported in the benchmark article published September 24, 2026. Jev’s results came from Bespoke Labs’ published run; the author compared those results with Gemma outputs rather than making new Jev API calls.
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| Question type | Records | Jev 1.13.0 | Plain Gemma 4 26B | Reported comparison |
|---|---|---|---|---|
| All | 3,880 | 77.3% | 75.3% | Jev ahead by 2.1 points; reported 95% range: 0.2–4.0 points |
| Yes/no | 1,399 | 84.6% | 84.8% | Effectively level; reported difference range spans 2.8 points ahead to 2.5 behind |
| Multiple choice | 1,848 | 82.8% | 78.3% | Jev ahead by 4.5 points; reported range: 2.0–7.1 points |
| Five-level rating | 633 | 45.2% | 45.5% | Nearly the same exact-level accuracy |
The uncertainty ranges need qualification: Jev per-record answers were not published, so the article compared independent proportions rather than paired outputs. Pairing could narrow the ranges; correlations among records drawn from the same passages or articles could widen them.
How to read the task breakdown
The aggregate score hides a meaningful difference by answer format. Yes/no accuracy was nearly identical, but the multiple-choice result favored Jev. On five-level ratings, exact-level accuracy was also close; that metric does not establish that the systems interpret or express rating confidence equally well.
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How the test was set up
The author describes a pre-registered evaluation that read probabilities for allowed answer labels from a plain Gemma 4 26B inference, then compared accuracy and calibration with DiffusionGemma and published Jev results. Both 26B model checkpoints were community 4-bit AWQ builds. The Gemma model arms used matched flags, prompts, label tokens, and scoring code. The tests ran on an NVIDIA L4 with 24 GB of memory.
The suite combined yes/no tasks from BoolQ, PAWS, SQuAD 2.0, Civil Comments, and Aegis 2.0; multiple-choice tasks from MultiNLI, PubMedQA, VitaminC, and English and German MASSIVE intents; and five-level ratings from HelpSteer2 and SummEval. The article says its rebuilt subset checksums matched the published suite. It used the Jev request parser to create a shared prompt format for Gemma and Bespoke Labs’ scoring definitions.
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These controls make the reported model-arm comparison more interpretable, but the setup remains a one-GPU, one-run-per-arm case study. The public datasets predate Gemma 4 and may overlap with its training data. Neither the benchmark design nor the scores establish performance on other prompts, quantizations, hardware, or production traffic.
Calibration: Gemma improved with labeled examples
Expected calibration error (ECE) measures the gap between a model’s confidence and its observed accuracy across confidence groups; lower values generally indicate better alignment. Across the 13 subsets, the reported median as-shipped ECE was 0.071 for Jev and 0.180 for plain Gemma. After fitting one temperature using 50 labels from each subset, Gemma’s reported median ECE fell to 0.080.
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That brings Gemma’s median close to Jev’s as-shipped result, but it does not show that Gemma was better calibrated on every task: after fitting, Gemma still had higher ECE on 8 of 13 subsets. The article also notes that Jev might improve if calibrated on its own outputs. The reported figures therefore compare shipped Jev with both unadjusted and fitted Gemma, not equally calibrated versions of both systems.
Latency and cost on the tested setup
For the tested prompts, the article reports 61 ms per plain Gemma decision on the instance and estimates a maximum cost of $5.43 per million decisions at full utilization, using the stated g6.xlarge hourly rate. It compares that estimate with $5.54 per million for Jev at the study’s median 132 input tokens. These are study-specific estimates, not universal prices: a rented GPU still incurs charges while idle, and longer prompts raise costs. The article’s summary identifies one L4 in us-east-1, three instances across runs, and one run per arm.
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For a deployment decision, compare the hosted API and self-hosted GPU under your own prompt lengths, concurrency, utilization, and answer mix. The study does not establish how either option performs economically under a different traffic pattern.
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What the results do—and do not—support
- If your workload is mostly yes/no: the pooled scores offer no meaningful accuracy advantage to either system in this suite; local task performance and calibration may matter more.
- If your workload is multiple choice: Jev had the clearer advantage in this comparison, though the reported uncertainty range and dataset mix should inform how much weight you give it.
- If confidence quality matters: Gemma’s ECE improved substantially after fitting on labeled examples, but the benchmark did not establish a universally superior calibration setup.
- If you are estimating serving cost: treat the cited per-million figures as full-utilization calculations for the tested case, not as a bill prediction for an idle or differently loaded instance.
The benchmark and its code, pre-registration, and per-item-results repository are linked in the author’s September 24, 2026 article. The comparison is a reported case study; its design and available results do not justify assuming the same ranking on a different model build, serving configuration, or evaluation set.
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