Jev vs basal-omni-1.0-ultra: published benchmark comparison

Data: JevBench v1.6.39, published 2026-10-22

Rank #3 on the open-weights board in v1.6.39.

Compare Jev-compatible models by separate measures and their measurement conditions. A historical measurement is not a current rank.

Jev and basal-omni-1.0-ultra: published axes and category radars

Pick any two. Radars for the score axes, capability by subject topic and use cases, chance-corrected competence per request type on the open and sealed sets, and competence per tier on each set. Further out is better on every spoke; the link keeps the pair.

System A

System B

A: basal-omni-1.0-ultra — Open weights · LLM decoder · Score 71.7 (#3)
B: Jev 1.13.0REFERENCE · TypeSafe — Jev — reference (TypeSafe, closed) · Score 71.5 (ranked, not ranked)

The four score axes

0–100, the values in the table. An axis a system has no published value for is left as a gap (it counts as 0 in the composite).

Capability by subject topic

basal-omni-1.0-ultra: No per-category values are published for this system.

Linear signed scale: −100 at the centre, 0 on the bold middle ring, 100 at the rim.

What the decision is about — math and dates, coding, rules, money, support work, everyday messages, safety. Chance-corrected competence per category (0 = at or below chance; negative averages are reported as 0, 100 = perfect), basal-omni-1.0-ultra: open+sealed; Jev 1.13.0: S+P+L1+L2+L3 items pooled; only categories with at least 30 items are spokes, smaller ones are listed below. Hover a category for its definition and item count.

What each category means · items per category

Rules, policy & law — applying written rules: company policies, contracts, regulations, eligibility. 1616 items (151 open / 1465 sealed)
Coding & software — code, SQL, repositories, developer tools and IT systems. 382 items (62 open / 320 sealed)
Math & numbers — a calculation decides the answer: arithmetic, word problems, probability, dates, units. 370 items (37 open / 333 sealed)
Finance & commerce — money: payments, refunds, invoices, orders, expenses, insurance payouts. 223 items (12 open / 211 sealed)
Support & operations — support tickets, incidents, logistics, scheduling desks and routing work to a team. 160 items (21 open / 139 sealed)
Everyday language — short everyday messages: intents, assistant requests, reading a detail out of a text. 145 items (11 open / 134 sealed)
Safety & security — untrusted or injected instructions, fraud, moderation, access and security triage. 109 items (6 open / 103 sealed)

Use cases (TypeSafe categories)

basal-omni-1.0-ultra: No per-category values are published for this system.

Linear signed scale: −100 at the centre, 0 on the bold middle ring, 100 at the rim.

The real-world application area of each decision, after the TypeSafe use-case map; an item can count in two. Chance-corrected competence per category (0 = at or below chance; negative averages are reported as 0, 100 = perfect), basal-omni-1.0-ultra: open+sealed; Jev 1.13.0: S+P+L1+L2+L3 items pooled; only categories with at least 30 items are spokes, smaller ones are listed below. Hover a category for its definition and item count.A point on the bold 0 ring printed 0.0 is a measured value, not a gap: these published cells clip below-chance results to 0, so 0 means at or below chance. Their score markers cannot fall inside that ring.

What each category means · items per category

Model routing — deciding which LLM or model tier should handle a prompt; classifying a request's intent, domain, difficulty or risk to pick a model. 524 items (86 open / 438 sealed)
Legal and compliance — contracts, policies, regulations, eligibility rules, checking documents against written requirements. 515 items (74 open / 441 sealed)
Customer support — classifying tickets and customer intents, urgency, refunds, routing cases to a team, checking support replies. 240 items (37 open / 203 sealed)
Other — none of the above: general reasoning, arithmetic or date puzzles, everyday assistant requests without a business workflow. 174 items (24 open / 150 sealed)
E-commerce marketplaces — product listings, product attributes, orders and deliveries, seller catalog policy. 119 items (9 open / 110 sealed)
Insurance claims — first-notice-of-loss reports, claim complexity, missing information, claim triage. 119 items (9 open / 110 sealed)
Risk assessment — estimating probabilities and severity of risks, incidents, vendor or underwriting risk. 118 items (8 open / 110 sealed)
Financial crime — suspicious transactions, KYC, fraud alerts, entity matching for investigations. 101 items (9 open / 92 sealed)
Feature extraction for predictive modeling — turning natural-language data into probabilistic features or estimates for a downstream prediction. 99 items (5 open / 94 sealed)
Lead generation — matching companies or inbound messages to an ideal customer profile, buyer intent, prioritising and routing sales leads. 95 items (4 open / 91 sealed)
Recruiting — resumes, applications, interview feedback, matching candidates to roles. 94 items (1 open / 93 sealed)
Graphs and knowledge graphs — entity types, relationships between records, contradictions between facts, multi-step lookups across linked facts. 94 items (3 open / 91 sealed)
LLM guardrails — checking LLM inputs, outputs and tool calls: prompt injection, jailbreaks, policy violations, tool-call errors, answer quality. 93 items (5 open / 88 sealed)
Moderation and trust and safety — user content moderation: toxicity, harassment, spam, unsafe advice, personal data exposure. 93 items (7 open / 86 sealed)
Semantic code linting — checking code or writing against conventions and guidelines, as in CI review of code. 90 items (3 open / 87 sealed)
Search and retrieval — scoring query-to-candidate relevance, reranking results, selecting useful context for RAG. 90 items (5 open / 85 sealed)
Scientific discovery — screening papers, labelling research passages or survey answers, checking that citations support claims, methodology checks. 90 items (2 open / 88 sealed)
Advertising — ad creatives, campaign copy, brand safety, prohibited claims in ads. 88 items (3 open / 85 sealed)
Gaming — player reports, in-game chat, game support. 86 items (3 open / 83 sealed)
Demand forecasting — purchase intent, product interest, demand and supply signals for forecasting. 83 items (3 open / 80 sealed)

Competence per request type, open / sealed

Linear signed scale: −100 at the centre, 0 on the bold middle ring, 100 at the rim.

Chance-corrected competence for Choice, Noul and Score on the 300 open and 1200 sealed decisions.

Competence per tier — open set

Linear signed scale: −100 at the centre, 0 on the bold middle ring, 100 at the rim.

Per-tier competence, the three request types pooled by their published decision counts.

Competence per tier — sealed set

Linear signed scale: −100 at the centre, 0 on the bold middle ring, 100 at the rim.

Per-tier competence on the sealed decisions, types pooled the same way; item text stays private.

How the categories were made

O1S chance-corrected competence: equal mean across the request types present, clipped to 0–100. Zero means the equal-weight mean across present request types was at or below baseline; negative means are clipped. It does not mean every type scored zero. Choice uses a random-option baseline; Noul uses a fixed 50% accuracy baseline (abstentions count as wrong); Score uses the midpoint-guess error (random-guess fallback when every gold is at the midpoint). Compare models within a category; row pools and supported types can differ.

Categories use ruled subject-topic labels and authoring use-case precedence across the answered pool union, with stable item identities counted once.

Official O1S; recorded failures count where supported; no missing response is imputed; stable identity dedupe parent first.
S-based category cells pool recorded supported responses from S+P and the answered L1/L2/L3 supplements. API overlay category cells keep their separately recorded A4/A5+P union. Headline and per-type/tier scores are unchanged.

All values as a table

Spoke	A: basal-omni-1.0-ultra	B: Jev 1.13.0
The four score axes
Intelligence	74.1	63.6
Calibration	91.0	90.6
Speed	87.3	91.5
Cost	50.4	54.7
Capability by subject topic
Rules, policy & law	—	49.4
Coding & software	—	64.3
Math & numbers	—	10.4
Finance & commerce	—	40.9
Support & operations	—	48.8
Everyday language	—	77.5
Safety & security	—	52.2
Use cases (TypeSafe categories)
Model routing	—	73.0
Legal & compliance	—	68.6
Customer support	—	49.6
Other	—	40.1
E-commerce	—	40.5
Insurance claims	—	46.1
Risk assessment	—	32.8
Financial crime	—	35.8
Feature extraction	—	28.3
Lead generation	—	49.2
Recruiting	—	0.0
Knowledge graphs	—	28.8
LLM guardrails	—	68.0
Moderation	—	51.6
Code linting	—	29.1
Search & retrieval	—	80.9
Science	—	21.2
Advertising	—	16.4
Gaming	—	5.1
Demand forecasting	—	0.0
Competence per request type, open / sealed
Choice · open	82.7	79.0
Choice · sealed	84.7	75.8
Noul · open	68.4	54.5
Noul · sealed	72.5	46.1
Score · open	69.1	63.3
Score · sealed	66.9	63.0
Competence per tier — open set
Easy	96.5	93.9
Standard	69.1	63.2
Judge	65.2	67.7
Hard	79.0	65.6
Competence per tier — sealed set
Easy	86.8	78.4
Standard	78.8	68.7
Judge	76.9	66.9
Hard	74.0	58.9

Published values

Measure	Jev 1.13.0 (TypeSafe AI)	basal-omni-1.0-ultra (Remek Kinas, Gemma-4-31B-it + merged LoRA, typed-decision readout, self-hosted)
Capability Score	77.1	82.5
Rank (open-weights board)	Reference, not ranked	#3
Composite (secondary)	71.5	71.7
intelligence	63.6	74.1
calibration	90.6	91.0
speed	91.5	87.3
cost	54.7	50.4
USD per 1,000 decisions	$0.032 per 1,000 decisions	$0.045 per 1,000 decisions
p50 latency (seconds)	0.2	0.102 measured on-GPU, no network · 0.35 est. incl. network (2x + 150 ms)
Last measured	2026-10-05	2026-10-17

* Self-hosted rows: the first value is the p50 we measured on our own hardware over loopback (on-GPU, or on-CPU where marked, no network). It is not directly comparable with API latency, which we time end to end from our measuring host, network included. “est.” is the est. incl. network (2x + 150 ms) that the ranking and the latency cap use for every self-hosted row. Model authors can ask us to time a hosted endpoint instead (on request).

Jev 1.13.0 (TypeSafe AI)

proprietary API

Published source

Measured on: the operator's hosted API
Cost basis (not published): TypeSafe list tariff USD 0.042 per 1M input tokens, output free; measured on the v1.6.1 common cost basis (all items except those outside the Jev reference input range) from this run's token usage
Latency adjustment: none (API)

basal-omni-1.0-ultra (Remek Kinas, Gemma-4-31B-it + merged LoRA, typed-decision readout, self-hosted)

Apache-2.0 (model card and HF metadata; base google/gemma-4-31B-it Apache-2.0), public, not gated

Published source

Measured on: Evaluator-owned rented Lium GPU pod, 1x NVIDIA H200 141 GB (sm_90, driver 580.173.02), Finland (EU), SPOT tier, USD 3.99/h, 17 Oct 2026; sealed run 08:59:30-09:25:01Z, one request at a time, no resumes. Speed is as measured on that card; the card's own figure is 80 ms p50 on an H200 (measured here: raw p50 0.107 s / p95 0.216 s over all 1,485 answered items; the Speed axis uses the official latency subset given in the row note, with the standing x2 + 0.15 s self-hosted adjustment on top). DEVICE DEVIATION: the pre-registered device was one non-preemptible 80 GB H100; no single-GPU H100 was rentable in the EU, so the run used this one H200 on the spot tier under a pre-registration amendment written before the sealed run (the card says about 32,000 tokens fit on a 141 GB H200 and about 10,000 on an 80 GB H100; the same 15 items were predicted to be refused on either card). Speed is therefore not strictly comparable to the H100 / RTX 5090 rows. Isolation: the author's server ran inside a network namespace with loopback only, with the cut PROVEN before any sealed item was dispatched (no interfaces but lo, no routes, outbound by name and by IP both failing) and proven again immediately before the first sealed request; credential-free environment (env -i, checked), HF offline flags, weights from a locally verified offline snapshot of the pinned revision (13 files, 62,578,671,091 bytes, every sha256 equal to the author's SHA256SUMS), sealed input held in tmpfs and deleted on the pod at 09:24:55Z, server process group killed by exact pid. Transfer: HF_HUB_DISABLE_XET=1 (plain HTTPS, same verification).
Cost basis (estimated): ESTIMATE (*est.), base-model market reference through the frozen pricing_v15 module: declared base google/gemma-4-31B-it -> google/gemma-4-31b-it USD 0.09/M input, USD 0.33999999999999997/M output (frozen at the 25 Sep cut-off, I-3). Estimated, not charged, not a quote from the author or any host. USD per 1,000 decisions over the common 1,485-item cost basis. Tokens as reported by the author's own server (usage.input_tokens = tokens of the rendered chat prompt incl. system prompt, options and the answer prefill; output_tokens 0, nothing is generated - the model reads the option-code logits at one answer position in one forward pass). The offline count through the author's own renderer and the pinned tokenizer predicted the server's numbers exactly, item by item: 744,183 input tokens over the 1,485 answered items. COMMON COST BASIS (v1.6.30): cost per 1,000 decisions is computed on the common 1,485 items of this draw, leaving out the 15 very long items (at least 76,358 input tokens as counted by the rows that answered them). They are the 15 items the Jev reference refused with max_tokens_exceeded, which the other rows that did not answer them refused or failed on; their tokens count for no row, whether the row refused or answered them, and a refused item still counts as a wrong answer for Intelligence. This row: USD 0.045102 per 1,000 decisions (0.698x the official cost cap USD 0.064595), against USD 0.044651 over all 1,500 items (v1.6.29).
Latency adjustment: x2 + 0.15 s (assumption, not measured)

Frequently asked questions

What does JevBench show for Jev and basal-omni-1.0-ultra?: Jev 1.13.0 has Capability 77.1 and is the unranked reference (the open-weights board ranks 139 open-weights systems; hosted API offerings are ranked on the API leaderboard). basal-omni-1.0-ultra (Remek Kinas, Gemma-4-31B-it + merged LoRA, typed-decision readout, self-hosted) has Capability 82.5. Rank #3 on the open-weights board in v1.6.39. Historical scores use their original method and are not ranked against the current release.
How does Capability differ from Composite?: Capability is the mean of Intelligence and Calibration; headline ranks require both official cost and median-latency caps. Composite additionally scores Speed and Cost and is secondary.
Can I compare the prices as actual bills?: Jev 1.13.0 (TypeSafe AI): not published, $0.032 per 1,000 decisions. basal-omni-1.0-ultra (Remek Kinas, Gemma-4-31B-it + merged LoRA, typed-decision readout, self-hosted): estimated, $0.045 per 1,000 decisions. Estimates and self-reported vendor prices are not measured charges.
Is basal-omni-1.0-ultra open source?: The published licence note for basal-omni-1.0-ultra (Remek Kinas, Gemma-4-31B-it + merged LoRA, typed-decision readout, self-hosted) is Apache-2.0 (model card and HF metadata; base google/gemma-4-31B-it Apache-2.0), public, not gated. Jev 1.13.0 is listed as proprietary API. Check the linked sources for terms.