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. René-1 31B FP8 (salfatigroup, Gemma 4 31B full fine-tune, one-pass option readout): estimated, $0.063 per 1,000 decisions. Estimates and self-reported vendor prices are not measured charges.

Is René-1 31B FP8 open source?

The published licence note for René-1 31B FP8 (salfatigroup, Gemma 4 31B full fine-tune, one-pass option readout) is apache-2.0. Jev 1.13.0 is listed as proprietary API. Check the linked sources for terms.

Jev vs René-1 31B FP8: published benchmark comparison

Q: What does JevBench show for Jev and René-1 31B FP8?

Jev 1.13.0 has Capability 77.1 and is the unranked reference (the open-weights board ranks 92 open-weights systems; hosted API offerings are ranked on the API leaderboard). René-1 31B FP8 (salfatigroup, Gemma 4 31B full fine-tune, one-pass option readout) has Capability 76.0. Rank #4 on the open-weights board in v1.6.1. Historical scores use their original method and are not ranked against the current release.

Data: JevBench v1.6.1, published 2026-10-06

Rank #4 on the open-weights board in v1.6.1.

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

Jev and René-1 31B FP8: 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: René-1 31B FP8 — Open weights · LLM decoder · Score 55.8 (#4)Language and category supplements have not been measured for this newly released row; the original current-pool category counts are shown.
B: Jev 1.13.0 — 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

René-1 31B FP8: Language and category supplements have not been measured for this newly released row; the original current-pool category counts are shown.

What the decision is about — math and dates, coding, rules, money, support work, everyday messages, safety. Chance-corrected competence per category (0 = chance, 100 = perfect), René-1 31B FP8: S+P; 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)

René-1 31B FP8: Language and category supplements have not been measured for this newly released row; the original current-pool category counts are shown.

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 = chance, 100 = perfect), René-1 31B FP8: S+P; 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 (René-1 31B FP8) is measured on 9 of 20 spokes only, so it is drawn as points, not a shape — measured on fewer items; full radar after the next re-measure.Gaps, not zeros: Open spokes (n/a or n=…) have fewer than 30 answered items for that system or no published value.

Low sample, n < 30 — indicative only

Category (items)	A: René-1 31B FP8	B: Jev 1.13.0
Feature extraction for predictive modeling (99; a system answered fewer than 30)	19.5 n=19	28.3 n=99
Lead generation (95; a system answered fewer than 30)	93.8 n=17	49.2 n=95
Graphs and knowledge graphs (94; a system answered fewer than 30)	69.6 n=18	28.8 n=94
LLM guardrails (93; a system answered fewer than 30)	55.3 n=17	68.0 n=93
Semantic code linting (90; a system answered fewer than 30)	37.1 n=16	29.1 n=90
Search and retrieval (90; a system answered fewer than 30)	77.0 n=17	80.9 n=90
Demand forecasting (83; a system answered fewer than 30)	15.6 n=16	0.0 n=83

Not drawn on the radar: with so few items a single answer moves a category score by several points, so these values are noise-prone. These values become spokes once the item pool reaches 30 items per category.

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

Chance-corrected competence (0 = chance) for Choice, Noul and Score on the 300 open and 1200 sealed decisions.

Competence per tier — open set

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

Competence per tier — sealed set

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: René-1 31B FP8	B: Jev 1.13.0
The four score axes
Intelligence	61.7	63.6
Calibration	90.2	90.6
Speed	87.1	91.5
Cost	46.0	54.7
Capability by subject topic
Rules, policy & law	71.2	49.4
Coding & software	69.0	64.3
Math & numbers	25.5	10.4
Finance & commerce	65.8	40.9
Support & operations	35.9	48.8
Everyday language	36.0	77.5
Safety & security	24.7	52.2
Use cases (TypeSafe categories)
Model routing	72.1	73.0
Legal & compliance	76.8	68.6
Customer support	49.1	49.6
Other	8.9	40.1
E-commerce	53.8	40.5
Insurance claims	73.0	46.1
Risk assessment	39.8	32.8
Financial crime	33.3	35.8
Feature extraction	n=19	28.3
Lead generation	n=17	49.2
Recruiting	—	0.0
Knowledge graphs	n=18	28.8
LLM guardrails	n=17	68.0
Moderation	69.6	51.6
Code linting	n=16	29.1
Search & retrieval	n=17	80.9
Science	—	21.2
Advertising	—	16.4
Gaming	—	5.1
Demand forecasting	n=16	0.0
Competence per request type, open / sealed
Choice · open	78.7	79.0
Choice · sealed	78.9	75.8
Noul · open	53.3	54.5
Noul · sealed	47.4	46.1
Score · open	56.8	63.3
Score · sealed	55.3	63.0
Competence per tier — open set
Easy	90.2	93.9
Standard	48.2	63.2
Judge	63.4	67.7
Hard	69.9	65.6
Competence per tier — sealed set
Easy	77.5	78.4
Standard	64.0	68.7
Judge	71.7	66.9
Hard	57.7	58.9

Published values

Measure	Jev 1.13.0 (TypeSafe AI)	René-1 31B FP8 (salfatigroup, Gemma 4 31B full fine-tune, one-pass option readout)
Capability Score	77.1	76.0
Rank (open-weights board)	Reference, not ranked	#4
Composite (secondary)	71.5	55.8
intelligence	63.6	61.7
calibration	90.6	90.2
speed	91.5	87.1
cost	54.7	46.0
USD per 1,000 decisions	$0.032 per 1,000 decisions	$0.063 per 1,000 decisions
p50 latency (seconds)	0.2	0.4
Last measured	2026-10-05	2026-10-07

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)

René-1 31B FP8 (salfatigroup, Gemma 4 31B full fine-tune, one-pass option readout)

apache-2.0

Published source

Measured on: Candidate re-measured on 2026-10-07, current v1.6.0/v1.6.1 1500-item pool (sha256901983ae...). Immutable salfatigroup/rene-1-31b-fp8@bd634489957f8da63ccce858ee33fd6c2929d286, Apache-2.0. Same unmodified reviewed contributor source and run-39 transport shim. Author noul/yesno type mapping only; two-decimal probability rounding retained. One evaluator-owned RTX PRO6000 Blackwell 96GB pod, loopback-only network namespace, offline credential-free inference; no external hosted API, no retry or batching. Unmodified run_v16/typesafe adapter, serial one request at a time, selfhosted x2+0.15s latency adjustment. Missing ATTRIBUTIONS.md and NOTICE disclosed: training-data overlap cannot be checked. Florian GO in DECISIONS.md (go gor it, Apache2.0 confirmed) resolves prior attribution hold; publication remains release lane responsibility. COMPLETE: 1500/1500 distinct input IDs, 1477 HTTP200 native answers,23 HTTP422 refusals under the author's own32768-token state+question /65536-token request budget; every refusal counted wrong. Smoke passed all8 checks. All42 release-manifest files and6 prior source files hash-verified. Raw sha256 a28a8f0068f38b548f064a6e678bf63b13304f9add0a67b448dcc8003c9f38f1. Stack torch2.14.0, transformers5.17.0, compressed-tensors0.19.0, accelerate1.15.0. Provider pod91916684-9980-419c-98b7-c7a714a3912c removed and verified absent; approxUSD0.17. Fresh latest feed places this candidate inside both official caps; material open-board top-five change requires release-lane preview disposition before publication.
Cost basis (estimated): ESTIMATE (base-model reference, not charged). Unmodified pricing_v15.reference resolves google/gemma-4-31B-it to google/gemma-4-31b-it at USD0.09/M input and USD0.34/M output in frozen snapshot. Measured output tokens are 0 by construction (decision_contract.py and release.json); one forward-pass option readout. Common v1.6.1 cost input range applied unchanged.
Latency adjustment: x2 + 0.15 s (assumption, not measured)

Frequently asked questions

What does JevBench show for Jev and René-1 31B FP8?: Jev 1.13.0 has Capability 77.1 and is the unranked reference (the open-weights board ranks 92 open-weights systems; hosted API offerings are ranked on the API leaderboard). René-1 31B FP8 (salfatigroup, Gemma 4 31B full fine-tune, one-pass option readout) has Capability 76.0. Rank #4 on the open-weights board in v1.6.1. 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. René-1 31B FP8 (salfatigroup, Gemma 4 31B full fine-tune, one-pass option readout): estimated, $0.063 per 1,000 decisions. Estimates and self-reported vendor prices are not measured charges.
Is René-1 31B FP8 open source?: The published licence note for René-1 31B FP8 (salfatigroup, Gemma 4 31B full fine-tune, one-pass option readout) is apache-2.0. Jev 1.13.0 is listed as proprietary API. Check the linked sources for terms.