Hosting = where inference runs; company = where the provider or lab is registered.
EU hosting means the route's inference runs inside the EU: an EU region, AWS Bedrock's EU cross-region (geo) profiles, Azure's Europe Data Zone, or a provider whose entire public fleet is documented as EU-hosted, each checked per model against the provider's documentation. Global deployments do not count, and neither does an EU billing region, an EU company or an EU control plane on its own. One disclosed company-policy exception stays in, marked “EU equivalent”.
Data confidentialitywhat the provider may do with your prompts
More settings
Evidence requirements (benchmark evidence, priced provider, measured task tokens) sit above the table they apply to.
“Hide deprecated” removes retired models (every Artificial Analysis configuration retired, on no current DesignArena board) and older retired configurations of current models. Applies to model tables, charts, price views & model offers; benchmark evidence stays unfiltered.
Compare Jev-compatible models by separate measures and their measurement conditions. A historical measurement is not a current rank.
Jev and kettei-fat: 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.
B: kettei-fat— Closed API (weights not public) · Score 77.7 (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
kettei-fat: 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), Jev 1.13.0: S+P+L1+L2+L3; kettei-fat: open+sealed 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)
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)
kettei-fat: 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.
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), Jev 1.13.0: S+P+L1+L2+L3; kettei-fat: open+sealed 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)
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)
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.
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
Vendor-hosted api.sqwish.ai/v1/systemone API, model `kettei-fat` (Sqwish docs: kettei-fat v1.0, released 14 Oct 2026), through the unchanged TypeSafe adapter; latency measured from Sandy (Germany) without API adjustment. Full S1200+P300 draw of v1.6-regular-20261012 through the unchanged run_api_full_v161.py; the full-set exposure gate logged all 1,200 sealed items to provider `sqwish` before the first request (custody #20263, deputy #20305/#20306). Native probabilities on all three question types. The runner recorded kettei-fat as the answering model on all 1,500 rows (the adapter reads the response's model field), so no row was answered by a fallback size. 15 of 1,500 items exceed the model's documented 65,536-token input limit (HTTP 422 context_too_long) and are scored as failures. Paid public fast-lane order a87f712c, run with the customer's own API key; hosting implementation and deployed weights were not independently inspected.
Cost basis (estimated)
Sqwish public price list, USD 0.06/M input, output not billed (docs.sqwish.ai/guides/pricing, fetched 16 Oct 2026 16:17Z, saved copy sha256 698a29e5...): kettei fat at USD 0.06 per million input tokens for decisions; "The models generate no output tokens, and none are billed"; "standard list prices, the same since launch on 14 October 2026". Counted as the manufacturer's standard launch list price under interpretation I-1 (the reading that admitted kushal-gemma4-31b-it-autoloops, SubQ Snap, d1-ultra, Sage 1.4 and Nimble), through a scoped re-freeze of the frozen pricing module that adds the two kettei keys and nothing else, with a zero-difference proof for every other registry entry. The once-per-inbox USD 5.00 starter credit is a trial credit and is not used. ESTIMATE in the scorer's sense: the tariff x this row's OWN provider-reported tokens (650,605 input tokens over the 1,485 answered items, 0 output tokens), divided by the 1,485 items of the common cost basis the scorer uses; the 15 items refused with HTTP 422 count as zero tokens and as wrong answers, as every refused request on this draw does (d1-ultra's identical 15 refusals included). The unchanged runner also wrote a conservative cost ceiling into those 15 refused rows (65,536 input tokens each, the model's input limit); the scoring copy drops that one field on those 15 rows only. With the ceiling charged on all 1,500 items (the former all-items basis), the figure would be USD 0.065346 per 1,000 decisions (1.012x the official cost cap); that is a sensitivity, not this row's cost, and on the common basis the 15 refused items are not part of cost at all. Not a bill and not a quote. 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 0.026287 per 1,000 decisions (0.407x the official cost cap USD 0.064595), against USD 0.026024 over all 1,500 items (v1.6.29).
Latency adjustment
none (API)
Frequently asked questions
What does JevBench show for Jev and kettei-fat?
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). kettei-fat (Sqwish) has Capability 86.3. API offering, ranked on the API leaderboard 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. kettei-fat (Sqwish): estimated, $0.026 per 1,000 decisions. Estimates and self-reported vendor prices are not measured charges.
Is kettei-fat open source?
The published licence note for kettei-fat (Sqwish) is Proprietary hosted API (no weights release). Jev 1.13.0 is listed as proprietary API. Check the linked sources for terms.