Gemini Decision 1 vs Kev

Gemini Decision 1 40

Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Status page at aistudio.google.com/status with no readable components or history (10 of 20) and no readable incident record (5 of 30). Rate-limit tiers and spend limits published for the platform, but the public rate-limit page, read on 11 November, has no row for Decision 1 and sends you to AI Studio (5 of 15). Backoff guidance for 429 and 503 and SDK retries (15). No SLA (0). The guide and the model page, read on 11 November, state no launch stage; the endpoint is v1beta and the model has one version, so half, as Gemini 4 Carbon was read (5 of 10). 10 + 5 + 5 + 15 + 0 + 5 = 40.

Kev 73

Scored on the local-package checklist, since Kev is a model you run. It isn't on a package index, because the PyPI name kev belongs to an unrelated 2021 project. It installs from the repository with uv and a lockfile, with Python 3.12 and 3.13 stated, or from release tarballs with SHA-256 checksums (10 of 20). Public CI on GitHub Actions runs the unit tests and the playground build on every push, and the latest runs on main had passed when we looked on 2 October. The model parity and API tests need weights and aren't in CI (20 of 25). One open issue, #8 from 20 September on date arithmetic, which the README lists as a limitation with a workaround, KEV_DATE_FACTS=1 (23 of 25). The Kev 1.0 release notes say what changed, including the server refusing over-long states with a 422 where it used to cut them silently, but the Python package has stayed at 0.1.0 and there's no changelog file (10 of 15). Kev 1.0 fixes the four checkpoints as one versioned family, while the package classifier still says alpha (10 of 15). Re-read on 1 November from a clone. No new release since Kev 1.0, the CI workflow still runs the unit tests and the playground build on pushes and pull requests, and the CI results and issue list rest on the 2 October check, since GitHub's pages answered 403. 10 + 20 + 23 + 10 + 10 = 73.

Gemini Decision 1 60

Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol (0). The pricing page lists $0.10 per million input tokens on the paid tier for every modality and no output charge, without a login (20). Free of charge on the free tier (20). A Google account and an AI Studio key come first (0). 0 + 20 + 20 + 0 = 40. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 40 is 60.

Kev 90

Free Apache-2.0 software with nothing to buy from Kev, so 20 + 20 + 20 for pricing, free use and no sign-up. No payment protocol (0). GPU time is yours, on your own hardware or on Modal at the hourly rates the deploy skill lists. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 60 is 90.

Gemini Decision 1 79

Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released on 11 November 2026, the day of this check (30). Deprecations with earliest dates and no stated minimum notice (4 of 12). Shutdowns in the last 90 days kept to their table (4 of 8). Dated changelogs: google-genai 3.2.0's entry adds the Decisions API; the platform changelog wasn't re-read today (11 of 15). SDK replies not sampled (5 of 10). google-genai 3.2.0 tagged the same day with client.decisions.create; the JavaScript SDK is per Google's post (15). Runtimes stated on the platform (10). 30 + 4 + 4 + 11 + 5 + 15 + 10 = 79. The model is hours old, so these lines are the platform's habits more than this model's record.

Kev 63

Read as a model, for deprecation notice and model churn rather than release counts, on the lines used for Decider, Laya, Jev and the other decision models listed as models. Kev 1.0 on 1 October 2026, a GitHub release and v1.0 tags on all four Hub repositories, is the last release and the last model change. The Hub's last commits on each repository, on 1 October, change only the model card, and the code on main has changed since (fixes to device probing, the fine-tuning skill and tests, to 23 October) without a release. 31 days on 1 November (20). No deprecation policy or notice period. Each new version replaced the weights on main on the day it shipped, and earlier versions stay pinnable under Hub tags (v7-base on the three smaller models, v1 on Kev-9B, v1-lora on Kev-27B), with the retired kev-family release pointing to kev-1.0 (5 of 12, level with Decider's 5). Kev-4B had several weight updates from 20 to 24 September and Kev-9B v2 and Kev-27B v2 replaced main on 30 September, and since Kev 1.0 fixed the four as one versioned family no weights have changed in 31 days, with pins still working (5 of 8, level with Laya's and above Decider's 4 for continuing replacements). Outside pull requests are still merged (#171 and #226 in October), but Jared Palmer wrote 184 of the 210 commits we cloned and a Devin bot 16 more, and issue replies rest on the 2 October check (18 of 25). No SDK of its own. Kev reuses TypeSafe's Python SDK, and agent skills handle deploys and fine-tunes. Not an MCP server, so no registry entry applies (8 of 15). A CI workflow and a uv lockfile, with torch capped below 2.9, and CI results per the 2 October check (7 of 10). 20 + 5 + 5 + 18 + 8 + 7 = 63.

Gemini Decision 1 70

Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Closed service with clear terms, SDKs Apache-2.0 (15). The pricing page states that free-tier content is used to improve products and paid-tier content isn't; abuse retention has no number (15 of 30). Deprecations table; the model page names no deprecation for this model (15 of 20). Data may be cached anywhere Google has facilities; no subprocessor list checked (5 of 20). Every benchmark, latency and cost comparison is Google's own, and the post gives charts without scores in its text. 15 + 15 + 15 + 5 = 50.

Kev 55

Apache-2.0 for the code, adapters and heads, on Apache-2.0 Qwen bases. The model cards give the training recipe, and the document and skill training partitions are on the Hub. Kev-27B's base is Qwen's post-trained release, whose training data is unknown, and the card says so (30). Self-hosted, so inputs stay on your hardware, which the model card states. There's no other data statement, and nothing on what a Modal deploy logs (20 of 30). Earlier versions stay at Hub tags, and the retired kev-family release points to kev-1.0. No deprecation policy (10 of 20). We found no telemetry code in the kev package and the docs say nothing either way. Weights download through the Hugging Face Hub client (10 of 20).

Gemini Decision 1 85

Read on the tool lines, as the other decision-model listings are. Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Answers are probabilities only, and the shared input is billed once across up to 1,000 questions (25 of 25). Limits are written down: 262,144 tokens shared by the input and all questions, 1,000 questions, 2 to 255 options, inline data to 100 MB (50 MB for PDFs), 15 MB per external URL (20 of 20). Errors return HTTP 400 with a named code an agent can branch on, plus the platform's backoff advice (15 of 20). SDK retries and backoff guidance, no idempotency key; nothing is written, so a repeat only costs input again (10 of 20). Three required fields, SDK 3.2 or later in Python and JavaScript, and REST (15 of 15). 25 + 20 + 15 + 10 + 15 = 85.

Kev 78

Read as an API an agent calls for a decision, as with Jev and Clef. Answers are a probability per allowed option, so output stays small, and the server caches the state, so more questions about the same document pay only for the questions. Validated context is 8,192 tokens on the three smaller models and 65,536 on Kev-27B (20 of 25). The caller sets the output shape, with any number of questions a request, and /v1/systemone/separate and /v1/systemone/permute check question isolation and option order (18 of 20). A 422 names the state's token count and the limit, and a missing key gets a 401 saying how to send it. Other errors aren't documented (15 of 20). Calls are stateless and safe to retry. There's no retry guidance and no rate limiting in the server (15 of 20). TypeSafe's Python SDK works unchanged per the README, and kev-latest is the default model, but setup is a clone, a uv sync and a GPU or a Mac (10 of 15).

Gemini Decision 1 45

Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Keys bound to a service account and restricted to the Gemini API, with no scopes and a token accepted in the URL (15 of 30, the platform's deduction). The pricing page, read on 11 November, says free-tier content is used to improve Google's products and paid-tier content isn't (10 of 20, the platform's reading). Abuse retention with no number (5 of 15). Request logs for billed projects are described for Generate Content; the Decisions guide says nothing on logging (10 of 15). security.txt and a reward programme, no certification naming the API (5 of 20). 15 + 10 + 5 + 10 + 5 = 45.

Kev 52

Read as a model, for retention, training and zero-retention options in place of read-only modes, on the lines used for the other decision models listed as models. No account. kev.serve binds to 127.0.0.1 and is open by default, and one optional bearer key (KEV_API_KEY) guards /v1/*. The deploy skill tells agents to always set it on Modal (15 of 30). Self-hosted, so nothing reaches the author and no caller data can train a model. Each model card says self-hosting keeps inputs on your own hardware and to apply your own data-handling policy. On the Modal route the operator's own Modal logs show every request, and nothing says what Modal keeps (18 of 20, level with Cloudflare's Clef, which keeps nothing unless stored). Caller text is tokenised so it can't produce Kev's delimiter tokens, questions can't read each other, and the playground has presets for fake delimiters. Nothing documents how hostile text in the state can move an answer, and the cards say not to make consequential decisions about people without human review (9 of 15). Each response carries a request ID, token usage and model time. The server keeps no log of calls (6 of 15). No SECURITY.md, disclosure policy, certifications or advisories. Weights are pinned by Hub revision and the release tarballs carry SHA-256 checksums. The pointer head is a pickled head.pt loaded with torch.load, which PyTorch 2.6 and later, the versions Kev requires, read in weights-only mode by default (4 of 20). 15 + 18 + 9 + 6 + 4 = 52.