Jev-Style Decision Models in Ollama 0.35: Nimble and Tev1, Setup and Benchmark

Jev-Style Decision Models in Ollama 0.35: Nimble and Tev1, Setup and Benchmark

Ollama 0.35, released on 28 September, added a /v1/systemone endpoint that follows Jev's API: you send a state and typed questions, and a local model returns a probability for every option instead of text. It ships with three models: Nimble (9B, from Bespoke Labs) and Tev1 (4B and 0.8B, from Together AI), all fine-tuned from Qwen3.5.

I already had Jev, Kev and Jeff measured on the same messages, so I pointed the same harness at Ollama and ran all three models on them: five classification conditions from the earlier posts, and the 4,599-question panel from Jeff vs Jev.

The short answer

• On short classification tasks, Nimble matched or beat Jev. TREC: 95.6% against 89.0% (p < 0.001). AG News and financial-news tweets: within three points either way, not significant.
• On the reasoning-heavy panel, Jev was well ahead: 85.7 against Nimble's 76.0, with the largest gaps on WinoGrande and BBH.
• Ollama's endpoint refused questions with more than 26 options for all three models, with a clear error rather than a wrong answer. BANKING77 (77 intents) cannot be asked as one question.
• Tev1 0.8B trailed on TREC and on the panel overall (41% and 59.7), though it had the top AG News score with descriptions. Both Tev1 sizes reject inputs longer than about 2,050 tokens.

Running them in Ollama

Jev itself is not in Ollama. It is a hosted API from TypeSafe, and the Ollama library has no jev model (checked on 4 October). What Ollama 0.35 added is a Jev-compatible endpoint, /v1/systemone, with the same request and response shape, and three local models that answer it. If you already call Jev, change the address and the model name and drop the API key header.

Pull a model and make sure the server is 0.35 or later:

ollama pull nimble        # 9.5 GB; tev1:4b is 4.5 GB, tev1:0.8b is 0.8 GB
curl -s http://localhost:11434/api/version

Then send a state and a typed question. This is the exact request the harness sent for the first TREC test question in the run with label descriptions (the question text is spelled as the dataset spells it):

curl -s http://localhost:11434/v1/systemone -H 'Content-Type: application/json' -d '{
  "model": "nimble",
  "state": "How far is it from Denver to Aspen ?",
  "questions": {
    "intent": {
      "type": "choice",
      "instructions": "What kind of answer does this question ask for?",
      "criteria": {
        "abbreviation": "an abbreviation or what it stands for",
        "entity": "a thing, such as an animal, product, color, event, substance or term",
        "description": "a definition, explanation, reason or manner",
        "human": "a person, a group of people or an organization",
        "location": "a place, such as a city, country, mountain or state",
        "numeric": "a number, such as a date, count, distance, price or percentage"
      }
    }
  }
}'

Nimble answered:

{"model": "nimble", "answers": {"intent": {"type": "choice", "choice": "numeric", "probabilities": {"abbreviation": 0.000131805257339387, "entity": 0.0002858253588729726, "description": 0.00029337022364426277, "human": 0.00015459642361178205, "location": 0.0005109321678204479, "numeric": 0.9986234705687111}, "confidence": 0.9930231414363588}}, "usage": {"input_tokens": 268, "output_tokens": 1}}

These are the same probability and confidence the harness recorded for this question on 1 October; the request and response are saved in drafts/jev-bench/results/ollama-systemone-example-2026-10-04.json.

Four things to know before you build on it:

• criteria takes 2 to 26 options. More is refused with an HTTP 400, as described below. Shortlist first.
• The descriptions are optional but they matter. The values under criteria can be null. Adding a one-line description per option moved Tev1 4B from 71.2% to 89.8% on TREC.
• confidence is not the top probability. Above, confidence is 0.993 while numeric has 0.9986. The calibration figures in this post use probabilities.
• Check which GPU backend Ollama picked. On this server Ollama listed the A100 twice, once through Vulkan and once through CUDA, and chose Vulkan. Setting OLLAMA_LLM_LIBRARY=cuda_v13 put it on CUDA. Accuracy was identical either way; if your NVIDIA card feels slow under Ollama 0.35, the server log says which backend it chose.

What each model saw in training

This decides how to read every number below, so it comes first.

Nimble publishes its training set: 2,676 synthetic examples across ten subject areas such as commerce, travel and software, with no named public datasets. I matched our test messages against it: none of the 500 TREC questions, 200 AG News articles or 200 financial tweets appear, and the dataset names do not occur in it. As far as its fine-tuning goes, Nimble's results here are zero-shot. What the Qwen3.5-9B base saw before that is not published.

Tev1 lists its sources, and two of them are ours: 1,500 AG News examples and 3,000 BANKING77 examples, alongside MultiNLI, BoolQ and SST-5. AG News is familiar ground for Tev1; TREC and the financial tweets are not.

Jev's training data is not published, so I cannot say either way for it.

Short classification: Nimble matched or beat Jev

Against Jev, message by message, Nimble got 38 TREC questions right that Jev missed and missed 5 that Jev got (p < 0.001); with descriptions, 17 against 4 (p = 0.007). On AG News and the financial tweets the differences were within a few items and not significant (p ≥ 0.34). Kev-9B, which was trained on TREC, scored about the same as Nimble, which was not.

Tev1 4B gained 19 points on TREC when each label got a one-line description, the largest jump of any model here, and stayed level with the others on AG News, which it was trained on. Tev1 0.8B fell to 41% on TREC with or without descriptions.

The reasoning-heavy panel: Jev was well ahead

The panel is the one Jeff publishes: BBH, Financial PhraseBank, JudgeBench, RAGTruth and WinoGrande, 4,599 questions. None of its benchmarks is in Nimble's training set.

Nimble tied Jev on financial sentiment and trailed it everywhere reasoning matters. Nimble's panel total of 76.0 sits between Kev-9B (73.8) and Jeff-2B v1.1 (81.9). Among the Ollama models, Tev1 had the strongest financial sentiment results: on Financial PhraseBank Tev1 4B scored 93.9 and Tev1 0.8B 89.7, both above Jev's 85.3 (103 against 17, p < 0.001; 137 against 93, p = 0.004). SST-5, a sentiment dataset, is in Tev1's training data. Tev1 scored 70.8 (4B) and 59.7 (0.8B), but those totals count 138 rejected questions as wrong; more on that below.

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Two limits you will hit

More than 26 options is refused. Sending BANKING77 with all 77 intents, Ollama 0.35's /v1/systemone returned HTTP 400: question "intent": criteria must contain 2–26 candidates for all three models, and CLINC150 and MASSIVE got the same: identical wording for every model, so it looks like a request check in the endpoint rather than in the models. That is the safer behaviour: Jeff v1.0 accepted such lists and silently never picked past the 26th (fixed in v1.1). But it means a 77-way intent task needs a shortlist step in your code before Ollama sees it. Tev1's own README asks for 2 to 24 options, since that is what it was trained on.

Tev1 rejects long inputs. On the panel, 138 questions came back with errors such as prompt 0 has 3429 tokens; expected 1–2050 (input is never truncated): 116 of JudgeBench's 350 and 22 of RAGTruth's 1,500. Refusing is better than truncating silently, but it caps what you can send. On the JudgeBench questions it did answer, Tev1 4B was right 63.2% of the time; Nimble, which has no such limit here, answered all of them.

Probabilities: well-behaved on TREC, over-confident on reasoning

Ollama returns full-precision probabilities, not the two-decimal rounding Jev's API returns, so thresholds can be set finely. How far the stated probability sat from actual accuracy depended on the task:

On TREC, Nimble's probabilities were almost exact, and a threshold could accept all 500 questions while keeping accepted answers 95% correct. On JudgeBench and WinoGrande it said it was about 21 points surer than it was. Nimble's card says no temperature was fitted after training, and Tev1's README says its "logprobs are model preferences, not calibrated confidence." Both are honest about it; the practical upshot is to set thresholds on your own labelled data, per task.

Speed

On one A100 with nothing else running, one request at a time over local HTTP, the median per TREC question was 192 ms for Nimble, 158 ms for Tev1 4B and 53 ms for Tev1 0.8B. On the same machine Kev-9B answered in 29 ms and Jeff in about 45 ms, so Ollama's path is slower here; I did not investigate why. Jev's API took about 220 ms from Korea; its server-side time was about 53 ms, measured on 30 BANKING77 requests on 29 September.

Which to use

• Short local classification, no data leaving the machine: Nimble. On TREC it matched the best model here, on AG News and the financial tweets it was level with Jev, and its probabilities were well calibrated on TREC.
• Financial sentiment: Tev1 4B scored highest of the Ollama models on Financial PhraseBank, 93.9 against Jev's 85.3.
• Judging answers, multi-step reasoning, pronoun resolution: Jev, by 15 to 20 points on the same questions.
• More than 26 options: shortlist first, whichever Ollama model you use.
• Tev1 0.8B: only for tasks you have measured it on. It did well on AG News, which it trained on, and poorly on TREC, which it did not.

What this does not show

Five short-text conditions and one panel, all English. One version of each model (Ollama's Q8_0 builds) and of Jev (1.13). Nimble's TREC lead is zero-shot with respect to its fine-tuning data, but its base model's pretraining is unknown, and so is Jev's training data. Thresholds were searched on the same samples they describe. Latency is local HTTP on an A100 and will differ on a Mac or a consumer GPU.

The per-sentence results from this post were later re-read together with other decision models' results to collect the sentences many of them get wrong. Where Decision Models Go Wrong

Updated 4 October 2026: added "Running them in Ollama", with the exact request the harness sent; the Vulkan note moved there from Speed.

Setup: Ollama 0.35.0 (Linux amd64 release tarball, CUDA 13 library, OLLAMA_LLM_LIBRARY=cuda_v13), /v1/systemone on 127.0.0.1, one A100 80GB PCIe. Models from the Ollama library: nimble:latest (blob bbf1d6fc03bb, 9.53 GB, Q8_0), tev1:4b (35f9281a3df5, 4.48 GB), tev1:0.8b (fa9732e3924d, 0.81 GB). Same request bodies, data files, label names, descriptions and instructions as the Kev vs Jev and Jeff vs Jev posts; Jev, Kev and Jeff rows are read from those result files. The panel is Jeff's jeff-panel output, 4,599 rows. Nimble's training set is bespokelabsai/nimble data/train.jsonl at 62076b4; overlap was checked by exact match of each test message, and an 80-character span of each panel question, after lowercasing and removing punctuation (scripts/jev-bench/check_nimble_overlap.py; 0 hits in all four sets). Rejected requests count as wrong in totals. Calibration gap is the mean top-option probability minus accuracy. McNemar tests are exact and two-sided. The accuracy runs shared the GPU with another job; latency was measured afterwards on an idle GPU, and both gave the same answers. Measured 2026-10-01.

All the text-classification measurements on this blog, put in the order you would use them: Classifier, LLM or Decision Model? A Measured Guide to Text Classification.

Nimble and Tev1 also took the second stage of a routing system, picking from a classifier's top candidates to get past the 26-option limit: Which messages should go to a person?