Hugging Face's logo

Datasets:
Leptos-ai
/
aurelis-decision-index

License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
engine: string
engine_options: struct<model: string>
  child 0, model: string
model_source: struct<kind: string, model: string, num_ctx: int64, single: bool, policy: string>
  child 0, kind: string
  child 1, model: string
  child 2, num_ctx: int64
  child 3, single: bool
  child 4, policy: string
loaded_seconds: double
frozen_corpus_sha256: string
rows_path: list<item: string>
  child 0, item: string
runner_sha256: string
latency: string
edition: string
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
  child 0, median: double
  child 1, p95: double
  child 2, mean: double
note: string
counts: struct<ok: int64, unsupported: int64>
  child 0, ok: int64
  child 1, unsupported: int64
benchmarks: list<item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: (... 6289 chars omitted)
  child 0, item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: int64, err (... 6277 chars omitted)
      child 0, catalog_id: int64
      child 1, dataset: string
      child 2, requests: int64
      child 3, answered: int64
      child 4, unsupported: int64
      child 5, errors: int64
      child 6, abstained: int64
      child 7, pending: int64
      child 8, scored_requests: int64
      child 9, metric: string
      child 10, score: double
      child 11, reference_same_cases: null
      child 12, median_ms: double
      child 13, detail: struct<field_accuracy: double, case_exact_accu
...
iSarcasmEval-A-Ar: struct<metric: string, score: null, scored_requests: int64>
              child 0, metric: string
              child 1, score: null
              child 2, scored_requests: int64
          child 3, iSarcasmEval-A-En: struct<metric: string, score: null, scored_requests: int64>
              child 0, metric: string
              child 1, score: null
              child 2, scored_requests: int64
          child 4, iSarcasmEval-B-En: struct<metric: string, score: null, scored_requests: int64>
              child 0, metric: string
              child 1, score: null
              child 2, scored_requests: int64
          child 5, iSarcasmEval-C-Ar: struct<metric: string, score: null, scored_requests: int64>
              child 0, metric: string
              child 1, score: null
              child 2, scored_requests: int64
          child 6, iSarcasmEval-C-En: struct<metric: string, score: null, scored_requests: int64>
              child 0, metric: string
              child 1, score: null
              child 2, scored_requests: int64
          child 7, GSM8K-10choice: struct<metric: string, score: double, scored_requests: int64>
              child 0, metric: string
              child 1, score: double
              child 2, scored_requests: int64
          child 8, GSM8K-4choice: struct<metric: string, score: double, scored_requests: int64>
              child 0, metric: string
              child 1, score: double
              child 2, scored_requests: int64
to
{'engine': Value('string'), 'counts': {'ok': Value('int64'), 'unsupported': Value('int64')}, 'successful_request_latency_ms': {'median': Value('float64'), 'p95': Value('float64'), 'mean': Value('float64')}, 'benchmarks': List({'catalog_id': Value('int64'), 'dataset': Value('string'), 'requests': Value('int64'), 'answered': Value('int64'), 'unsupported': Value('int64'), 'errors': Value('int64'), 'abstained': Value('int64'), 'pending': Value('int64'), 'scored_requests': Value('int64'), 'metric': Value('string'), 'score': Value('float64'), 'reference_same_cases': Value('null'), 'median_ms': Value('float64'), 'detail': {'field_accuracy': Value('float64'), 'case_exact_accuracy': Value('float64'), 'custom_metrics': {'ndcg_at_10': Value('float64'), 'mrr': Value('float64'), 'recall_at_10': Value('float64'), 'candidate_recall': Value('float64'), 'scorable_candidate_recall': Value('float64'), 'candidates_scored': Value('float64'), 'candidates_retrieved': Value('int64'), 'bm25_ndcg_at_10': Value('float64'), 'quality_quality': Value('float64'), 'quality_cost_usd': Value('float64'), 'quality_utility': Value('float64'), 'quality_oracle_optimal': Value('float64'), 'quality_utility_regret': Value('float64'), 'cost_aware_quality': Value('float64'), 'cost_aware_cost_usd': Value('float64'), 'cost_aware_utility': Value('float64'), 'cost_aware_oracle_optimal': Value('float64'), 'cost_aware_utility_regret': Value('float64'), 'value_regret': Value('float64'), 'normalized_rank_regret': Value('float6
...
Value('int64'), 'mean': Value('int64')}}, 'cluster_macro_accuracy': Value('float64'), 'positive_f1_by_field': {'sarcastic': Value('float64'), 'sarcasm': Value('float64'), 'irony': Value('float64'), 'satire': Value('float64'), 'understatement': Value('float64'), 'overstatement': Value('float64'), 'rhetorical_question': Value('float64')}, 'category_macro_f1': Value('float64'), 'scored_fields': Value('int64'), 'chance_on_rows': Value('float64')}, 'tracks': {'RouterBench-0shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'RouterBench-5shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-B-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}}}), 'note': Value('string'), 'edition': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              engine: string
              engine_options: struct<model: string>
                child 0, model: string
              model_source: struct<kind: string, model: string, num_ctx: int64, single: bool, policy: string>
                child 0, kind: string
                child 1, model: string
                child 2, num_ctx: int64
                child 3, single: bool
                child 4, policy: string
              loaded_seconds: double
              frozen_corpus_sha256: string
              rows_path: list<item: string>
                child 0, item: string
              runner_sha256: string
              latency: string
              edition: string
              successful_request_latency_ms: struct<median: double, p95: double, mean: double>
                child 0, median: double
                child 1, p95: double
                child 2, mean: double
              note: string
              counts: struct<ok: int64, unsupported: int64>
                child 0, ok: int64
                child 1, unsupported: int64
              benchmarks: list<item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: (... 6289 chars omitted)
                child 0, item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: int64, err (... 6277 chars omitted)
                    child 0, catalog_id: int64
                    child 1, dataset: string
                    child 2, requests: int64
                    child 3, answered: int64
                    child 4, unsupported: int64
                    child 5, errors: int64
                    child 6, abstained: int64
                    child 7, pending: int64
                    child 8, scored_requests: int64
                    child 9, metric: string
                    child 10, score: double
                    child 11, reference_same_cases: null
                    child 12, median_ms: double
                    child 13, detail: struct<field_accuracy: double, case_exact_accu
              ...
              iSarcasmEval-A-Ar: struct<metric: string, score: null, scored_requests: int64>
                            child 0, metric: string
                            child 1, score: null
                            child 2, scored_requests: int64
                        child 3, iSarcasmEval-A-En: struct<metric: string, score: null, scored_requests: int64>
                            child 0, metric: string
                            child 1, score: null
                            child 2, scored_requests: int64
                        child 4, iSarcasmEval-B-En: struct<metric: string, score: null, scored_requests: int64>
                            child 0, metric: string
                            child 1, score: null
                            child 2, scored_requests: int64
                        child 5, iSarcasmEval-C-Ar: struct<metric: string, score: null, scored_requests: int64>
                            child 0, metric: string
                            child 1, score: null
                            child 2, scored_requests: int64
                        child 6, iSarcasmEval-C-En: struct<metric: string, score: null, scored_requests: int64>
                            child 0, metric: string
                            child 1, score: null
                            child 2, scored_requests: int64
                        child 7, GSM8K-10choice: struct<metric: string, score: double, scored_requests: int64>
                            child 0, metric: string
                            child 1, score: double
                            child 2, scored_requests: int64
                        child 8, GSM8K-4choice: struct<metric: string, score: double, scored_requests: int64>
                            child 0, metric: string
                            child 1, score: double
                            child 2, scored_requests: int64
              to
              {'engine': Value('string'), 'counts': {'ok': Value('int64'), 'unsupported': Value('int64')}, 'successful_request_latency_ms': {'median': Value('float64'), 'p95': Value('float64'), 'mean': Value('float64')}, 'benchmarks': List({'catalog_id': Value('int64'), 'dataset': Value('string'), 'requests': Value('int64'), 'answered': Value('int64'), 'unsupported': Value('int64'), 'errors': Value('int64'), 'abstained': Value('int64'), 'pending': Value('int64'), 'scored_requests': Value('int64'), 'metric': Value('string'), 'score': Value('float64'), 'reference_same_cases': Value('null'), 'median_ms': Value('float64'), 'detail': {'field_accuracy': Value('float64'), 'case_exact_accuracy': Value('float64'), 'custom_metrics': {'ndcg_at_10': Value('float64'), 'mrr': Value('float64'), 'recall_at_10': Value('float64'), 'candidate_recall': Value('float64'), 'scorable_candidate_recall': Value('float64'), 'candidates_scored': Value('float64'), 'candidates_retrieved': Value('int64'), 'bm25_ndcg_at_10': Value('float64'), 'quality_quality': Value('float64'), 'quality_cost_usd': Value('float64'), 'quality_utility': Value('float64'), 'quality_oracle_optimal': Value('float64'), 'quality_utility_regret': Value('float64'), 'cost_aware_quality': Value('float64'), 'cost_aware_cost_usd': Value('float64'), 'cost_aware_utility': Value('float64'), 'cost_aware_oracle_optimal': Value('float64'), 'cost_aware_utility_regret': Value('float64'), 'value_regret': Value('float64'), 'normalized_rank_regret': Value('float6
              ...
              Value('int64'), 'mean': Value('int64')}}, 'cluster_macro_accuracy': Value('float64'), 'positive_f1_by_field': {'sarcastic': Value('float64'), 'sarcasm': Value('float64'), 'irony': Value('float64'), 'satire': Value('float64'), 'understatement': Value('float64'), 'overstatement': Value('float64'), 'rhetorical_question': Value('float64')}, 'category_macro_f1': Value('float64'), 'scored_fields': Value('int64'), 'chance_on_rows': Value('float64')}, 'tracks': {'RouterBench-0shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'RouterBench-5shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-B-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}}}), 'note': Value('string'), 'edition': Value('string')}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Aurelis on the Decision Index 0.3

Complete runs of the public 0.3 suite for Aurelis 2B, made with the Decision Index kit at 9eb2dbe and scored by it.

model served from public index (0.3) answered run
Aurelis 2B Ollama, mrscratchcat22/aurelis:2b, the published build 51.29 99.95% runs/aurelis-2b
Aurelis 2B, development run Ollama, two builds of the same weights (see below) 51.24 99.95% runs/aurelis-2b-2026-10-16

runs/aurelis-2b is the one to cite. The two runs agree within 0.05 on the index and within 1.5 on every benchmark.

Compared with other models on the board (Aurelis from runs/aurelis-2b, the others from the board's data of 2026-10-16; chance-corrected skill, 0 is guessing, 100 is every answer right):

Aurelis 2B LiquidAI d1-3B vLLM-SR d3-nano CoCo-Decision-4B Perplexity Decider Flash v1.2 Cloudflare clef-flash Cloudflare clef
parameters 2.5B 3.1B 2.2B 4.2B 4.5B 9.7B 27.8B
public index 51.29 48.99 42.22 55.38 60.06 56.15 61.71
Knowledge & Reasoning 32.2 24.3 25.3 34.8 41.4 53.0 52.8
Language Understanding 54.1 56.5 42.2 63.2 65.1 47.2 61.3
Retrieval & Classification 54.2 52.8 58.8 59.9 65.2 52.2 62.8
Tools & Automation 79.5 74.5 58.9 76.8 81.0 81.9 80.7
Arts & Human Taste 35.9 38.9 22.4 40.2 46.6 48.4 48.9
All 37 benchmarks (bold: best of the seven)
benchmark area Aurelis 2B LiquidAI d1-3B vLLM-SR d3-nano CoCo-Decision-4B Perplexity Decider Flash v1.2 Cloudflare clef-flash Cloudflare clef
GPQA Diamond Knowledge 21.8 4.8 9.5 28.6 25.9 35.4 31.3
WinoGrande Knowledge 51.7 55.5 59.7 67.2 77.3 95.1 87.1
GSM8K Knowledge 27.9 19.0 42.2 25.9 61.2 36.3 52.1
ChessBench Knowledge 9.0 8.3 0.6 10.2 14.1 16.2 17.8
MuSR Knowledge 30.6 31.3 35.7 37.6 36.8 78.0 74.2
SATA-Bench Knowledge 38.6 30.4 28.8 33.1 10.5 35.8 32.6
CRUXEval Knowledge 9.0 0.0 7.6 11.5 40.7 78.6 78.6
CLadder Knowledge 71.0 13.2 20.7 67.6 70.8 95.3 88.2
HLE Knowledge 0.0 0.0 0.0 0.0 0.0 0.0 0.0
MMLU-Pro Knowledge 31.8 38.0 36.5 49.2 54.0 60.9 61.7
BBH Knowledge 62.1 60.0 33.9 47.7 61.5 54.9 61.8
ContractNLI Language 74.8 71.9 55.0 77.6 75.3 77.6 73.0
ANLI Language 46.2 43.6 34.0 54.1 54.1 38.8 55.0
HellaSwag Language 79.6 85.6 87.9 92.1 91.8 98.1 97.6
ACOS Language 36.1 35.7 10.2 34.1 33.4 23.6 31.0
FinEntity Language 72.9 82.4 81.7 80.2 86.0 95.7 94.2
iSarcasmEval Language 19.4 17.6 22.9 39.2 47.8 0.0 31.0
VAST Language 59.4 65.5 35.5 66.7 65.3 24.4 39.0
NLI4CT Language 45.9 49.6 44.7 61.5 65.6 58.3 66.9
RAGTruth Language 49.4 53.5 0.0 59.4 63.7 0.0 57.6
BANKING77 Retrieval 86.3 87.8 91.2 89.6 90.0 90.7 94.0
CLINC150 Retrieval 89.6 91.1 95.1 92.1 93.3 66.7 97.4
BRIGHT Retrieval 19.1 32.8 31.0 32.9 38.5 30.1 40.6
Amazon ESCI Retrieval 41.3 38.8 33.7 46.0 47.0 46.6 46.5
PhishNChips Retrieval 64.2 7.7 34.0 18.0 45.1 50.1 58.7
HoVer Retrieval 18.1 48.2 59.8 74.0 72.4 22.4 31.2
BFCL Tools 92.3 85.7 89.4 94.5 94.3 98.3 97.9
ToolRet Tools 56.6 60.4 55.2 44.0 59.7 61.0 64.3
API-Bank Tools 85.0 77.7 55.5 69.9 86.2 93.0 91.8
Home appliances Tools 72.7 63.6 12.5 87.5 75.0 97.7 80.7
When2Call Tools 87.0 82.1 76.4 86.0 86.2 53.8 63.3
BPoMP Arts 88.5 64.3 14.3 87.0 83.2 90.8 94.1
Humicroedit Arts 25.8 27.3 21.1 31.3 23.1 50.1 33.1
POP909 Arts 8.4 23.3 9.5 7.2 25.5 0.7 14.8
cfcolor Arts 14.1 23.4 18.3 28.7 36.0 31.9 34.0
Habermas Arts 43.1 40.2 8.1 22.4 44.3 59.0 54.5
New Yorker Arts 35.8 55.0 63.3 64.7 67.6 57.9 62.8

Each run directory holds the kit's files: results.jsonl.gz, scores.json, index.json, benchmark-summary.json, environment.json and status.json.

How it was run

engine settings hardware
Aurelis 2B aurelis_engine:AurelisOllama (code/aurelis_engine.py) Ollama /v1/systemone, the model's own window (16,384 tokens), one request at a time 1 x AMD Radeon RX 9070 XT (16 GB)

runs/aurelis-2b ran from 2026-10-17 11:22 to 17:57 UTC on a source build of Ollama (commit d9b8601), which serves the model through llama-server.

The engine sends every request's state and questions unmodified, as one /v1/systemone request. Its fixed rules: an empty state is sent as "(no state)" (Ollama refuses an empty one), and a question with more than 24 options is answered in two rounds over groups of 24 in their given order (no option is dropped). A prompt longer than the window is refused by Ollama and recorded as unsupported, never shortened: 66 of the 140,178 scored requests, the same 66 in both runs. Latency of runs/aurelis-2b (request wall time through Ollama, including prompt construction): median 59 ms, mean 169 ms, p95 630 ms.

About runs/aurelis-2b. Until 16:50 UTC the published tag set an 8,192-token window, not the 16,384 the model is trained and documented for; it was republished on ollama.com then with 16,384 and the same weights (layer c2eed024). Ollama refuses a request that does not fit its window rather than shortening it, so 31 requests failed in that part. The answers to requests that fit show no sign of it: against the development run's answers from the same build, those answered before the fix differ by a median largest probability of 0.002 and give the same top answer for 99.8% of questions, those answered after it 0.005 and 98.6%. The kit's resume asked the 31 again at 16,384: 25 were answered, and 6 are longer than 16,384 tokens and recorded unsupported, the same 6 as in the development run. results.jsonl.gz keeps the 31 failed rows before their retries, as the kit's resume writes it; the kit scores the last row of each request, and re-scored it gives 51.29 / 63.71. environment.json names the version tag of the same build.

About runs/aurelis-2b-2026-10-16. The development run, from 2026-10-15 16:24 to 2026-10-16 12:28 UTC. Ollama updated itself during it: requests before about 09:45 UTC were answered by Ollama 0.40.3 from a build of the same weights that used the RENDERER tev1 layout; 0.40.5 no longer loads that build, so the rest were answered by the repackaged build that is published as mrscratchcat22/aurelis:2b (decision type pplx-decider, the trained prompt as tokenizer.chat_template.systemone). Both render the same prompt (188 tokens on the kit's layout check) and their answers differ by a median 0.016 in probability. The 4,174 requests that failed while the update had broken the old build were removed from results.jsonl and answered again on resume. The file here is compacted (payload and raw_output removed, as the kit's --compact writes); re-scored with the kit it gives the same 51.24 / 63.66.

Reproduce 

git clone https://github.com/apolinario/decision-index && cd decision-index && git checkout 9eb2dbe
pip install -e . httpx
cp <this repo>/code/aurelis_engine.py .
ollama pull mrscratchcat22/aurelis:2b           # Ollama 0.40.5 or newer
python -m decision_index pipeline --edition 0.3 --engine aurelis_engine:AurelisOllama \
    --option model=mrscratchcat22/aurelis:2b --compact --out runs/aurelis-2b

Artificial Analysis Decision Index, partly

The Artificial Analysis Decision Index is a different benchmark: 25% each for AA-Omniscience-Decision (private), HLE-MultiChoice, When2Call and WildGuardTest. Two of its public parts are inside this suite with the same items: the 3,423 When2Call tasks AA keeps (229 ambiguous "request for information" tasks dropped, as listed in AA's methodology) and AA's 513 text-only HLE multiple-choice questions. Aurelis answers 92.6% of the When2Call tasks with the options shuffled per task as AA does (92.5% in the kit's order) and 12.1% of the HLE questions, asked again through the published build. Aurelis was trained on When2Call's train split, never on its test. Aurelis is not on AA's board and these are not AA's measurements; models on both boards (Clef Flash, GLiDE) get within about a point of AA's When2Call numbers when scored this way. The other rows are AA's numbers of 2026-10-17:

decision model maker When2Call HLE-MultiChoice AA index
Aurelis 2B (measured here) Leptos 92.6 12.1 —
d1-ultra Liquid AI 92.6 15.0 65.2
Celeris-1 Decision Celeris 83.7 13.3 52.3
GLiDE Fastino 83.7 10.5 53.9
Microsoft-Decision-1 Microsoft 81.9 10.9 53.8
Jev 1.14 TypeSafe 80.8 18.9 57.1
Jev 1.13 TypeSafe 80.7 20.9 57.5
Mercury Decide Inception 79.3 10.1 52.7
d1 Liquid AI 77.4 11.5 50.3
Decider v1.1 27B Perplexity 76.5 15.4 53.1
GPT-6 Luna (Decisions) OpenAI 74.1 11.7 48.5
Clef Cloudflare 71.0 11.1 48.6
Clef-Flash Cloudflare 64.4 10.3 45.8
Clef-Omni Cloudflare 62.1 9.0 44.3
Downloads last month
109
Total file size:
88.9 MB