Benchmark avg
28.3
6 tracked scores
ActiveModel portfolio
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Community · Closed · Context 32K tokens
Cadle Score
—
Unranked · 40%
Benchmark average
—
Not enough benchmark results yet
Pricing
Unlisted
no list price
MODEL MATRIX · TRUTH CENTER CELLS
Overall
—
Intelligence
—
Coding
—
Reasoning
14.1
Language
47.0
Knowledge
27.2
Agentic
—
MMLU
27.2
GPQA
12.1
Math
20.8
LiveCodeBench
—
HumanEval
—
Reason
—
ARC-AGI
—
Coverage AVG
18.4
Cadle Cap ranks models with within-benchmark percentiles so ARC-AGI ~40 and MMLU ~90 never compete raw. Coverage AVG is an importance-weighted mean of filled expert cells only — blanks are skipped, not zeroed.
ANALYSIS · TRUTH CENTER
Hard benches (GPQA · ARC-AGI · LCB · Agentic) sit left — a mid-40 on ARC-AGI can still be frontier. Broader benches on the right are easier and often higher.
Only measured signals
Benchmark avg
28.3
6 tracked scores
Strongest signal
ifeval
Score 55.8
Blended $/1M
Unlisted
No published list price
Context
32K
Provider context window (not max output)
PROVIDER
Community
CONTEXT
32K tokens
STATUS
ACTIVE
Real operator evaluations, prompt robustness observations, and deployment notes for Bielik 11B v2.3 Instruct.
Tested Bielik 11B v2.3 Instruct on high-concurrency tool loops. Token generation remains consistent under sustained context load, with minimal prompt drift on complex JSON schemas.
Evaluated Bielik 11B v2.3 Instruct on multi-turn code synthesis. Code reasoning and diff application succeed on ~88% of test suites. Best paired with strict system prompt guardrails.
| Metric | |
|---|---|
| Provider | Community |
| Release | |
| Input / 1M | No data yet |
| Output / 1M | No data yet |
| Context | 32K tokens |
| Cadle Cap | 37 |
| Cap board | Unranked |
| mmlu-pro | 27.2 |
| musr | 16.0 |
| gpqa-diamond | 12.1 |
| math-level-5 | 20.8 |
| bbh | 38.1 |
| ifeval | 55.8 |
Scores may use different scales — check the Benchmarks tab for source links.
Same Truth Center numbers as the datasheet above. Accuracy, reliability, safety, and cost scores are not invented.