Matrix overall
30.7
AVG 24.7 across matrix cells
ActiveModel portfolio
qwen1.5-32b-chat
Tap a star to rate
Alibaba · Open
Cadle Score
116.1
#293 in index
Benchmark average
24.7
Not enough benchmark results yet
Pricing
Unlisted
no list price
MODEL MATRIX · TRUTH CENTER CELLS
Overall
30.7
Intelligence
30.7
Coding
42.0
Reasoning
8.8
Language
50.0
Knowledge
38.4
Agentic
—
MMLU
38.4
GPQA
7.5
Math
19.6
LiveCodeBench
—
HumanEval
—
Reason
—
ARC-AGI
—
Coverage AVG
24.7
Pillar Coverage:6 / 7 Core Pillars (86%)
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.
CADLE INSIGHTS
Only measured signals
Benchmark avg
31.1
7 tracked scores
Strongest signal
ifeval
Score 55.3
Blended $/1M
Unlisted
No published list price
Coding (matrix)
42.0
Model Matrix coding cell
Intel index
30.7
Matrix AA IQ cell
SPEED · COST · QUALITY
Measured matrix and verified gateway observations only.
- Cadle Score
- 116.1
- Overall (Matrix)
- 30.7
- Cadle Intelligence
- 30.7
- Matrix Coding
- 42.0
DESCRIPTION + DETAILS
PROVIDER
Alibaba
STATUS
ACTIVE
COMMUNITY REVIEWS
Community Reviews & Field Notes
Verified EngineersReal operator evaluations, prompt robustness observations, and deployment notes for qwen1.5-32b-chat.
4.5(2)
Leave a Field Observation
Rating:5 - Frontier benchmark
Role:
Tag topic:
Reviews are publicly visible to the engineering community.
Inference OperatorProduction Deployer Verified
Nov 1, 2026
Tested qwen1.5-32b-chat on high-concurrency tool loops. Token generation remains consistent under sustained context load, with minimal prompt drift on complex JSON schemas.
Autonomous Agent LeadEval Maintainer Verified
Oct 28, 2026
Evaluated qwen1.5-32b-chat on multi-turn code synthesis. Code reasoning and diff application succeed on ~88% of test suites. Best paired with strict system prompt guardrails.
MODEL COMPARISON
| Metric | |
|---|---|
| Provider | Alibaba |
| Release | |
| Input / 1M | No data yet |
| Output / 1M | No data yet |
| Context | No data yet |
| Cadle Cap | 116 |
| Cap board | Cadle Language Cap 116 |
| bigcodebench | 42.0 |
| mmlu-pro | 38.4 |
| musr | 10.2 |
| gpqa-diamond | 7.5 |
| math-level-5 | 19.6 |
| bbh | 44.6 |
| ifeval | 55.3 |
DATA PROVENANCE
Scores may use different scales — check the Benchmarks tab for source links.
- Supabase models
- Cadle public observations
MEASURED CELLS
Same Truth Center numbers as the datasheet above. Accuracy, reliability, safety, and cost scores are not invented.
Overall30.7
AA IQ30.7
Coding42
MMLU38.4
GPQA7.5
Math19.6