Matrix overall
75.4
AVG 75.4 across matrix cells
ActiveModel portfolio
O3-Mini-2025-01-31 (Med)
Tap a star to rate
OpenAI · Closed · Context 128K tokens
Cadle Score
—
Unranked · 40%
Benchmark average
—
Not enough benchmark results yet
Pricing
Unlisted
no list price
MODEL MATRIX · TRUTH CENTER CELLS
Overall
75.4
Intelligence
—
Coding
75.4
Reasoning
—
Language
—
Knowledge
—
Agentic
—
MMLU
—
GPQA
—
Math
—
LiveCodeBench
75.4
HumanEval
—
Reason
—
ARC-AGI
—
Coverage AVG
75.4
Pillar Coverage:1 / 7 Core Pillars (14%)
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
75.4
1 tracked scores
Strongest signal
livecodebench
Score 75.4
Blended $/1M
Unlisted
No published list price
Context
128K
Provider context window (not max output)
Coding (matrix)
75.4
Model Matrix coding cell
SPEED · COST · QUALITY
Measured matrix and verified gateway observations only.
- Overall (Matrix)
- 75.4
- Matrix Coding
- 75.4
DESCRIPTION + DETAILS
PROVIDER
OpenAI
CONTEXT
128K tokens
STATUS
ACTIVE
COMMUNITY REVIEWS
Community Reviews & Field Notes
Verified EngineersReal operator evaluations, prompt robustness observations, and deployment notes for O3-Mini-2025-01-31 (Med).
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
Oct 31, 2026
Tested O3-Mini-2025-01-31 (Med) 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 27, 2026
Evaluated O3-Mini-2025-01-31 (Med) 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 | OpenAI |
| Release | |
| Input / 1M | No data yet |
| Output / 1M | No data yet |
| Context | 128K tokens |
| Cadle Cap | 72 |
| Cap board | Unranked |
| livecodebench | 75.4 |
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.
Overall75.4
Coding75.4
LiveCodeBench75.4