Benchmark avg
39.0
12 tracked scores
ActiveModel portfolio
gpt-4.5-preview-2025-02-27
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OpenAI · Closed · Context 128K tokens
Cadle Score
138.4
#149 in index
Benchmark average
45.6
Not enough benchmark results yet
Pricing
Unlisted
no list price
MODEL MATRIX · TRUTH CENTER CELLS
Overall
—
Intelligence
—
Coding
44.9
Reasoning
41.0
Language
69.8
Knowledge
—
Agentic
17.5
MMLU
—
GPQA
60.7
Math
58.1
LiveCodeBench
—
HumanEval
—
Reason
—
ARC-AGI
45.1
Coverage AVG
45.6
Pillar Coverage:5 / 7 Core Pillars (71%)
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
Strongest signal
math-level-5
Score 78.6
Blended $/1M
Unlisted
No published list price
Context
128K
Provider context window (not max output)
Coding (matrix)
44.9
Model Matrix coding cell
Agentic
17.5
Tool-use / agentic matrix cell
SPEED · COST · QUALITY
Measured matrix and verified gateway observations only.
- Cadle Score
- 138.4
- Matrix Coding
- 44.9
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 gpt-4.5-preview-2025-02-27.
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 gpt-4.5-preview-2025-02-27 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 gpt-4.5-preview-2025-02-27 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 | 138 |
| Cap board | Cadle Language Cap 138 |
| arc-agi-2 | 0.8 |
| fiction-livebench | 63.9 |
| arc-agi | 10.3 |
| vpct | 17.5 |
| lech-mazur-writing | 75.6 |
| aider-polyglot | 44.9 |
| simplebench | 21.4 |
| cybench | 17.5 |
| weirdml | 39.4 |
| otis-mock-aime-2024-2025 | 37.7 |
| math-level-5 | 78.6 |
| gpqa-diamond | 60.7 |
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.
Coding44.9
Agentic17.5
GPQA60.7
Math58.1
ARC-AGI45.1