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1. Introduction

We're introducing GRM-3.2-Cliff, our intermediate model built for long-horizon agentic tasks and extremely difficult reasoning problems in local environments. GRM-3.2-Cliff marks a substantial leap in long-horizon task capability over its predecessor, GRM-2.5-Plus, and is designed to serve as a dependable engine for complex, multi-step local workflows.

The model is purpose-built for long-horizon agentic tasks and problems that are simply hard — difficult coding challenges, advanced mathematics, and rigorous logical reasoning. GRM-3.2-Cliff aims to sustain coherent, goal-directed behavior over extended interactions while remaining optimized for resource-constrained hardware, making it ideal for developers and researchers who need local execution without sacrificing multi-step planning and self-correction performance.

2. Key Capabilities

Long-Horizon Agentic Mastery: GRM-3.2-Cliff is specifically optimized to maintain coherence, planning quality, and task fidelity across long, multi-step agentic workflows, representing a major upgrade over GRM-2.5-Plus.
Local Workflow Efficiency: Engineered to run smoothly in lower GPU environments while delivering high-tier reasoning performance.
Elite Reasoning on Hard Problems: Strong performance on difficult coding, advanced mathematics, and logical reasoning tasks with careful, structured step-by-step problem-solving.
Robust Coding Ability: Handles complex, multi-file coding tasks, debugging, refactoring, and long-running terminal sessions locally.
Consistent Logical Reasoning: Built to reason carefully through multi-constraint logic problems without losing track of intermediate steps over extended execution runs.

3. Performance

GRM-3.2-Cliff is designed as our premier mid-sized model for local, long-horizon agentic work. It builds directly on the strengths of GRM-2.5-Plus while targeting common edge-case failures in smaller models — contextual drift, multi-step degradation, and loss of initial goal states — delivering strong reliability across extended sessions.

Detailed Benchmarks

	GRM-3.2-Cliff	GRM-2.5-Plus	GPT-5.6-Luna	Sonnet 5	Gemini 3 Pro
Knowledge & STEM
MMLU-Pro	83.3	84.2	—	—	89.8
GPQA Diamond	82.4	82.7	92.3	—	91.9
Reasoning & Coding
LiveCodeBench v6	69.3	67.2	—	—	82.9
General Agent
SWE-bench Verified	70.3	—	—	85.2	76.2
SWE-bench Pro	43.4	—	62.7	63.2	—
Terminal-Bench 2.1	45.3	—	84.7	80.4	—
NL2Repo	28.5	—	—	—	—

Scores are taken from each provider's own published model card, blog post, or system card where available; "—" indicates a score was not publicly reported by that provider at the time of writing. Different labs may use different agent scaffolds when reporting SWE-bench and Terminal-Bench results, so cross-provider comparisons should be read with that caveat.

4. Family

The GRM-3.2 family is available in various sizes to suit every use case.

Model	Size	Domain
GRM-3.2-Sky	35B-A3B	Flagship model for long-horizon tasks
GRM-3.2-Cliff	9B	Capable model for low GPU environments
GRM-3.2-Turf	1.2B	Lightweight model for practical reasoning

5. Architecture

GRM-3.2-Cliff is built on the Ornith-1.0-9B architecture, a 9B-parameter model optimized for long-horizon agentic workflows, complex coding tasks, advanced mathematics, and logical reasoning, structured to run efficiently in low-to-mid GPU hardware environments.

GRM-3.2-Cliff is developed by OrionLLM and released under the Apache 2.0 License.