INTELLECT-3 API & Inference Endpoint

Introduction

INTELLECT-3 is a 106B (A12B) parameter Mixture-of-Experts reasoning model post-trained from GLM-4.5-Air-Base using supervised fine-tuning (SFT) followed by large-scale reinforcement learning (RL).

bench

Training was performed with prime-rl using environments built with the verifiers library. All training and evaluation environments are available on the Environments Hub.

The model, training frameworks, and environments are open-sourced under fully-permissive licenses (MIT and Apache 2.0).

For more details, see the technical report.

Evaluation

INTELLECT-3 achieves best-in-class performance on math, coding, and reasoning benchmarks:

Table with columns: Benchmark, MATH-500, AIME24, AIME25, LCB, GPQA, HLE, MMLU-Pro
Benchmark	MATH-500	AIME24	AIME25	LCB	GPQA	HLE	MMLU-Pro
INTELLECT-3	98.1	90.8	88.0	69.3	74.4	14.6	81.9
GLM-4.5-Air	97.8	84.6	82.0	61.5	73.3	13.3	73.9
GLM-4.5	97.0	85.8	83.3	64.5	77.0	14.8

Model Variants

Table with columns: Model, HuggingFace
Model	HuggingFace
INTELLECT-3	PrimeIntellect/INTELLECT-3
INTELLECT-3-FP8	PrimeIntellect/INTELLECT-3-FP8

Serving with vLLM

The BF16 version can be served on 2x H200s:

bash
vllm serve PrimeIntellect/INTELLECT-3 \
    --tensor-parallel-size 2 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --reasoning-parser deepseek_r1

The FP8 version can be served on a single H200:

bash
vllm serve PrimeIntellect/INTELLECT-3-FP8 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --reasoning-parser deepseek_r1

Citation

bibtex
@misc{intellect3,
  title={INTELLECT-3: Technical Report},
  author={Prime Intellect Team},
  year={2025},
  url={https://huggingface.co/PrimeIntellect/INTELLECT-3}
}

Introduction

bench

Training was performed with prime-rl using environments built with the verifiers library. All training and evaluation environments are available on the Environments Hub.

The model, training frameworks, and environments are open-sourced under fully-permissive licenses (MIT and Apache 2.0).

For more details, see the technical report.

Evaluation

INTELLECT-3 achieves best-in-class performance on math, coding, and reasoning benchmarks:

Table with columns: Benchmark, MATH-500, AIME24, AIME25, LCB, GPQA, HLE, MMLU-Pro
Benchmark	MATH-500	AIME24	AIME25	LCB	GPQA	HLE	MMLU-Pro
INTELLECT-3	98.1	90.8	88.0	69.3	74.4	14.6	81.9
GLM-4.5-Air	97.8	84.6	82.0	61.5	73.3	13.3	73.9
GLM-4.5	97.0	85.8	83.3	64.5	77.0	14.8

Model Variants

Table with columns: Model, HuggingFace
Model	HuggingFace
INTELLECT-3	PrimeIntellect/INTELLECT-3
INTELLECT-3-FP8	PrimeIntellect/INTELLECT-3-FP8

Serving with vLLM

The BF16 version can be served on 2x H200s:

bash
vllm serve PrimeIntellect/INTELLECT-3 \
    --tensor-parallel-size 2 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --reasoning-parser deepseek_r1

The FP8 version can be served on a single H200:

bash
vllm serve PrimeIntellect/INTELLECT-3-FP8 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --reasoning-parser deepseek_r1

Citation

bibtex
@misc{intellect3,
  title={INTELLECT-3: Technical Report},
  author={Prime Intellect Team},
  year={2025},
  url={https://huggingface.co/PrimeIntellect/INTELLECT-3}
}

INTELLECT-3

README

Introduction

Evaluation

Model Variants

Serving with vLLM

Citation

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README

Introduction

Evaluation

Model Variants

Serving with vLLM

Citation