IQuest-Coder-V1

Blog (2026-01-01)  •  Blog (2026-03-02)  •  Technical Report

IQuest-Coder-V1 Model Family Update

IQuest-Coder-V1 Model Family Update: Released 7B & 14B Family Models, 40B-Thinking and 40B-Loop-Thinking, specially optimized for tool use, CLI agents (Like Claude Code and OpenCode) & HTML/SVG generation, all with 128K context, now on Hugging Face!

7B Models

14B Models

40B Models

Sampling Parameters:

For the IQuest-Coder-V1-Instruct: We suggest using Temperature=0.6, TopP=0.85, TopK=20.

For the IQuest-Coder-V1-Thinking: We suggest using Temperature=1.0, TopP=0.95, TopK=20.

IQuest-Coder-V1 Highlights

IQuest-Coder-V1 is a new family of code large language models (LLMs) designed to advance autonomous software engineering and code intelligence. Built on the innovative code-flow multi-stage training paradigm, IQuest-Coder-V1 captures the dynamic evolution of software logic, delivering state-of-the-art performance across critical dimensions:

• Performance: Achieves leading results on SWE-Bench Verified (76.2%), BigCodeBench (49.9%), LiveCodeBench v6 (81.1%), and other major coding benchmarks, surpassing competitive models across agentic software engineering, competitive programming, and complex tool use.
• Code-Flow Training Paradigm: Moving beyond static code representations, our models learn from repository evolution patterns, commit transitions, and dynamic code transformations to understand real-world software development processes.
• Dual Specialization Paths: Bifurcated post-training delivers two specialized variants—Thinking models (utilizing reasoning-driven RL for complex problem-solving) and Instruct models (optimized for general coding assistance and instruction-following).
• Efficient Architecture: The IQuest-Coder-V1-Loop variant introduces a recurrent mechanism that optimizes the trade-off between model capacity and deployment footprint. The 7B and 14B models adopt shallow architectures for faster inference speed.
• Native Long Context: All models natively support up to 128K tokens without requiring additional scaling techniques.
• CLI Agent Integration: Demonstrates initial deployment capabilities on ClaudeCode and OpenCode platforms, with the ability to integrate into CLI-based agent workflows.
• HTML and SVG Generation: Features preliminary support for HTML and SVG code generation.
• Architectural Chain-of-Thought via Recurrent Depth: 40B-Loop-Thinking is a research-oriented, experimental model prototype designed to explore how structural chains of thought and procedural chains of thought can be combined within a single system. The model uniquely integrates structural chains of thought—realized through loop-based computation enabled by the dual-iteration LoopCoder architecture—with procedural chains of thought derived from explicit reasoning trajectories trained via reinforcement learning. Unlike standard reasoning models that rely solely on token-level chain-of-thought expansion, Loop-Thinking introduces implicit multi-step computation at the architectural level through a looped Transformer design. In this design, the second iteration refines the hidden states produced by the first iteration using a global–local attention gating mechanism. This results in a nested reasoning mechanism: the loop structure supports iterative representation refinement, while the reasoning-oriented training paradigm injects explicit problem decomposition behavior. It is important to note that this model is not intended to achieve state-of-the-art performance across benchmarks, but rather to validate the complementary roles of loop-based computation and reasoning-oriented training in shaping reasoning structures, and to provide experimental evidence for future model design.

Model Overview

The IQuest-Coder-V1 series includes models ranging from 7B to 40B parameters, with both standard and Loop variants:

Architecture Features:

• Grouped Query Attention (GQA) for efficient inference
• Native 128K context length support
• Vocabulary size: 76,800 tokens
• Loop variants use recurrent transformer design with shared parameters across two iterations

For more details, please refer to our Technical Report, GitHub.

Quickstart

IQuest-Coder-V1 uses custom modeling code via Hugging Face's auto_map feature. We recommend using transformers>=4.52.4.

Basic Usage with Transformers

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Using Thinking Models

For complex reasoning tasks, use the Thinking variant:

model_name = "IQuestLab/IQuest-Coder-V1-40B-Thinking"

# The Thinking model includes explicit reasoning traces
# Use similar code as above, but expect longer, more detailed responses
# with step-by-step problem decomposition

Deployment with vLLM

For production deployment, you can use vLLM to create an OpenAI-compatible API endpoint. Please refer to the vLLM PR for implementation details.

vllm serve IQuestLab/IQuest-Coder-V1-40B-Instruct --tensor-parallel-size 8

For Thinking models with reasoning support:

vllm serve IQuestLab/IQuest-Coder-V1-40B-Thinking --reasoning-parser qwen3 --tensor-parallel-size 8

When using tool, IQuest-Coder-V1-40B-Instruct and IQuest-Coder-V1-40B-Loop-Instruct should use --tool-parser qwen3, while IQuest-Coder-V1-7B-Instruct, IQuest-Coder-V1-7B-Thinking, IQuest-Coder-V1-14B-Instruct, IQuest-Coder-V1-14B-Thinking, IQuest-Coder-V1-40B-Thinking and IQuest-Coder-V1-40B-Loop-Thinking should use --tool-parser qwen3_coder.

CLI-Like Agents and Tools Usage

CLI-like agent capabilities are available for the following models: IQuest-Coder-V1-7B-Instruct, IQuest-Coder-V1-7B-Thinking, IQuest-Coder-V1-14B-Instruct, IQuest-Coder-V1-14B-Thinking, IQuest-Coder-V1-40B-Thinking and IQuest-Coder-V1-40B-Loop-Thinking.

Step 1: Deploy the model with vLLM and set tool parser (Attention: Do not set reasoning parser for Instruct LLMs, otherwise it will cause unexpected errors):

vllm serve IQuestLab/IQuest-Coder-V1-7B-Instruct --tool-parser qwen3_coder

or

vllm serve IQuestLab/IQuest-Coder-V1-7B-Thinking --tool-parser qwen3_coder --reasoning-parser qwen3 

Step 2: Use Claude Code to enjoy it:

export ANTHROPIC_BASE_URL="http://iquestcoder.link"
export ANTHROPIC_AUTH_TOKEN="sk-iquestcoder"
claude --model IQuestCoder-V1-7B-Instruct

Evaluation Results

Benchmark Parameters

SWE-Bench Verified Evaluation

We provide the evaluation framework and trajectory data for reproducing our SWE-Bench Verified results in IQuest-Coder-Eval/SWE-Verified/.

The evaluation framework is based on R2E-Gym. To reproduce the evaluation:

cd IQuest-Coder-Eval/SWE-Verified/R2E-Gym

# Install dependencies
pip install -e .

# Run evaluation
bash benchmark/bench/loopcoder/loopcoder.sh

The trajectory file ./IQuest-Coder-Eval/SWE-Verified/traj.zip contains the complete agent trajectories for our SWE-Bench Verified evaluation.

Limitations

• Research Prototype: The current models are designed for research purposes. Real-world user experience may differ from state-of-the-art commercial models, with weaker instruction-following capabilities in certain scenarios.
• Long-Context Management: Due to parameter size constraints, performance on long-horizon tasks and multi-turn tool invocations is limited, particularly in scenarios requiring sustained context management and complex agentic workflows.
• Reasoning vs. Efficiency Trade-off: Thinking models provide superior reasoning but generate longer responses; Instruct models are more efficient for straightforward tasks.
• Code Execution: Models generate code but do not execute it; always validate outputs in sandboxed environments.
• Domain Specificity: While trained on diverse codebases, performance may vary on highly specialized or proprietary frameworks.
• Factuality: Models may generate plausible but incorrect code; verify critical implementations thoroughly.

Citation

If you find our work helpful, please cite:

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