In this report, we introduce the IQuest-Coder-V1 series-(7B/14B/40B/40B-Loop), a new family of code large language models (LLMs). Moving beyond static code representations, we propose the code-flow multi-stage training paradigm, which captures the dynamic evolution of software logic through different phases of the pipeline. Our models are developed through the evolutionary pipeline, starting with the initial pre-training consisting of code facts, repository, and completion data. Following that, we implement a specialized mid-training stage that integrates reasoning and agentic trajectories in 32k-context and repository-scale in 128k-context to forge deep logical foundations. The models are then finalized with post-training of specialized coding capabilities, which is bifurcated into two specialized paths: the thinking path (utilizing reasoning-driven RL) and the instruct path (optimized for general assistance). IQuest-Coder-V1 achieves state-of-the-art performance among competitive models across critical dimensions of code intelligence: agentic software engineering, competitive programming, and complex tool use. To address deployment constraints, the IQuest-Coder-V1-Loop variant introduces a recurrent mechanism designed to optimize the trade-off between model capacity and deployment footprint, offering an architecturally enhanced path for efficacy-efficiency trade-off. We believe the release of the IQuest-Coder-V1 series, including the complete white-box chain of checkpoints from pre-training bases to the final thinking and instruction models, will advance research in autonomous code intelligence and real-world agentic systems.
Figures & tables
Figure 1: IQuest-Coder-V1 performance across different benchmarks. The score of LiveCodeBench v6 is from IQuest-Coder-V1-40B-Loop-Thinking model, and the rest are IQuest-Coder-V1-40B-Loop-Instruct model. The orange dash line represents the average score of the selected models.
Figure 2: Code-Flow Training pipeline of IQuest-Coder-V1.
Model Size
Layers
Hidden Size
Intermediate Size
Attention
Max Context
Query Heads
KV Heads
Vocabulary
Base Models (Stage 1)
IQuest-Coder-V1-7B-Base-Stage1
14
5120
27648
GQA
131072
40
8
76800
IQuest-Coder-V1-14B-Base-Stage1
28
5120
27648
GQA
131072
40
8
76800
IQuest-Coder-V1-40B-Base-Stage1
80
5120
27648
GQA
131072
40
8
76800
Base Models (Stage 2)
IQuest-Coder-V1-7B-Base
14
5120
27648
GQA
131072
40
8
76800
Table 1: Architecture of IQuest-Coder-V1.
Model
Python
Java
TypeScript
C#
Average
EM
ES
EM
ES
EM
ES
EM
ES
EM
ES
6B+ Models
DeepSeek-Coder-6.7B-Base
41.1
79.2
39.9
80.1
46.3
82.4
55.0
86.9
45.6
82.1
DS-Coder-V2-Lite-Base
41.8
78.3
46.1
81.2
44.6
81.4
58.7
87.9
47.8
82.2
CodeQwen1.5-7B
40.7
77.8
47.0
81.6
45.8
82.2
59.7
87.6
48.3
82.3
Qwen2.5-Coder-7B
42.4
78.6
48.1
82.6
46.8
83.4
59.7
87.9
49.3
83.1
Table 2: Performance comparison on CrossCodeEval Tasks.
Model
EvalPlus
BigCodeBench
FullStackBench
HumanEval
HumanEval+
MBPP
MBPP+
Full
Hard
6B+ Models
DeepSeek-Coder-V2-Lite-Instruct
81.1
75.6
85.2
70.6
37.8
18.9
49.4
Qwen2.5-Coder-7B-Instruct
87.2
81.7
84.7
72.2
37.8
13.5
42.2
Seed-Coder-8B-Instruct
81.1
75.6
86.2
73.3
44.6
23.6
55.8
IQuest-Coder-V1-7B-Instruct
79.9
73.2
73.5
63.5
38.9
23.0
39.7
Table 3: Performance comparison on code generation tasks.
Model
CruxEval
LiveCodeBench
Input-COT
Output-COT
V5
V6
6B+ Models
DeepSeek-Coder-V2-Lite-Instruct
57.1
56.2
13.2
19.4
Qwen2.5-Coder-7B-Instruct
66.9
66.0
14.4
18.9
Seed-Coder-8B-Instruct
62.0
66.6
19.2
22.3
IQuest-Coder-V1-7B-Instruct
45.8
54.2
24.6
24.6
Table 4: Performance comparison on Code Reasoning Evaluation.
Figure 3: IQuest-Coder-V1 performance across different benchmarks.
Model
Mercury
Beyond@1
Pass@1
6B+ Models
DeepSeek-Coder-V2-Lite-Instruct
76.8
91.4
Qwen2.5-Coder-7B-Instruct
69.9
84.8
Seed-Coder-8B-Instruct
78.5
93.8
IQuest-Coder-V1-7B-Instruct
42.1
50.4
Table 5: Performance comparison on code efficiency task.
Model
Bird
Spider
Execution Accuracy
Execution Accuracy
6B+ Models
DeepSeek-Coder-V2-Lite-Instruct
41.6
72.4
Qwen2.5-Coder-7B-Instruct
53.1
79.8
Seed-Coder-8B-Instruct
44.7
72.7
IQuest-Coder-V1-7B-Instruct
37.7
67.7
Table 6: Performance comparison on Text2SQL Tasks.
Model
Agentic Coding
General Tool Use
Terminal-Bench
Terminal-Bench (2.0)
SWE-Verified
Mind2Web
BFCL V3
6B+ Models
DeepSeek-Coder-V2-Lite-Instruct
5.0
0.0
-
26.7
-
Qwen2.5-Coder-7B-Instruct
6.3
0.0
-
38.4
54.2
Seed-Coder-8B-Instruct
7.5
2.5
-
38.2
-
IQuest-Coder-V1-7B-Instruct
22.5
11.2
45.0
40.5
34.0
Table 7: Combined performance on agentic coding tasks (Terminal-Bench, Terminal-Bench 2.0, SWE-Verified) and general tool-use tasks (Mind2Web, BFCL V3).
We present Qwen3-Coder-Next, an open-weight language model specialized for coding agents. Qwen3-Coder-Next is an 80-billion-parameter model that activates only 3 billion parameters during inference, enabling strong coding capability with efficient inference. In this work, we explore how far strong training recipes can push the capability limits of models with small parameter footprints. To achieve this, we perform agentic training through large-scale synthesis of verifiable coding tasks paired with executable environments, allowing learning directly from environment feedback via mid-training and reinforcement learning. Across agent-centric benchmarks including SWE-Bench and Terminal-Bench, Qwen3-Coder-Next achieves competitive performance relative to its active parameter count. We release both base and instruction-tuned open-weight versions to support research and real-world coding agent development.
Agentic repository-level code understanding is essential for automating complex software engineering tasks, yet the field lacks reliable benchmarks. Existing evaluations often overlook the long tail topics and rely on popular repositories where Large Language Models (LLMs) can cheat via memorized knowledge. To address this, we introduce SWE-QA-Pro, a benchmark constructed from diverse, long-tail repositories with executable environments. We enforce topical balance via issue-driven clustering to cover under-represented task types and apply a rigorous difficulty calibration process: questions solvable by direct-answer baselines are filtered out. This results in a dataset where agentic workflows significantly outperform direct answering (e.g., a ~13-point gap for Claude Sonnet 4.5), confirming the necessity of agentic codebase exploration. Furthermore, to tackle the scarcity of training data for such complex behaviors, we propose a scalable synthetic data pipeline that powers a two-stage training recipe: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning from AI Feedback (RLAIF). This approach allows small open models to learn efficient tool usage and reasoning. Empirically, a Qwen3-8B model trained with our recipe surpasses GPT-4o by 2.3 points on SWE-QA-Pro and substantially narrows the gap to state-of-the-art proprietary models, demonstrating both the validity of our evaluation and the effectiveness of our agentic training workflow.
Songcheng Cai, Zhiheng Lyu, Yuansheng Ni +13
University of Waterloo · University of Toronto · The Hong Kong University of Science and Technology +2
Composer 2 is a specialized model designed for agentic software engineering. The model demonstrates strong long-term planning and coding intelligence while maintaining the ability to efficiently solve problems for interactive use. The model is trained in two phases: first, continued pretraining to improve the model's knowledge and latent coding ability, followed by large-scale reinforcement learning to improve end-to-end coding performance through stronger reasoning, accurate multi-step execution, and coherence on long-horizon realistic coding problems. We develop infrastructure to support training in the same Cursor harness that is used by the deployed model, with equivalent tools and structure, and use environments that match real problems closely. To measure the ability of the model on increasingly difficult tasks, we introduce a benchmark derived from real software engineering problems in large codebases including our own. Composer 2 is a frontier-level coding model and demonstrates a process for training strong domain-specialized models. On our CursorBench evaluations the model achieves a major improvement in accuracy compared to previous Composer models (61.3). On public benchmarks the model scores 61.7 on Terminal-Bench and 73.7 on SWE-bench Multilingual in our harness, comparable to state-of-the-art systems.