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).