Solving long-horizon agentic tasks requires language agents to interact with their environments over multiple steps, and reliable performance often comes from the expensive capable models available through cloud providers. Meanwhile, alternative efficient models with inferior performance but a much lower cost are emerging, including lower-tier cloud models and smaller open-weights models deployable on local hardware. This raises a natural question that remains less explored than traditional query-level routing: can capable and efficient models collaborate within a single long-horizon agentic task to substantially reduce cost while preserving task accuracy? In this paper, we systematically investigate key design choices for such collaboration through model handoffs, where one model takes over from another, focusing on handoff direction and context transfer. We find that efficient-to-capable handoffs generally underperform capable-only execution with limited or even no cost reduction, whereas capable-to-efficient handoffs at suitable stages of task progress largely preserve or even improve accuracy while substantially reducing cost. These findings suggest that a one-time, simple, well-timed handoff strategy is good enough for cost-effective agent collaboration, motivating AgentBaton, a lightweight detector that learns when to hand off from recurring patterns in task progress. Across 11 model pairs and two representative benchmarks, AgentBaton reduces API costs by 65.9% on average while largely preserving task accuracy, with accuracy gains of up to 8.8 percentage points over capable-only execution. Experiments across various reasoning-effort settings further show that AgentBaton extends the empirical accuracy-cost Pareto frontier.
Figures & tables
Figure 1: Accuracy–cost trade-offs across reasoning-effort settings on DeepSWE v1.1. Solid lines and circles denote single-model baselines. For AgentBaton , diamonds denote handoffs to Qwen 3.8 27B and squares denote handoffs to GPT-5.6 Luna; marker colors identify the source model, as listed in the legend. The green dashed line with a light green band connects the empirical Pareto frontier among evaluated AgentBaton configurations, whose effort labels are highlighted. For example, the purple labeled MAX denotes GLM-5.3 at max effort handing off to Qwen 3.8 27B at xhigh effort; the red labeled HIGH denotes Kimi K3 at high effort handing off to GPT-5.6 Luna at max effort.
Figure 2: Overview of one-time model handoff and context transfer. The receiving model inherits the workspace at handoff and continues the task. Execution history is provided either as an inspectable logfile or directly in context.
Terminal-Bench 2.1
DeepSWE v1.1
30%
75%
30%
75%
Handoff
In-context
Logfile
In-context
Logfile
In-context
Logfile
In-context
Logfile
Efficient → Capable
Qwen → GLM
83.2 / 1.32
83.2 / 1.22
68.5 / 1.19
79.8 / 1.26
51.3 / 4.04
62.8 / 2.93
38.9 / 2.61
53.1 / 1.87
Qwen → Kimi
79.8 / 2.91
84.3 / 1.58
70.8 / 3.31
79.8 / 1.30
47.8 / 7.77
62.0 / 4.45
37.2 / 5.76
50.4 / 2.52
Qwen → GPT
83.2 / 1.19
89.9 / 0.87
82.0 / 2.15
85.4 / 0.85
73.5 / 6.20
70.8 / 5.00
58.4 / 8.84
67.3 / 5.74
Table 1: Context transfer across handoff directions at 30% and 75% of the source model’s baseline trajectory. Each cell reports accuracy (%) / mean recorded API cost per task (USD) in the handoff runs. Bold marks strictly higher accuracy or lower cost between transfer methods at the same checkpoint. GPT, GLM, Kimi, and Qwen denote GPT-5.6 Sol, GLM-5.3, Kimi K3, and Qwen3.8-27B.
Figure 3: Accuracy and mean API cost across handoff progress on DeepSWE v1.1. Colors identify the capable model: GLM-5.3, Kimi K3, and GPT-5.6 Sol; the efficient model is Qwen 3.8 27B. Solid curves show handoffs using logfile transfer in (a–b); in (c–d), GLM-5.3 and Kimi K3 use in-context transfer, while GPT-5.6 Sol uses logfile transfer. Horizontal dashed lines ( ) show the corresponding capable-only baselines; denotes Qwen-only execution. Progress is relative to the source model’s completed baseline trajectory; 0% denotes receiving-model-only execution.
Figure 4: Unsuccessful and successful capable-to-efficient handoffs. Each row shows an earlier checkpoint, the capable model’s intervening progress, and the next checkpoint. Left and right boxes report separate Qwen 3.8 27B continuations. Percentages refer to the capable-only trajectory.
Capable model
Handoffs
Acc. (%)
Cost ($)
GLM-5.3
6/113
54.9
2.54
Kimi K3
0/113
64.6
4.89
GPT-5.6 Sol
113/113
62.8
3.75
Table 2: Prompt-based handoff to Qwen 3.8 27B on DeepSWE v1.1. Handoffs reports the number of tasks with a recorded handoff; cost is mean API cost/task.
Figure 5: Online detection in AgentBaton with separate, growing message histories.
DeepSWE v1.1
Terminal-Bench 2.1
Capable model
Efficient model
Handoff (%)
Acc. (%) ↑
Cost ()\downarrow$
Handoff (%)
Acc. (%) ↑
Cost ()\downarrow$
—
Qwen 3.8 27B
—
29.2
0.00
—
69.7
0.00
—
GPT-5.6 Luna
—
60.2
0.40
—
82.2
0.07
GPT-5.6 Sol
—
—
68.1
5.32
—
87.6
0.97
Qwen 3.8 27B
26.6
60.2 -11.6%
1.02 -80.8%
58.4
82.0 -6.4%
0.43 -55.7%
GPT-5.6 Luna
67.3 -1.2%
1.39 -73.9%
82.0 -6.4%
0.47 -51.5%
Table 3: AgentBaton results. We use logfile transfer when the source model is GPT-5.6 Sol or GPT-5.6 Luna, and in-context transfer for all other source models. Subscripts give relative changes from capable-only: green is better, red worse. Handoff is the mean detected progress (%), shared across receiving models for a given source. Dashes denote single-model runs.
DeepSWE v1.1
Terminal Bench 2.1
Method
Acc. (%)
Cost ($)
Acc. (%)
Cost ($)
Capable: GLM 5.3; Efficient: Qwen 3.8 27B
MTRouter
58.4
3.67
80.9
1.35
Fixed progress
59.3
0.52
78.7
0.38
Fixed turn
54.9
0.48
76.4
0.18
AgentBaton
58.4
0.49
80.9
0.27
Table 4: AgentBaton versus routing and fixed handoff baselines with Qwen 3.8 27B. Fixed thresholds use the mean progress or turn under AgentBaton for each setting. Accuracy is in percent; cost is mean API cost per task.
Appendix figures & tables3 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 6: Accuracy and mean API cost across handoff progress on Terminal-Bench 2.1. Colors identify the capable model: GLM-5.3, Kimi K3, and GPT-5.6 Sol; the efficient model is Qwen 3.8 27B. Solid curves show handoffs using logfile transfer in (a–b); in (c–d), GLM-5.3 and Kimi K3 use in-context transfer, while GPT-5.6 Sol uses logfile transfer. Horizontal dashed lines ( ) show the corresponding capable-only baselines; denotes Qwen-only execution. Progress is relative to the source model’s completed baseline trajectory; 0% denotes receiving-model-only execution.
DeepSWE v1.1
Terminal-Bench 2.1
Capable model
Efficient model
Transfer
Acc. (%)
Cost ($)
Acc. (%)
Cost ($)
GPT-5.6 Sol
Qwen 3.8 27B
In-context
54.9
1.03
85.4
0.43
Logfile
60.2
1.02
82.0
0.43
GPT-5.6 Luna
In-context
61.9
1.27
84.3
0.47
Logfile
67.3
1.39
82.0
0.47
Claude Opus 5
Qwen 3.8 27B
In-context
61.9
2.61
87.6
1.13
Appendix
Table 5: Complete AgentBaton results for both context-transfer methods. Each row reports accuracy and mean API cost per task from the same configuration.
Single model
AgentBaton → Qwen
AgentBaton → Luna
Source model
Effort
Acc.
Cost
Acc.
Cost
Acc.
Cost
GPT-5.6 Sol
low
35.4
0.65
47.8
0.20
65.5
0.60
medium
51.3
1.26
54.9
0.30
68.1
0.80
high
62.8
1.88
57.5
0.50
62.8
0.90
xhigh
71.7
2.79
59.3
0.70
69.0
1.10
max
71.7
4.84
61.9
1.00
70.8
1.40
Appendix
Table 6: Complete numerical results for the reasoning-effort sweep on DeepSWE v1.1 in Figure 1 . Effort refers to the source model. Accuracy is in percent and cost is mean API cost in USD per task. Dashes indicate configurations not evaluated in this sweep.