[XM-Dong]

DT-Router is a proprietary router developed by Ant Digital Technologies. This submission includes 8,400 full-split results and 420 robustness routing decisions.

Prices below are USD per million tokens, based on official public pricing checked on 2026-10-29. All discounted or introductory rates are publicly advertised by the official providers.

Model	Input	Output	Official source
lingdt-3.0-flash	$0.021	$0.062	[Ant Digital MaaS](https://maas.antdigital.com/api/v1/model-service/public/page-list)
anthropic/claude-haiku-5.5	$0.10	$0.50	[Anthropic](https://platform.claude.com/docs/en/models/haiku-5-5/overview#pricing)
glm-5.3-flash	$0.15	$0.50	[Z.AI](https://docs.z.ai/guides/overview/pricing)
google/gemini-3.8-flash	$0.75	$3.75	[Google](https://ai.google.dev/gemini-api/docs/pricing#gemini-3.8-flash)

LingDT uses the official discounted rate of CNY 0.14/0.42, converted at 6.7208 CNY/USD and rounded to three decimal places. Gemini uses Google's published introductory pricing through December 31, 2026. Haiku uses the applicable ≤100K-input-token tier.

RouterArena data were not used for training. Reasoning tokens are included in output_tokens.

[XM-Dong]

/evaluate

[github-actions]

Router Evaluation Results

Router: DT-Router
Dataset Split: full

RouterArena Metrics

Metric	Value
RouterArena Score	0.7790
Accuracy	81.02%
Total Cost	$4.029790
Avg Cost per Query	$0.000480
Avg Cost per 1K Queries	$0.4797
Number of Queries	8400
Abnormal Entries	7
Robustness Score	0.7881

⚠️ 7 of 8400 queries (0.1%) had no valid generation (inference failed / empty answer) and were scored as incorrect (0). These queries still count toward the denominator, so accuracy and cost reflect the full query set. Please regenerate predictions for these queries and resubmit for a complete evaluation.

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Evaluation completed by RouterArena automated workflow

[yl231]

Thanks for the submission, @XM-Dong. We've gone through the prediction files, and they look like genuine model calls: the prompts match RouterArena's exactly, token usage tracks prompt length, and where other submissions used the same models (gemini-3.8-flash, glm-5.3-flash, claude-haiku-5.5), your answers score in line with theirs on the same queries.

Before we can review further, we need a description of the router, since neither the PR nor github.com/antgroup/DTRouter says how it works. In particular:

1. Method. What signal does the router use to pick a model for each query, and how was the policy trained or calibrated? Which datasets were used, and how were their items selected and formatted?
2. RouterArena data. The README rule covers any use of RouterArena data in building the router: its queries, labels, prompt templates, or its dataset composition (for example, sizing a calibration set to match RouterArena's per-source counts). Please confirm that none of these was used.
3. Runs on RouterArena. How many router configurations were scored on RouterArena, locally or here, before this one, and how was the submitted one chosen?
4. lingdt-3.0-flash. It handles 6,845 of the 8,400 queries. Since it's your own model, could you confirm that RouterArena's queries weren't part of its training data?

Seven rows have empty answers with zero token usage (ArcMMLU_114, ArcMMLU_158, ArcMMLU_315, GeoBench_866, MMLUPro_health_4907, QANTA_Literature_40, QANTA_History_1616). If those were provider errors, you're welcome to regenerate them once with the same model. The new price entries will be reviewed separately, together with the pending pricing update.

We'd like to hear back on these before we continue the review.

[XM-Dong]

/evaluate

[github-actions]

Router Evaluation Results

Router: DT-Router
Dataset Split: full

RouterArena Metrics

Metric	Value
RouterArena Score	0.7793
Accuracy	81.06%
Total Cost	$4.047159
Avg Cost per Query	$0.000482
Avg Cost per 1K Queries	$0.4818
Number of Queries	8400
Abnormal Entries	2
Robustness Score	0.7881

⚠️ 2 of 8400 queries (0.0%) had no valid generation (inference failed / empty answer) and were scored as incorrect (0). These queries still count toward the denominator, so accuracy and cost reflect the full query set. Please regenerate predictions for these queries and resubmit for a complete evaluation.

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Evaluation completed by RouterArena automated workflow

[XM-Dong]

@yl231 Thank you for carefully reviewing our submission and checking the prediction files. We appreciate the opportunity to clarify the method and evaluation process.

1. Routing method
   We use a draft-and-escalate approach. LingDT first generates a draft answer. The router then assesses the query and draft for task type, difficulty, and answer adequacy, and considers candidate models’ capabilities and costs to decide whether to retain the draft or escalate. Most queries retain LingDT’s draft under this capability–cost trade-off.
   External calibration data were used to characterize candidate-model capabilities and calibrate the routing prompt. The detailed prompts and decision rules remain proprietary.
2. Calibration data and RouterArena
   We used multiple external datasets, including MMLU, MMLU-Pro, LiveCodeBench, MATH, AIME, and xRouteBench, alongside internal datasets. Examples overlapping with RouterArena were excluded. These data supported capability profiling and prompt calibration, rather than fitting routing decisions against RouterArena’s per-question correctness labels.
3. Evaluation runs and submission selection
   We evaluated multiple configurations locally on RouterArena, primarily to compare candidate-model combinations. We did not inspect RouterArena’s per-question correctness or error breakdowns to adjust routing rules.
4. LingDT training data
   LingDT-3.0-Flash is an enterprise variant of the publicly available Ling-3.0-Flash, with additional improvements for agent workloads. The public base model is available through OpenRouter. Although the enterprise variant’s weights are not public, it is accessible through a public MaaS platform.
   No RouterArena queries were used in the enterprise-specific training or tuning. We cannot independently verify question-level overlap in the original base model’s pretraining corpus. This limitation on independent verification applies to all candidate models in the pool.
   We also evaluated both versions individually:
   | Model | Accuracy | Total generation cost |
   |---|---:|---:|
   | LingDT-3.0-Flash | 71.33% | $0.8236 |
   | Ling-3.0-Flash | 71.45% | $0.8765 |
   Both use the same input and output token prices. Their aggregate accuracies differ by approximately 0.12 percentage points, providing a supplementary comparison showing similar performance on RouterArena.
5. Missing answers
   We reran each of the seven missing generations once using its originally selected model. Five now have answers. GeoBench_866 and QANTA_History_1616 were again blocked by GLM’s content-safety filter. We retained these two failed generations and counted them as incorrect.
   The updated predictions are already included in the result files submitted with this PR.
6. Updated generation costs
   After these updates, accuracy is 81.0630%. Final-answer generation costs $4.047159499. Including the preceding LingDT drafts for escalated queries brings the answering-model generation cost to $4.438232758, and the Arena score will be 77.78.
   We initially reported final-answer costs based on the accounting convention we observed in similar leaderboard submissions. If you need an itemized cost breakdown covering all LingDT draft-generation calls, we would be happy to provide it.
   Thank you again for your time.

[XM-Dong]

/evaluate

[github-actions]

Router Evaluation Results

Router: DT-Router
Dataset Split: full
Evaluator: main @ 9f6ae25

RouterArena Metrics

Metric	Value
RouterArena Score	0.7821
Accuracy	81.40%
Total Cost	$4.047159
Avg Cost per Query	$0.000482
Avg Cost per 1K Queries	$0.4818
Number of Queries	8400
Abnormal Entries	2
Robustness Score	0.7881

⚠️ 2 of 8400 queries (0.0%) had no valid generation (inference failed / empty answer) and were scored as incorrect (0). These queries still count toward the denominator, so accuracy and cost reflect the full query set. Please regenerate predictions for these queries and resubmit for a complete evaluation.

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Evaluation completed by RouterArena automated workflow

[XM-Dong]

Hi @yl231 , the merge conflicts and line-ending issue have been resolved. The evaluation has also completed successfully with the updated workflow. Could you please take another look when convenient? We’re happy to provide any further details. Thank you!