Atria Dawn Preview
Shanghai Artificial Intelligence Laboratory · Open Weight · bench-align-v5
Self-hostedCapability shape
Seven axes from the ranking source. A missing axis is drawn as a gap.
Runtime service evidence
Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim.
Time to first token: latency to the first answer chunk (Artificial Analysis, via BenchLM). Reasoning models include thinking time, so values can run to tens or hundreds of seconds.
Evidence key: ObservedUnavailable
| Measurement | Value | Observed | Last good | Evidence |
|---|---|---|---|---|
| Time to first token | Unavailable | Unavailable | Unavailable | |
| Throughput | Unavailable | Unavailable | Unavailable |
Regional or per-endpoint measurements appear only when the API supplies them; none are modelled here.
Endpoint and price matrix
Every published price component, including cache reads and writes.
Workload-aware monthly cost example
10 conversations per day × 8 messages × 22 active days, 1200 input and 400 output tokens per message, no cache. This uses the same calculator as the cost simulator, so an unavailable applicable rate makes the example unavailable too.
Benchmark record
13 matched benchmark rows with their published value, unit, and provenance.
Coding
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.1 Terminal-Bench 2.1 (provider run) | 78.3 | Terminal-based software-agent tasks | Professional software engineering | DeepSeek V4 Flash 0731 update |
| SWE-bench Pro SWE-bench Pro | 59.6 | 1,865 repository problems | Long-horizon professional engineering | SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? |
Agentic
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.1 Terminal-Bench 2.1 (provider run) | 78.3 | Terminal-based software-agent tasks | Professional software engineering | DeepSeek V4 Flash 0731 update |
| BrowseComp BrowseComp | 92.5 | Research questions requiring browsing | Hard web research | BrowseComp |
| GDPval-AA GDPval-AA | 1583 | Agentic real-world work tasks | Professional agentic workflows | DeepSeek-V4 Technical Report |
| CyberGym CyberGym | 86.5 | 1,507 vulnerability analysis instances | Real-world cybersecurity | CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale |
| JobBench JobBench | 50.3 | 130 tasks across 35 occupations | Professional multi-source workflows | JobBench: Aligning Agent Work With Human Will |
| AutomationBench AutomationBench | 53.8 | 600 public automation tasks | Long-horizon automation | Kimi K3: Open Frontier Intelligence |
| DeepSearchQA DeepSearchQA | 96.0 | Agentic browsing and list-answer questions | Agentic web research | Muse Spark Eval Methodology |
| BFCL v4 Berkeley Function Calling Leaderboard v4 | 77.0 | Function-calling tasks | Advanced tool use | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| MLE-Bench Lite MLE-Bench Lite | 86.2 | Low-resource ML competitions | Agentic machine learning | MiniMax M2.7: Early Echoes of Self-Evolution |
| τ³-bench results τ³-Bench Tool-Agent-User Evaluation | 41.2 | Corrected customer-service tasks plus knowledge and voice evaluation modes | Long-horizon, multimodal, and knowledge-aware tool use | Official τ³-bench repository and release notes |
| WideResearch WideResearch | 81.9 | Open-ended research tasks | Broad research-agent workflows | Qwen3.6 launch benchmarks |
Lifecycle and limitations log
Lifecycle events the source associates with this model, plus what this profile does not claim.
That is not evidence the model has no lifecycle plan — only that this source published none.