LongCat-Flash-Lite-Sparse
Meituan · 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
17 matched benchmark rows with their published value, unit, and provenance.
Knowledge
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MMLU Massive Multitask Language Understanding | 85.31 | 57 subjects | Elementary to professional level | Measuring Massive Multitask Language Understanding |
| GPQA-D GPQA Diamond | 69.5 | Graduate-level science questions | Graduate level | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| MMLU-Pro Massive Multitask Language Understanding Professional | 79.24 | Multiple subjects | Professional level | MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark |
| C-Eval C-Eval | 85.76 | Chinese academic and professional exams | High school to professional level | C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models |
| CMMLU Chinese Massive Multitask Language Understanding | 84.3 | Chinese academic QA | Broad Chinese knowledge | DeepSeek-V4 Technical Report |
Coding
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.0 Terminal-Bench 2.0 | 33.7 | Terminal-based software tasks | Professional software engineering | Terminal-Bench 2.0 |
| SWE-bench Verified Software Engineering Benchmark Verified | 68.2 | 500 verified issues | Professional software engineering | SWE-bench: Can Language Models Resolve Real-World GitHub Issues? |
| SWE-bench Pro SWE-bench Pro | 40.63 | 1,865 repository problems | Long-horizon professional engineering | SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? |
| SWE Multilingual SWE Multilingual | 59.33 | Multilingual software-engineering tasks | Professional software engineering | MiniMax M2.7: Early Echoes of Self-Evolution |
Mathematics
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MATH-500 MATH-500 Problem Set | 95.8 | 500 problems | High school to undergraduate | Measuring Mathematical Problem Solving With the MATH Dataset |
| AIME26 AIME 2026 | 65.7 | Competition math problems | Olympiad-style mathematics | Qwen3.6 launch benchmarks |
| HMMT Feb 2026 Harvard-MIT Mathematics Tournament February 2026 | 40.5 | Competition math problems | Olympiad-style mathematics | Qwen3.6 launch benchmarks |
| IMOAnswerBench IMOAnswerBench | 49.4 | Advanced mathematical answer generation | Olympiad-level mathematics | DeepSeek-V4 Technical Report |
Agentic
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.0 Terminal-Bench 2.0 | 33.7 | Terminal-based software tasks | Professional software engineering | Terminal-Bench 2.0 |
| BrowseComp BrowseComp | 48.62 | Research questions requiring browsing | Hard web research | BrowseComp |
| MCP Atlas MCP Atlas | 45.6 | Tool-integrated agent tasks | Advanced tool use | Introducing GPT-5.4 mini and nano |
| VITA-Bench VITA-Bench | 21.7 | Interactive consumer-service agent tasks | Long-horizon real-world workflows | VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications |
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.