Deploying text embedding models under resource constraints is challenged by the sequence-length-dependent computational cost of self-attention. However, existing sequence-reduction strategies remain predominantly static and offer limited flexibility across inference budgets. We propose Jasper-Flash, an inference-scalable text embedding framework combining Elastic Token Compression (ETC) with Compression-Adaptive Progressive Distillation (CAPD). ETC uses a length threshold and a user-selected compression ratio to adjust sequence length on the fly, enabling flexible latency–quality trade-offs within a single model. To mitigate the optimization difficulty introduced by lossy sequence reduction, CAPD integrates heterogeneous multi-teacher distillation with a progressive curriculum over fixed and dynamically sampled compression ratios. We instantiate Jasper-Flash as Jasper-Token-Compression-600M, a bilingual text embedding model. Experiments on MTEB and C-MTEB demonstrate competitive performance across compression ratios and substantial reductions in encoding latency, particularly for longer inputs. The code and model are available at https://huggingface.co/infgrad/Jasper-Token-Compression-600M.