IndexTeam/Index-Translate-omni-35B

Index-Translate-Omni 35BA3B

Built on the Index-Translate family, with multilingual text translation, terminology and formatting constraints, image translation, speech recognition, and bidirectional Chinese–English speech translation.

Text Translation

Model	FLORES COMET22 ↑	WMT24++ COMET22 ↑	WMT26 Judge ↑	MEME ↑
Index-Translate 2B	0.8655	0.8489	60.26	0.6443
Index-Translate-Omni 2B	0.8655	0.8489	60.26	0.6443
Index-Translate 9B	0.8789	0.8601	75.35	0.7387
Index-Translate-Omni 9B	0.8789	0.8601	75.35	0.7387
Index-Translate 35BA3B (preview)	0.8794	0.8586	76.76	0.7405
Index-Translate-Omni 35BA3B	0.8795	0.8593	78.01	0.7405

Instruction Following

Model	instTrans Quality / IFscore ↑	instTrans_minor Quality / IFscore ↑ / Off-target ↓	IFMTBench XCOMET-XXL / IFscore ↑
Index-Translate 2B	0.5391 / 0.7569	0.3050 / 0.6586 / 3.97%	0.7712 / 0.7584
Index-Translate-Omni 2B	0.5391 / 0.7569	0.3050 / 0.6586 / 3.97%	0.7712 / 0.7584
Index-Translate 9B	0.6771 / 0.8209	0.5222 / 0.7725 / 3.47%	0.7957 / 0.8760
Index-Translate-Omni 9B	0.6771 / 0.8209	0.5222 / 0.7725 / 3.47%	0.7957 / 0.8760
Index-Translate 35BA3B (preview)	0.6901 / 0.8336	0.5151 / 0.7715 / 4.05%	0.7926 / 0.8991
Index-Translate-Omni 35BA3B	0.6867 / 0.8371	0.5163 / 0.7843 / 3.62%	0.7934 / 0.9070

Original Index-Translate figures come from the published benchmarks. The original 35BA3B reference is the published preview checkpoint.

Speech

CER is a percentage (lower is better). BLEU and chrF2 are higher-is-better. Translation cells show BLEU / chrF2 / target CER.

Model	Chinese ASR CER ↓	English ASR CER ↓	English → Chinese	Chinese → English
Index-Translate-Omni 2B	13.8113	34.2560	36.982 / 30.357 / 43.3452	30.747 / 51.152 / 48.0670
Index-Translate-Omni 9B	17.8917	14.4097	43.507 / 36.312 / 41.3767	34.656 / 55.376 / 46.3720
Index-Translate-Omni 35BA3B	13.0053	15.4950	40.074 / 32.862 / 44.8097	32.411 / 53.452 / 46.1700

35BA3B uses one shared audio encoder and connector for all four tasks, with repetition penalty 1.1 for English ASR and the original decoding for the other tasks. Results use the same 30 fixed samples per task. One English → Chinese case reached the 256-token limit with no target translation and remains in the score. Sample counts differ across model sizes.

Image Translation

COMET22 × 100 (higher is better). Each Omni model is compared with its Qwen3.5 baseline on paired evaluated subsets.

Model	MMTIT ↑	FLORES-rendered ↑	Vistra ↑
Qwen3.5-2B	52.164	50.187	55.326
Index-Translate-Omni 2B	65.809	53.612	65.596
Qwen3.5-9B	73.814	67.804	68.963
Index-Translate-Omni 9B	77.604	68.438	71.815
Qwen3.5-35B-A3B	75.602	70.005	71.067
Index-Translate-Omni 35BA3B	77.011	69.891	71.482

Image scores are retained from the evaluated image configurations and cover the evaluated subsets.

Usage

See the native inference guide.