Qwen3.8-Flash is a multimodal mixture-of-experts model from the Qwen team at Alibaba, and the production counterpart of the open-weight Qwen3.8-Flash-Next preview that introduces the architecture intended for the Qwen4 family. The main model carries 125 billion parameters alongside a separate 51 billion parameter N-gram embedding table, while activating roughly 6 billion parameters per token. It accepts interleaved image and text input and returns text, handling 262,144 tokens of context natively with extension to 1,000,000 tokens using YaRN. The production configuration runs with the 1M context window by default and adds built-in tool support.
Four architectural changes separate it from earlier Qwen releases: hybrid attention that pairs Gated DeltaNet for history compression with Qwen Sparse Attention, which uses a lightweight indexer to select micro-blocks of context; a Gated Residual scheme; N-gram embeddings; and training with the Muon optimizer, refined around orthogonalization accuracy and the division of parameters between Muon and AdamW. Qwen reports training cost around one ninth that of Qwen3.7-Plus, with QSA attention kernels measured up to 7.6 times faster in prefill and 4.9 times faster in decode at 1M-token context. Reported scores include 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 84.5 on AndroidWorld and 95.7 on MathVision.
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Past 30 DaysVision Evals is Roboflow's ground-truth benchmark: every model runs the same real-world samples across six vision tasks, and answers are scored against ground truth.
Evals updated August 27, 2026Pricing updated August 27, 2026
Qwen3.8 Flash averages 70.3% across the six Vision Evals tasks, ranking #16 of 34 models overall.
Its weakest relative showing is Reasoning, ranking #27 of 34 at 37.8%.
At $0.0004 per sample it is the 3rd cheapest of the 34 benchmarked models, and its average inference time of 8.2s per sample makes it the 22nd fastest.
Field medians: Object Detection 54.4%, Counting 60.1%, Identification 84.4%, OCR 89.1%, Data Extraction 86.1%, Reasoning 55.3%.
| Task | Score | Field (0 to 100) | Rank | Cost / sample | Speed |
|---|---|---|---|---|---|
| Object Detection | 58.5% | #11 of 34 | $0.0007 | 12.87s | |
| Counting | 59.5% | #18 of 34 | $0.0002 | 5.47s | |
| Identification | 90.6% | #8 of 34 | $0.0001 | 5.42s | |
| OCR | 88.9% | #18 of 34 | $0.0003 | 9.15s | |
| Data Extraction | 86.6% | #14 of 34 | $0.0002 | 4.58s | |
| Reasoning (low) | 37.8% | #27 of 34 | $0.0002 | 4.67s | |
| Reasoning (high) | 68.9% | #13 of 34 | $0.0011 | 30.65s |
Overall benchmark score against estimated cost per sample. Upper-left is the sweet spot: high quality at low cost.
34 models on the current benchmark · scores and efficiency pooled across all six tasks at low effort · Qwen3.8 Flash highlighted
Qwen3.8 Flash scores from a single evaluation run · Methodology
View all Vision Evals →Qwen3.8 Flash costs $0.150 per 1M input tokens and $0.470 per 1M output tokens.
Pricing updated Aug 27, 2026
Qwen3.8 Flash ships under a custom, model-specific license rather than a standard permissive or restrictive one, so the Qwen3.8 Flash license has to be read directly. Custom model licenses range from effectively permissive to research-only.
Uncertainty around licensing can delay or stop a project, and acceptable-use policies attached to custom licenses are binding terms rather than guidance. Review them alongside the Qwen3.8 Flash license before production deployment.
If the custom terms rule out your use case, a commercial license from the rights holder is the way through. Roboflow's licensing page lists the supported models whose commercial license is included in a Roboflow plan, so it is worth checking whether Qwen3.8 Flash — or a permissively licensed alternative — fits your deployment.
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Yes. Qwen3.8 Flash accepts image input and handles OCR, data extraction, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is Identification at 90.6% (#8 of 34). You can test it on your own image in the demo above.
Yes. its transcriptions match the ground truth 88.9% on average (#18 of 34) on Vision Evals OCR. Pulling specific fields out of documents (data extraction) scores 86.6%.
Not its strength. On Vision Evals, Qwen3.8 Flash scores 58.5% mAP@50 on object detection (#11 of 34) and 59.5% exact-match accuracy on object counting. For production counting or precise localization, pairing it with a specialized detector like RF-DETR or your own trained model in a Roboflow Workflow is usually more reliable: detect the objects, then count the detections.
On our benchmark's task mix, Qwen3.8 Flash averages $0.0004 per sample at $0.15 per 1M input and $0.47 per 1M output tokens (#3 of 34 on cost), with an average speed of 8.2s per sample across the benchmark. Actual cost depends on your images and prompts.
On the overall Vision Evals ranking, Qwen3.8 Flash sits #16 of 34 at 70.3%, just behind Muse Glimmer 30B (70.8%) and just ahead of Gemini 3.5 Flash-Lite (69.7%). See the full side-by-side: Qwen3.8 Flash vs Muse Glimmer 30B.