Private vision-language prototypes
Run image-and-text analysis inside infrastructure you control instead of sending every input to a hosted model.
Independent tool overview
Step3-VL-10B at a glance
Step3-VL-10B is StepFun's Apache-2.0-licensed, 10-billion-parameter vision-language model for image understanding, OCR, GUI grounding, spatial reasoning, math, and text generation. It targets a single 24GB GPU for BF16 inference, with an official FP8 checkpoint also available.
Visit the official Step3-VL-10B site ↗
- Parameters
- 10 billion
- License
- Apache License 2.0
- Model types
- Base, Chat, and FP8 checkpoints
- Minimum listed VRAM
- 24GB for the documented BF16 setup
- Model weights
- Approximately 20GB, plus about 4GB runtime overhead
- Release
- January 2026 by StepFun
Overview
What Step3-VL-10B is
Step3-VL-10B is an open-weight multimodal foundation model built around a 1.8B-parameter perception encoder, a Qwen3-8B decoder, and a projector that joins visual and language representations. StepFun publishes Base, Chat, and FP8 checkpoints, plus examples for Transformers, vLLM, and SGLang.
The model is most interesting to teams that want capable image-and-text reasoning under their own control. StepFun reports strong results for OCR, documents, GUI grounding, visual STEM problems, and spatial tasks, but its benchmark table is vendor-authored. The model card also discloses an evaluation-setting error affecting some Qwen3-VL comparator results, so teams should reproduce their own task-specific evaluation before treating any ranking as settled.
Use cases
Who Step3-VL-10B is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Document and OCR experiments
Evaluate extraction, question answering, charts, diagrams, and multilingual document understanding on a compact open-weight model.
GUI grounding research
Test screenshot perception, interface element grounding, and visual-agent components with a reproducible checkpoint.
Visual STEM reasoning
Explore math, geometry, physics, graph, and diagram tasks where both perception and multi-step reasoning matter.
Self-hosted multimodal APIs
Serve the model through vLLM or SGLang behind an OpenAI-compatible endpoint for controlled internal applications.
Capabilities
Core Step3-VL-10B features
Image-and-text conversation
The Chat checkpoint accepts an image and text prompt, then returns a natural-language response.
PE-lang visual encoder
A 1.8B-parameter language-optimized perception encoder processes visual input.
Qwen3-8B decoder
The language backbone is a Qwen3-8B decoder joined to the visual encoder through a learned projector.
Multi-crop image processing
The documented strategy combines a 728-by-728 global view with multiple 504-by-504 local crops.
Sequential reasoning
The standard SeRe mode uses sequential generation with a documented maximum of 64K tokens in evaluation.
Parallel coordinated reasoning
PaCoRe aggregates evidence from 16 parallel rollouts and uses substantially more test-time compute.
OCR and document understanding
The model is trained and benchmarked on text recognition, document, chart, and visual question-answering tasks.
GUI and spatial grounding
StepFun evaluates screen grounding, visual location, counting, and 2D or 3D spatial understanding.
Official deployment recipes
The model card includes Transformers inference plus vLLM and SGLang server examples.
OpenAI-compatible serving
vLLM and SGLang can expose a familiar chat-completions-style endpoint for applications.
FP8 checkpoint
StepFun publishes an official FP8 variant for teams evaluating lower-precision deployment.
Permissive code and weight license
The project is published under Apache 2.0, subject to the license's notice, attribution, patent, and warranty terms.
Process
How the Step3-VL-10B workflow works
Step 1
Define the real task
Collect representative images, languages, resolutions, prompts, expected outputs, safety cases, and latency requirements.
Step 2
Review code and license
Inspect the repository, custom modeling code, dependencies, checkpoint provenance, Apache notices, and security posture before loading.
Step 3
Start with the Chat checkpoint
Use the documented processor and deterministic settings to establish a reproducible local baseline.
Step 4
Build an application-specific evaluation
Measure extraction accuracy, grounding, hallucination, refusal behavior, latency, memory use, and cost on your own data.
Step 5
Choose an inference path
Use Transformers for direct experiments or vLLM and SGLang when an OpenAI-compatible service and higher throughput are needed.
Step 6
Add production controls
Sandbox inputs, validate outputs, restrict tools, log model and prompt versions, monitor drift, and route uncertain cases to a human.
Cost
Step3-VL-10B pricing and free plan
StepFun does not charge a license fee for downloading the Apache-2.0 model weights. Real cost comes from GPU hardware or cloud instances, storage, engineering, evaluation, monitoring, and any third-party hosting provider. No official Step3-VL-10B managed-API price was published in the reviewed project materials.
Model weights
Free download
Base, Chat, and FP8 checkpoints are available under Apache 2.0.
- No per-token fee from StepFun for self-hosted weights
- License compliance and all infrastructure costs remain the deployer's responsibility
Public demo
Free, best effort
An official Hugging Face Space provides a convenient evaluation surface when available.
- Not a production SLA
- Capacity, privacy, queueing, and availability can differ from self-hosting
Self-hosted BF16
Infrastructure cost
The documented target is at least 24GB of VRAM, such as an RTX 4090 or A100.
- Approximately 20GB of model weights plus about 4GB of runtime overhead
- Cloud cost depends on GPU, utilization, region, throughput, and operational design
Managed hosting
Provider-specific
Teams can deploy with their own cloud stack or a compatible model-hosting provider.
- Verify that the provider supports the required custom code and multimodal preprocessing
- Pricing, privacy, scaling, and region controls depend on the chosen provider
Pricing checked . Check current pricing at the source ↗
Assessment
Step3-VL-10B strengths and limitations
Where it stands out
- Open-weight multimodal model with a permissive Apache 2.0 license
- 10B size is materially more deployable than hundred-billion-parameter vision-language models
- Documented BF16 deployment fits the 24GB VRAM class
- Official Base, Chat, and FP8 checkpoints
- Strong vendor-reported results across OCR, GUI grounding, visual STEM, and spatial tasks
- Transformers, vLLM, and SGLang deployment paths are documented
- OpenAI-compatible serving reduces application-integration friction
- Local operation can support tighter data-boundary control
- Technical report describes architecture, training, and evaluation settings
- Project materials expose both standard and high-compute reasoning modes
What to consider
- StepFun's performance claims come primarily from its own report and model card rather than broad independent production testing
- The model card discloses incorrect evaluation settings for some Qwen3-VL comparator results and says corrections are in progress
- PaCoRe uses 16 parallel rollouts, so its best reported scores do not represent ordinary single-pass cost or latency
- The documented standard checkpoint currently supports BF16 inference, which still requires substantial GPU memory
- The 24GB minimum leaves little headroom once real batching, longer context, caching, and application services are added
- The official recipe loads custom remote code, which should be reviewed and pinned before execution
- Documented vLLM support uses a validated nightly build rather than only a long-lived stable release
- Images with tiny text, dense layouts, unusual scripts, occlusion, blur, or complex spatial relationships can still be misread
- The model can hallucinate visual details, OCR text, coordinates, calculations, and causal explanations
- Benchmark accuracy does not establish reliability for medical, legal, financial, safety, or autonomous-control decisions
- The model card does not provide a comprehensive safety evaluation, red-team report, or deployment policy
- Open weights shift patching, abuse prevention, monitoring, scaling, and incident response to the operator
- Self-hosting is not free after GPU, storage, bandwidth, engineering, and observability costs
- Apache 2.0 does not guarantee that model outputs are accurate, non-infringing, or safe for a particular use
- Training-data composition and all associated provenance questions are not exhaustively documented in the overview materials
- A public demo is useful for exploration but should not be treated as a private or production-grade service
- Tool calling and GUI grounding require external permissions, sandboxing, confirmation, and deterministic application controls
- Teams should pin weights, code, tokenizer, processor, and inference versions because deployment behavior can change
Compare
Step3-VL-10B alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Miscellaneous
Hunyuan Vision 1.5 Thinking
Compare Tencent's vision-reasoning model when visual thinking quality matters more than this exact 10B deployment target.
Explore Hunyuan Vision 1.5 Thinking →Consumer
Qwen3.5-Omni
Consider Qwen's broader omnimodal family when audio and video understanding are also required.
Explore Qwen3.5-Omni →Coding
Meta Llama Models
Consider Meta's model family when ecosystem breadth and established self-hosting tooling are priorities.
Explore Meta Llama Models →Consumer
Gemini 3
Consider a managed Gemini API when infrastructure operations matter less than hosted multimodal capability.
Explore Gemini 3 →Questions
Step3-VL-10B FAQs
What is Step3-VL-10B?
It is StepFun's 10-billion-parameter open-weight vision-language model for image-and-text reasoning, OCR, documents, GUI grounding, spatial tasks, math, and general conversation.
Is Step3-VL-10B open source?
StepFun publishes the repository and model checkpoints under Apache License 2.0. The more precise description is open-weight, because the released artifact is a trained model rather than a fully reproducible disclosure of every training datum and process.
How much VRAM does Step3-VL-10B need?
The official model card lists about 20GB for the weights, about 4GB of runtime overhead, and a 24GB minimum for the documented BF16 setup. Production workloads may need more headroom.
What is the difference between SeRe and PaCoRe?
SeRe is the standard sequential reasoning mode. PaCoRe aggregates 16 parallel reasoning rollouts, which can improve reported scores but materially increases compute, latency, and implementation complexity.
Can Step3-VL-10B run behind an OpenAI-compatible API?
Yes. The official model card provides vLLM and SGLang examples that expose OpenAI-compatible endpoints.
Can I use Step3-VL-10B commercially?
Apache 2.0 generally permits commercial use, modification, and distribution when its conditions are followed. Organizations should have counsel review license compliance and their specific data, output, patent, and regulatory risks.
Are StepFun's benchmark claims independently verified?
The published tables are primarily vendor-reported. The model card also notes an evaluation-setting error for some comparator results, so reproduce the tests that matter to your application.
Is Step3-VL-10B safe for autonomous GUI control?
Not by itself. Visual grounding can be wrong. Any agent needs narrow permissions, sandboxing, action validation, user confirmation for consequential steps, and detailed monitoring.
Bottom line
Our Step3-VL-10B verdict
Step3-VL-10B is a compelling compact vision-language checkpoint for teams that value self-hosting, permissive licensing, and a credible path to 24GB-GPU deployment. Its official serving recipes and broad multimodal evaluation make it practical to test. Treat the headline benchmark story as a starting hypothesis, especially given the disclosed comparator-setting correction, and approve it only after task-specific accuracy, safety, latency, and cost testing.
Visit Step3-VL-10B website ↗Similar Consumer Tools
View all Consumer AI tools →
Translate with ChatGPT
Translate with ChatGPT - Translate text, voice, or images instantly across 50+ languages
ERNIE 5.0
ERNIE 5.0 - Baidu's omni-modal, top-ranked Chinese model
TranslateGemma
TranslateGemma
.png)
Qwen3-Max-Thinking
Qwen3-Max-Thinking - Alibaba's new flagship reasoning model competitive with models like Claude 4.5 Opus, GPT 5.2-Thinking, and Gemini 3 Pro across benchmarks
AI training for the future of work.
Get access to all our AI courses, hundreds of real-world AI use cases, live expert-led workshops, an exclusive network of AI early adopters, and more.
AI Courses
Get unlimited access to all of our current & upcoming industry-specific AI courses for the duration of your subscription.
Daily Guides
To keep up with the rapid pace of AI, our team publishes AI implementation guides daily. Our library contains 300+ practical use cases to automate real-world work.
Workshops
Join weekly, live, interactive sessions with industry leaders who are at the forefront of AI for hands-on implementation guidance and exclusive insights.
Community
Network with an exclusive community of AI-first professionals who are working smarter with AI. Learn how early adopters are using AI in their work and businesses.