Grok 4.20 Reasoning Gv2
At a glance
- Price per 1M tokens (input)
- $1.25
- Price per 1M tokens (output)
- $2.50
- Context
- 1,000,000 tokens
- Free access
- yes (see below)
Refreshed daily; data verified September 25, 2026. Published September 25, 2026.
Grok 4.20 Reasoning Gv2 by xAI is designed for developers and enterprise teams building applications that require deep visual analysis combined with autonomous decision-making. It excels in tasks such as interpreting complex diagrams, extracting structured data from images, and orchestrating multi-step workflows through tool calling, where the model invokes external functions to retrieve or act on information. Unlike general-purpose models, this variant emphasizes reasoning transparency, breaking down its logic into explicit steps to improve reliability in high-stakes environments like medical imaging support or industrial inspection systems. xAI’s approach prioritizes alignment with user intent through iterative refinement of reasoning chains, reducing hallucinations in vision-language tasks.
Specifications & pricing
| Input (per 1M tokens) | $1.25 |
|---|---|
| Output (per 1M tokens) | $2.50 |
| Cache read (per 1M tokens) | $0.20 |
| Context window | 1,000,000 tokens |
| Max output | 1,000,000 tokens |
| Capabilities | images, tool calling, reasoning |
LiteLLM community dataset (MIT), verified September 25, 2026. Official xAI pricing.
What Grok 4.20 Reasoning Gv2 would cost on your workload — run it through the cost calculator →
Where to try Grok 4.20 Reasoning Gv2 for free
- xAI offers a free chat — Grok (limited free access). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
Frequently asked questions
What types of image-based tasks is this model best suited for?+
It is effective for analyzing technical schematics, medical scans, and document layouts where understanding spatial relationships and textual content within images is essential. It supports use cases like automated quality control in manufacturing or assisting radiologists by highlighting anomalies in imaging data.
How does tool calling work in this model, and why is it useful?+
Tool calling allows the model to invoke predefined functions such as databases, APIs, or calculation tools during reasoning. For example, after identifying a part number in an image, it can call an inventory system to check stock levels. This enables dynamic, actionable outputs beyond pure text or image description.
How does this model differ from other Grok variants in the line?+
While other Grok models may prioritize speed or general language fluency, this version focuses on detailed reasoning over visual inputs. It allocates more computational steps to justify conclusions, making it less suited for rapid chat but more appropriate for audit-ready analysis.
What are the current limitations users should be aware of?+
The model may struggle with low-resolution or heavily occluded images, and its reasoning depth can increase response time. It also requires clear prompt structure to effectively engage tool use, as ambiguous instructions may lead to incomplete or incorrect function calls.
Compare with others
Org chart: how to move your company onto AI
A practical map: which company roles and processes AI agents can take over, where to start, and in what order to roll it out.