DeepSeek’s first vision model vs. Gemini 3.7 Flash: It comes down to spend vs. speed

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DeepSeek released V4 Flash Vision Exp on August 21, its first model that accepts image input. Image input means a model can understand a chart, screenshot, or photo document in the same way it can with text. 

DeepSeek V4 Flash Vision Exp reached API gateways like OpenRouter on August 27. It adds image understanding to the company’s budget V4 Flash model. It keeps the same low price of $0.22 per million input tokens and $0.66 per million output tokens. The price doubles during weekday peak hours. 

Google’s Gemini 3.7 Flash, released August 13, is the obvious comparison. It is the budget vision workhorse most developers default to, billed at $0.75 and $3.75 per million on OpenRouter.

DeepSeek pitches the model for document and chart understanding, as well as visual question answering. Google calls Gemini 3.7 Flash its “most intelligent workhorse model yet“. With both making strong claims, I wanted to know which one is better to use for image input.

The tests

I ran both models through three image tests that imitate real back-office work:

Chart reading – a stacked bar chart with a cost line plotted on a second y-axis using a different scale, so the answer cannot be read by eyeballing where the line crosses the bars.

Invoice audit – a vendor invoice with three planted errors: a line total that doesn’t match quantity times price, a subtotal that matches nothing, and a due date before the invoice date.

Incident diagnosis – forty lines of production logs where a payment service crash sits at the bottom, but the real cause, a batch job exhausting the database connection …

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