DeepSeek V4 Flash Vision Exp: Pricing, Limits, and Model Comparison
A fact-checked analysis of DeepSeek V4 Flash Vision Exp covering image token costs, API limits, agent benchmarks, comparisons with Gemini 3.7 Flash and Claude Opus 4.8, and current Modelflare pricing and availability.
DeepSeek released DeepSeek V4 Flash Vision Exp on August 21, 2026. The exact API model ID is deepseek-v4-flash-vision-exp. It accepts text and images, returns text, and keeps the same token rates as V4 Flash.
That last detail is the real story. DeepSeek resizes each image before inference and caps it at 384 input tokens. This makes screenshot-heavy agent loops unusually inexpensive. It also creates the model's clearest trade-off: a large image is reduced to roughly an 800×800-pixel budget, so low cost should not be confused with full-resolution visual fidelity.
This is an experimental model, not a silent production replacement for every vision workload. The analysis below separates DeepSeek's official specifications and provider-published benchmark claims from worked cost calculations and current Modelflare availability. Volatile facts were checked on August 22, 2026.
DeepSeek V4 Flash Vision Exp at a glance
| Property | Official value |
|---|---|
| Release date | August 21, 2026 |
| Status | Experimental API model |
| Model ID | deepseek-v4-flash-vision-exp |
| Input and output | Text and image input; text output |
| Context window | 1M tokens |
| Maximum output | 384K tokens |
| Thinking | Thinking and non-thinking modes; thinking is the documented default |
| Image formats | JPEG, PNG, GIF, WebP |
| API formats | Chat Completions, Responses API, Anthropic-compatible Messages |
| Image token ceiling | 384 input tokens per image after automatic resizing |
| Concurrency limit | 2,500 |
| FIM completion | Not supported |
DeepSeek has not disclosed parameter count, active parameters, expert count, training corpus, or architecture changes for this release. Any precise number presented without a first-party source is speculation.
What actually changed from V4 Flash
DeepSeek says the vision model is on par with the official V4 Flash for pure-text agents, reasoning, and world knowledge. The change is native visual understanding: screenshots, document images, charts, and other pictures can now enter the same agent loop without a separate image-captioning proxy.
The launch table reports the following results for V4 Flash Vision Exp:
| Evaluation | DeepSeek-published score |
|---|---|
| Terminal Bench 2.1 | 83.9 |
| NL2Repo | 57.7 |
| DeepSWE | 59.3 |
| DSBench-Hard | 63.6 |
| AutomationBench, public | 25.7 |
| ApexBench, Pass@1 | 36.5 |
| Agents' Last Exam | 27.3 |
| Chartography | 64.3 |
| ZeroBench, Pass@5 | 35.0 |
DeepSeek also says multimodal-agent performance is close to Claude Opus 4.8. This is a provider statement, not an independent Modelflare test. The rows mix text-agent and vision-dependent workloads, and the launch note specifies DeepSeek Harness minimal mode, max effort, top_p=0.95, and temperature=1.0 for its public code-agent tests. Use the table to choose workloads to replay, not as a universal ranking.
Official pricing and the real image cost
DeepSeek's pricing page lists the following US-dollar rates per one million tokens. Peak hours are 01:00–04:00 and 06:00–10:00 UTC; all other hours are off-peak.
| Meter | Off-peak | Peak |
|---|---|---|
| Cached input | $0.007 | $0.014 |
| Uncached input | $0.22 | $0.44 |
| Output | $0.66 | $1.32 |
| Maximum input charge for one image at 384 tokens | $0.00008448 | $0.00016896 |
The per-image row is a calculation, not a separate product price: 384 × input rate / 1,000,000. Smaller images may use fewer tokens. Text input and generated output are billed in addition to image tokens.
Automatic resizing explains both the low price and the limit. Images below roughly 384×384 pixels are scaled up while preserving aspect ratio. Larger images are scaled down toward a total pixel count near 800×800. A 2000×2000 image and a 5000×5000 image therefore reach the same 384-token ceiling. You save money, but very small text, fine chart labels, and precise screen coordinates may disappear during resizing.
Price comparison with Gemini 3.7 Flash and Claude Opus 4.8
The three models solve different versions of the multimodal problem. The table uses each vendor's direct standard API rates and excludes tools, storage, retries, and gateway discounts.
| Model | Release status | Input scope | Context / max output | Uncached input | Output | Main trade-off |
|---|---|---|---|---|---|---|
| DeepSeek V4 Flash Vision Exp | Experimental | Text, image | 1M / 384K | $0.22 off-peak; $0.44 peak | $0.66 off-peak; $1.32 peak | Lowest rates and 384-token image cap; aggressive image resizing |
| Gemini 3.7 Flash | GA | Text, image, video, audio, PDF | 1M / 64K | $0.75 through Dec 31, 2026 | $3.75 through Dec 31, 2026 | Broader modalities and built-in tools; higher price and later price increase |
| Claude Opus 4.8 | GA | Text, image, PDF | 1M / 128K | $5.00 | $25.00 | High-resolution vision and mature computer-use work; premium cost |
For a text-only workload of 100K uncached input tokens plus 10K output tokens:
- DeepSeek costs about $0.0286 off-peak or $0.0572 peak.
- Gemini 3.7 Flash costs about $0.1125 at its introductory rate.
- Claude Opus 4.8 costs about $0.75.
At peak rates, the DeepSeek example is about 49% cheaper than Gemini; off-peak it is about 75% cheaper. It is about 92% cheaper than Claude at peak and 96% cheaper off-peak. These percentages compare invoices, not answer quality.
Image economics widen the gap, but resolution changes too. Anthropic documents 1,296 visual tokens for a 1000×1000 image on Opus 4.8, or about $0.00648 per image. DeepSeek caps any image at $0.00016896 peak and $0.00008448 off-peak. That is roughly 38× to 77× less input cost, but DeepSeek reduces the image toward an 800×800 pixel budget while Opus can retain more detail.
As a concrete batch, 100 one-megapixel images plus 100K text input and 10K output cost at most about $0.0370 off-peak or $0.0741 peak on DeepSeek. Using Anthropic's documented 1,296 tokens per one-megapixel image, the equivalent token bill is about $1.398 on Opus 4.8. This is not a quality-equivalent test; it shows why cheap visual triage and high-resolution inspection should be treated as different workloads.
Which model fits which workload
Choose DeepSeek V4 Flash Vision Exp for a controlled test when the workload consists of many screenshots, ordinary documents, charts, OCR-like extraction, or visual observations inside a tool loop, and price per successful task matters more than retaining every source pixel.
Choose Gemini 3.7 Flash for broader multimodality when one workflow must handle image, video, audio, and PDF inputs, or depends on Google's search grounding, code execution, file search, URL context, or preview Computer Use. It is GA, while the DeepSeek vision endpoint is explicitly experimental.
Choose Claude Opus 4.8 for high-resolution visual agent work when dense documents, fine UI elements, computer use, and long-running browser tasks justify a premium. Anthropic supports up to 2,576 pixels on the long edge and 4,784 visual tokens for high-resolution images; that fidelity is part of why a per-image price comparison is not apples to apples.
The practical architecture can use both tiers: a cheap first pass to classify, extract, and route images, then a high-resolution model only for ambiguous or detail-sensitive cases. Measure the extra handoff, latency, and error rate; a two-model pipeline is not automatically cheaper if most images escalate.
Image input methods and limits
DeepSeek's vision guide supports three input paths:
- inline base64 for a local image;
- a public HTTP or HTTPS URL;
- a reusable
file_idfrom the Files API.
| Boundary | Limit |
|---|---|
| Inline request body | 48 MiB |
| One base64 or URL image | 32 MiB |
| One Files API image | 64 MiB |
| Images in one request | 600 |
Total images without file_id |
64 MiB |
Total images including file_id |
200 MiB |
| Maximum image edge | 8,192 px; 4,096 px with 15 or more images |
| External URL | 8,192 characters and a 60-second download limit |
detail: "low" forces a 512×512 preprocessing step and is useful for fast, low-detail OCR or classification. high, original, and currently auto preserve the original before DeepSeek's normal resizing rule. In Chat Completions, images are allowed only in user messages. The Responses API also permits them in developer messages and tool-output items, but rejects image parts in system or assistant messages.
Minimal Responses API example through Modelflare
Modelflare now lists the exact model ID and the Responses API route. Use an API key assigned to the deepseek-stable group:
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["MODELFLARE_API_KEY"],
base_url="https://modelflare.dev/v1",
)
response = client.responses.create(
model="deepseek-v4-flash-vision-exp",
input=[
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "Read this dashboard. Return the three anomalies and the visible evidence for each.",
},
{
"type": "input_image",
"image_url": "https://example.com/dashboard.png",
"detail": "low",
},
],
}
],
)
print(response.output_text)
For a private image, use base64 or the Files API instead of a public URL. Do not place credentials, private object URLs, or personal data in logs. The Responses implementation is stateless: previous_response_id, conversations, and store are not supported. Function calling and web search are supported, while built-in computer use, code interpreter, file search, and MCP are ignored by DeepSeek's Responses-compatible endpoint.
Production checklist for an experimental vision model
- Pin the exact
deepseek-v4-flash-vision-expID; do not assumedeepseek-v4-flashis a vision alias. - Build a privacy-safe evaluation set with small text, dense tables, charts, screenshots, rotated images, and adversarial content.
- Compare
detail: "low"with the default on task success, latency, and billed input—not visual aesthetics. - Test Chat Completions, Responses, and Messages separately if your clients use more than one protocol.
- Verify structured output, tool-call correlation, streaming, cancellation, refusals, and malformed image handling.
- Use Files API IDs for repeated private images and define retention and deletion procedures.
- Record image count, dimensions, input tokens, cached tokens, output tokens, retries, latency, route, and final charge.
- Keep a GA fallback for detail-sensitive or business-critical work until the experimental model passes a real traffic threshold.
An HTTP 200 only proves that one request completed. It does not prove that a screenshot was read correctly, every tool call was authorized, or the low-cost path reduced cost per successful task.
DeepSeek V4 Flash Vision Exp pricing on Modelflare
Yes. In the later production check on August 22, 2026, Modelflare's public Models & Pricing catalog listed the exact deepseek-v4-flash-vision-exp ID in the deepseek-stable group, with OpenAI-compatible Chat Completions and Responses API routes.
The public catalog configures the official peak reference rates—$0.44 input, $1.32 output, and $0.014 cached input per million tokens—and applies a 0.9x deepseek-stable group multiplier:
| Modelflare group | Input | Cached input | Output | Maximum uncached input cost for one 384-token image |
|---|---|---|---|---|
deepseek-stable |
$0.396 / 1M | $0.0126 / 1M | $1.188 / 1M | $0.000152064 |
At these rates, 100K uncached text-input tokens plus 10K output tokens cost about $0.05148. A workload with 100 images at the 384-token ceiling, 100K text input, and 10K output costs at most about $0.06669, before retries or other requests.
Do not treat the 0.9x group price as DeepSeek's direct off-peak schedule. Modelflare applies the group multiplier to its configured reference prices; it does not automatically switch between DeepSeek's vendor peak and off-peak windows. Group access, pricing, and routes can change, so use the live pricing page and the completed request record as the current source of truth.
Bottom line
DeepSeek V4 Flash Vision Exp is compelling because it changes the economics of visual agents: image input is capped at 384 tokens and priced at the same rate as V4 Flash. For screenshot triage, document extraction, chart reading, and high-volume visual tool loops, that can make a meaningful cost difference.
The same cap is also the warning label. Aggressive resizing can erase the exact detail that computer-use and dense-document tasks need, the endpoint is experimental, and DeepSeek's launch benchmarks are provider-published. Treat it as a low-cost visual worker to evaluate—not proof that one model now replaces every multimodal stack.
Official sources and update log
Primary sources:
- DeepSeek API change log: V4 Flash Vision Exp release
- DeepSeek Vision guide
- DeepSeek models and pricing
- DeepSeek Responses API guide
- Google: Gemini 3.7 Flash model page
- Google: Gemini API pricing
- Anthropic: Claude model overview
- Anthropic: vision tokenization and image limits
Update log:
- August 22, 2026: Initial publication. Official release status, specifications, image processing, limits, protocol support, peak/off-peak pricing, competitor prices, worked calculations, and Modelflare catalog availability checked.
- Later on August 22, 2026: Updated after the exact model became available on Modelflare. Added the
deepseek-stable0.9x prices, worked Modelflare costs, supported routes, and a Modelflare Responses API example.