DeepSeek V4 Flash Vision (Exp) Launches on the API: Same Price as V4-Flash, Now With Eyes

DeepSeek V4 Flash Vision (Exp) went live on the Deep​Seek API platform today, August 21, 2026, and the most important number in the announcement is not a benchmark — it is the price: this experimental vision model bills at exactly the same token rates as DeepSeek V4 Flash, the text-only workhorse whose pure-text capability it matches outright. That makes this the first time Deep​Seek's own API has accepted images at all, and it means the cheapest way to run a 1M-context agent just became multimodal for free.

What actually shipped

DeepSeek V4 Flash Vision (Exp) is an experimental multimodal model, accessed with the model ID deepseek-v4-flash-vision-exp. It takes text plus images in and produces text out, over a 1M-token context window with a 384K maximum output, in both thinking and non-thinking modes. It supports the Chat Completions format, Anthropic-style Messages, and the Responses format, which is the trio an agent framework needs to wire it in without a custom adapter.

On image input, Deep​Seek accepts JPEG, PNG, GIF, and WebP, passed three ways: base64 inline, an external URL, or a file uploaded through the new Files API. There is no video or audio input yet — the "vision" here is stills only.

Deep​Seek's own positioning, in its release note: pure-text capability — agents, reasoning, world knowledge — is on par with the official DeepSeek V4 Flash release, while multimodal agent capability takes a major leap and lands close to Opus-4.8. That is a vendor claim, unreproduced, and worth reading carefully, because the benchmark detail behind it is more interesting than the headline.

Why the benchmark jump is bigger than it looks

All of the following figures are Deep​Seek's own, published today in the launch note, not independently verified. On the agent benchmarks that embed screenshots and images, the text-only DeepSeek V4 Flash does not read them — it simply ignores the multimodal parts of the task. DeepSeek V4 Flash Vision (Exp) actually looks, which is why the deltas are so large:

• ApexBench Pass@1 — Vision-Exp 36.5 vs V4-Flash 26.2 (Opus-4.8 39.4)

• Agents' Last Exam — Vision-Exp 27.3 vs V4-Flash 25.2 (Opus-4.8 25.7)

• Terminal-Bench 2.1 — Vision-Exp 83.9 vs V4-Flash 82.7 (Opus-4.8 85.0)

• NL2Repo — Vision-Exp 57.7 vs V4-Flash 54.2 (Opus-4.8 69.7)

• DeepSWE — Vision-Exp 59.3 vs V4-Flash 54.4 (Opus-4.8 58.0)

• Chartography — Vision-Exp 64.3 (text-only V4-Flash cannot attempt it)

• ZeroBench Pass@5 — Vision-Exp 35.0 (text-only V4-Flash cannot attempt it)

Two of those numbers deserve a second look. On Agents' Last Exam, Vision-Exp (27.3) edges Opus-4.8 (25.7), and on DeepSWE it beats Opus-4.8 (59.3 vs 58.0) as well. That is what "close to Opus-4.8" actually rests on — not one marquee result, but a band of agentic benchmarks where seeing the screen closes most of the gap to the frontier multimodal model while underselling it on price by roughly an order of magnitude.

What an image costs, to the decimal

Images are tokenized for billing — up to 384 tokens each — and then charged at the same per-token rates as DeepSeek V4 Flash. Because Deep​Seek moved all of its models to peak/off-peak pricing on August 16, 2026, the exact numbers depend on when you call:

• Input, cache miss — $0.22 per 1M tokens off-peak, $0.44 peak

• Input, cache hit — $0.007 per 1M tokens off-peak, $0.014 peak

• Output — $0.66 per 1M tokens off-peak, $1.32 peak

• Peak hours — 01:00–04:00 and 06:00–10:00 UTC (all other hours off-peak)

Do the arithmetic and the cost of vision nearly vanishes. One image at its 384-token cap is about 0.0085 cents off-peak and 0.017 cents at peak, as input. An agent that reads 50 screenshots in a single run is adding roughly half a cent of input tokens — before the model even writes its first output token. Deep​Seek also clamps resolution before inference: the low detail level resizes to 512×512, and high/original pre-scales the image (small ones up to about 384×384, large ones down to about 800×800), so a high-resolution original is never fed to the model at its native pixel count.

The supporting cast: a free Files API and Harness 0.1.1

Two infrastructure pieces shipped alongside the model. The Files API is live and free: upload an image once, reference it by file_id, and reuse it across requests without re-sending the bytes — meaningful for a browsing agent that holds a page in context across several turns. And Deep​Seek Harness 0.1.1, released the same day, has out-of-the-box support for the new model, so the harness that benchmarks and drives Deep​Seek agentic workloads can target it immediately.

The production caveat, stated plainly

This is an experimental model. Deep​Seek labels it explicitly as such, has announced no GA date, and recommends against running it directly in production; the stated plan is to iterate on feedback and ship a stable version later. The practical consequence is that the text brain is proven — it is the same weights family that powers V4-Flash — but the vision path is new code, and nobody outside Deep​Seek has stress-tested it at scale.

That is exactly the situation where a routing layer earns its keep. On OrcaRouter, both deepseek/deepseek-v4-flash-vision-exp and deepseek/deepseek-v4-flash are live behind one API, at Deep​Seek's list price passed through with zero markup. You can point image-bearing traffic at the experimental model and, in the same configuration, set an automatic failover to a fallback the moment it errors or times out — which de-risks the whole "new code" problem. The routing DSL also lets you send only the calls that actually contain images to the vision model and keep pure-text traffic on DeepSeek V4 Flash, capturing the benchmark gains where they exist and paying nothing extra where they don't.

What to watch next

The open questions are the usual ones for an experimental launch. When does DeepSeek V4 Flash Vision (Exp) reach GA, and does the price hold? Does video or audio input follow — the model is stills-only today, which leaves the large multimodal frontier models unopposed on moving media. And do the independent evaluations (the first third-party run on ApexBench or Terminal-Bench) reproduce the published numbers? The interesting thing is that even if every benchmark drifts down, the one claim that is already cost-effective — vision at text prices — is a pricing fact, not a benchmark claim, and that does not need reproducing.