DeepSeek-V4-Flash-Vision-Exp

POST /v1/chat/completions model: DeepSeek-V4-Flash-Vision-Exp

What this is

A vision tower bolted onto the DeepSeek-V4-Flash weights; the Exp in the name is experimental. It is the only model on this endpoint that has both a one-million-token context and image input.

The language side shares its architecture with the text model, field for field. The table below is read from the config.json of the weights this endpoint actually loads.

Architecture	DeepseekV4ForCausalLM, model_type = deepseek_v4
Layers	43
Hidden size	4,096
Attention	64 query heads, 1 KV head, head dim 512 (MLA)
Mixture of experts	256 routed + 1 shared, 6 activated per token, expert intermediate 2,048
Sparse index	a DSA indexer with index_n_heads / index_topk
Sliding window	128
Vocabulary	129,280
Native context	1,048,576
Quantisation	FP8 e4m3, block size [128, 128], dynamic activation scaling, ue8m0 scale format

The vision tower (same config.json, expressed as flat vision_* keys rather than a nested vision_config):

Layers / width / heads	32 layers, 1,024 wide, 16 heads
Patch size	14
Intermediate size	2,816
Downsample ratio	3
Per-image token ceiling	384
Minimum pixels	147,456
Maximum width:height ratio	8

vision_max_n_token = 384 is a hard ceiling: a larger image is compressed to 384 tokens, so very high-resolution inputs do not cost linearly more — and do not carry more detail either.

On this endpoint

At a glance

Context length	1,048,576 tokens
Input modalities	text + images
Streaming	✅
Tool calling	✅
JSON output	✅ json_object
Thinking	✅ — off by default, ask for it explicitly
Stability	experimental

Image input

How to send	an {"type":"image_url","image_url":{"url":"data:image/png;base64,…"}} part in content
Metering	usage.prompt_tokens_details.image_tokens
Per-image ceiling	384 tokens (above)

curl https://developer.amd.com.cn/radeon/api/v1/chat/completions \
  -H "Authorization: Bearer $RADEON_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "DeepSeek-V4-Flash-Vision-Exp",
    "messages": [{
      "role": "user",
      "content": [
        { "type": "image_url", "image_url": { "url": "data:image/png;base64,iVBORw0KGgo..." } },
        { "type": "text", "text": "What does this image say?" }
      ]
    }]
  }'

messages

As permissive as the text model: a system message may sit at any position, there may be more than one, and the role may be spelled either system or developer.

This differs from Qwen3.8-Flash-Next, which accepts only a single leading system. For one client driving both, follow the stricter shape.

Thinking

Off by default. With reasoning_effort omitted, reasoning_tokens comes back as 0; you have to ask for thinking explicitly.

All six reasoning_effort values are accepted (minimal, low, medium, high, xhigh, max, plus none and omission) — the most permissive model on this endpoint.

Thinking text	choices[0].message.reasoning
Token count	usage.completion_tokens_details.reasoning_tokens
Top-level alias	usage.reasoning_tokens — this model does emit it

Limits

Context window	1,048,576 tokens, counted as prompt plus output
JSON output	response_format: {"type": "json_object"}
Turning thinking on	reasoning_effort (or the equivalent reasoning.effort)