DeepSeek V4 Flash Vision Exp
Experimental multimodal DeepSeek V4 Flash model for image understanding, coding, and agentic work
- Context
- 1.0M
- Released
- 1mo ago
- Parameters
- —
- Input / 1M tokens
- $0.22
- Output / 1M tokens
- $0.65
- Cached input / 1M tokens
- $0.00686
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
No benchmark results have been matched to this model yet.
Specifications
- Developer
- DeepSeek
- Model family
- DeepSeek
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- August 21, 2026
- Model ID
deepseek/deepseek-v4-flash-vision-exp- Openness
- Open weights
- License
- MIT
- Parameters
- —
- Input modalities
- text, image
- Output modalities
- text
- Modality
- text+image->text
- Tokenizer
- DeepSeek
- Max output tokens
- 262K
- Hugging Face
- deepseek-ai/DeepSeek-V4-Flash-Vision-Exp
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- deepseek/deepseek-v4-flash-vision-exp
- Source family
- deepseek-flash
- Last updated
- 2026-09-01
- Model context
- 1M
- Model output limit
- 384K
- Open weights
- Yes
- File attachments
- Yes
- Reasoning
- Yes
- Tool calling
- Yes
- Structured output
- Yes
- Temperature control
- Yes
Downloadable weights
Published benchmarks
| Benchmark | Score | Evaluation details | Evidence |
|---|---|---|---|
| Terminal-Bench | 83.9 · score | DeepSeek Harness minimal mode · max effort · 2.1 | Source |
| NL2Repo | 57.7 · score | DeepSeek Harness minimal mode · max effort | Source |
| CyberGym | 75.3 · score | DeepSeek Harness minimal mode · max effort | Source |
| DeepSWE | 59.3 · score | DeepSeek Harness minimal mode · max effort | Source |
| Toolathlon-Verified | 75.9 · score | DeepSeek Harness minimal mode · max effort | Source |
| DSBench-Hard | 63.6 · score | DeepSeek Harness minimal mode · internal · max effort | Source |
| AutomationBench | 25.7 · score | DeepSeek Harness minimal mode · public · max effort | Source |
| ApexBench | 36.5 · pass@1 | — | Source |
| Agents' Last Exam | 27.3 · score | — | Source |
| Chartography | 64.3 · score | — | Source |
| ZeroBench | 35 · pass@5 | — | Source |
Complete source record (JSON)
{
"id": "deepseek/deepseek-v4-flash-vision-exp",
"name": "DeepSeek V4 Flash Vision Exp",
"description": "Experimental multimodal DeepSeek V4 Flash model for image understanding, coding, and agentic work",
"family": "deepseek-flash",
"last_updated": "2026-09-01",
"attachment": true,
"reasoning": true,
"tool_call": true,
"structured_output": true,
"temperature": true,
"modalities": {
"input": [
"text",
"image"
],
"output": [
"text"
]
},
"limit": {
"context": 1000000,
"output": 384000
},
"benchmarks": [
{
"name": "Terminal-Bench",
"score": 83.9,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "score",
"harness": "DeepSeek Harness minimal mode",
"variant": "max effort",
"version": "2.1"
},
{
"name": "NL2Repo",
"score": 57.7,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "score",
"harness": "DeepSeek Harness minimal mode",
"variant": "max effort"
},
{
"name": "CyberGym",
"score": 75.3,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "score",
"harness": "DeepSeek Harness minimal mode",
"variant": "max effort"
},
{
"name": "DeepSWE",
"score": 59.3,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "score",
"harness": "DeepSeek Harness minimal mode",
"variant": "max effort"
},
{
"name": "Toolathlon-Verified",
"score": 75.9,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "score",
"harness": "DeepSeek Harness minimal mode",
"variant": "max effort"
},
{
"name": "DSBench-Hard",
"score": 63.6,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "score",
"harness": "DeepSeek Harness minimal mode",
"variant": "max effort",
"dataset": "internal"
},
{
"name": "AutomationBench",
"score": 25.7,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "score",
"harness": "DeepSeek Harness minimal mode",
"variant": "max effort",
"dataset": "public"
},
{
"name": "ApexBench",
"score": 36.5,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "pass@1"
},
{
"name": "Agents' Last Exam",
"score": 27.3,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "score"
},
{
"name": "Chartography",
"score": 64.3,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "score"
},
{
"name": "ZeroBench",
"score": 35,
"source": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
"metric": "pass@5"
}
],
"release_date": "2026-08-21",
"open_weights": true,
"license": "MIT",
"weights": [
{
"label": "Hugging Face",
"url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp"
}
]
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| deepinfra/fp8 | 1.0M | $0.22 | $0.65 | $0.00686 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| gmicloud/fp8 | 1.0M | $0.44 | $1.32 | $0.01 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| siliconflow/fp8 | 1.0M | $0.44 | $1.32 | $0.03 | — | 98.92% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 |
Provider data:OpenRouter
Deployment graph
Can I run this?
Vertical Tabs only shows compatibility when it has structured evidence tying this exact model to hardware, runtime, quantization, memory use, and observed performance.