# DeepSeek V4 Flash Vision Exp Usage, Cost & Rank | OpenCode Data

> DeepSeek V4 Flash Vision Exp ranked #11 by tokens across OpenCode last week, with 0.9% of tokens over the past two months. DeepSeek V4 Flash Vision Exp costs $0.30 per 1M input tokens and $1.20 per 1M output tokens.

Experimental multimodal DeepSeek V4 Flash model for image understanding, coding, and agentic work

- Page: https://opencode.ai/data/deepseek/deepseek-v4-flash-vision-exp
- JSON: https://opencode.ai/data/deepseek/deepseek-v4-flash-vision-exp.json
- Model ID: deepseek/deepseek-v4-flash-vision-exp
- Lab: DeepSeek
- Updated: 2026-10-02T08:39:37.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 384K |
| Knowledge cutoff | - |
| Release date | 2026-08-21 |
| Input modalities | text, image |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | Yes |
| Weights | [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp) |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $0.30 | $1.20 | $0.0060 | $0.30 |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #11 |
| Tokens | 8.5T |
| Share of all tokens | 0.9% |
| Change vs previous 2 months | +100% |
| Unique users | 325K |
| Completed sessions | 918,951 |
| Average tokens per session | 9.2M |
| Average cost per session | $0.1349 |
| Total spend | $123,958 |
| Input tokens served from cache | 97.2% |
| Weekly retention | 63.6% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-21 | 74B | 4.3K | 5,490 |
| 2026-08-22 | 271B | 9.8K | 16,410 |
| 2026-08-23 | 309B | 9.8K | 15,060 |
| 2026-08-24 | 462B | 15K | 21,487 |
| 2026-08-25 | 488B | 16K | 22,267 |
| 2026-08-26 | 428B | 16K | 23,785 |
| 2026-08-27 | 389B | 14K | 21,125 |
| 2026-08-28 | 314B | 12K | 15,444 |
| 2026-08-29 | 271B | 9.3K | 12,727 |
| 2026-08-30 | 249B | 8.6K | 13,232 |
| 2026-08-31 | 354B | 13K | 19,179 |
| 2026-09-01 | 353B | 13K | 18,653 |
| 2026-09-02 | 334B | 12K | 22,357 |
| 2026-09-03 | 302B | 12K | 18,990 |
| 2026-09-04 | 223B | 11K | 20,340 |
| 2026-09-05 | 94B | 7.4K | 14,289 |
| 2026-09-06 | 92B | 6.7K | 14,706 |
| 2026-09-07 | 124B | 9.7K | 28,270 |
| 2026-09-08 | 120B | 9.4K | 29,486 |
| 2026-09-09 | 149B | 8.9K | 33,112 |
| 2026-09-10 | 204B | 8.9K | 30,472 |
| 2026-09-11 | 157B | 6.5K | 26,028 |
| 2026-09-12 | 130B | 4.4K | 19,365 |
| 2026-09-13 | 125B | 4.1K | 24,108 |
| 2026-09-14 | 141B | 5.9K | 27,111 |
| 2026-09-15 | 160B | 5.9K | 30,815 |
| 2026-09-16 | 171B | 5.7K | 24,914 |
| 2026-09-17 | 170B | 5.8K | 24,947 |
| 2026-09-18 | 154B | 5.2K | 24,334 |
| 2026-09-19 | 120B | 4.2K | 18,012 |
| 2026-09-20 | 128B | 4.2K | 18,332 |
| 2026-09-21 | 186B | 6.1K | 30,558 |
| 2026-09-22 | 175B | 5.4K | 28,697 |
| 2026-09-23 | 143B | 4.8K | 21,154 |
| 2026-09-24 | 127B | 4.3K | 25,691 |
| 2026-09-25 | 108B | 3.4K | 17,399 |
| 2026-09-26 | 91B | 3K | 14,870 |
| 2026-09-27 | 87B | 3.1K | 19,818 |
| 2026-09-28 | 131B | 4.4K | 30,532 |
| 2026-09-29 | 140B | 4.4K | 28,310 |
| 2026-09-30 | 130B | 4.1K | 19,993 |
| 2026-10-01 | 87B | 3.4K | 20,538 |
| 2026-10-02 | 23B | 1.5K | 6,544 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | China | 2.2T | 25.9% |
| 2 | United States | 928B | 10.9% |
| 3 | Brazil | 489B | 5.8% |
| 4 | Japan | 358B | 4.2% |
| 5 | Germany | 266B | 3.1% |
| 6 | Hong Kong | 247B | 2.9% |
| 7 | India | 236B | 2.8% |
| 8 | Singapore | 236B | 2.8% |
| 9 | Russia | 197B | 2.3% |
| 10 | Spain | 189B | 2.2% |
| 11 | Indonesia | 188B | 2.2% |
| 12 | Argentina | 153B | 1.8% |
| 13 | Colombia | 149B | 1.8% |
| 14 | Mexico | 144B | 1.7% |
| 15 | France | 132B | 1.5% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 7 | [longcat-2.5-preview](https://opencode.ai/data/meituan/longcat-2-5-preview.md) | Meituan | 2.6T |
| 8 | [glm-5.3-flash](https://opencode.ai/data/zhipuai/glm-5-3-flash.md) | Zhipu | 2.1T |
| 9 | [mimo-v2.5](https://opencode.ai/data/xiaomi/mimo-v2-5.md) | Xiaomi | 794B |
| 10 | [muse-spark-1.2-contributor](https://opencode.ai/data/meta/muse-spark-1-2-contributor.md) | Meta | 738B |
| 11 | [deepseek-v4-flash-vision-exp](https://opencode.ai/data/deepseek/deepseek-v4-flash-vision-exp.md) | DeepSeek | 687B |
| 12 | [gpt-6-luna](https://opencode.ai/data/openai/gpt-6-luna.md) | OpenAI | 640B |
| 13 | [deepseek-v4-pro](https://opencode.ai/data/deepseek/deepseek-v4-pro.md) | DeepSeek | 465B |
| 14 | [mimo-v2.6-pro](https://opencode.ai/data/xiaomi/mimo-v2-6-pro.md) | Xiaomi | 464B |
| 15 | [nemotron-3.5-lightning](https://opencode.ai/data/nvidia/nemotron-3-5-lightning.md) | NVIDIA | 267B |
| 16 | [ling-3.0-flash-fin](https://opencode.ai/data/inclusionai/ling-3-0-flash-fin.md) | inclusionAI | 256B |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| Terminal-Bench | 83.9 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |
| NL2Repo | 57.7 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |
| CyberGym | 75.3 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |
| DeepSWE | 59.3 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |
| Toolathlon-Verified | 75.9 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |
| DSBench-Hard | 63.6 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |
| AutomationBench | 25.7 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |
| ApexBench | 36.5 | pass@1 | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |
| Agents' Last Exam | 27.3 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |
| Chartography | 64.3 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |
| ZeroBench | 35 | pass@5 | https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp |

## Methodology

- Updates: Aggregated every hour. Days and weeks use UTC.
- Tokens: Input, output, reasoning, and cached tokens for each request.
- Users and sessions: Approximate counts of distinct users and OpenCode sessions.
- Cost: Session cost is the average cost per OpenCode session. Token prices are list prices from the OpenCode model catalog.
- Retention: The share of a model's users in one week who use it again the next week.
- Citation: Cite OpenCode Data (opencode.ai/data) with the update time shown at the top of the page.