# DeepSeek V4 Pro 0813 Usage, Cost & Rank | OpenCode Data

> DeepSeek V4 Pro 0813 had 0% of tokens across OpenCode over the past two months. DeepSeek V4 Pro 0813 costs $1.32 per 1M input tokens and $3.96 per 1M output tokens.

DeepSeek V4 Pro snapshot with million-token context and support for thinking and non-thinking modes

- Page: https://opencode.ai/data/deepseek/deepseek-v4-pro-0813
- JSON: https://opencode.ai/data/deepseek/deepseek-v4-pro-0813.json
- Model ID: deepseek/deepseek-v4-pro-0813
- Lab: DeepSeek
- Updated: 2026-09-28T12:09:39.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 384K |
| Knowledge cutoff | - |
| Release date | 2026-08-12 |
| Input modalities | text |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | Yes |
| Weights | [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813) |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $1.32 | $3.96 | $0.04 | - |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | Unranked |
| Tokens | 72M |
| Share of all tokens | 0% |
| Change vs previous 2 months | +100% |
| Unique users | 26 |
| Completed sessions | 422 |
| Average tokens per session | 171K |
| Average cost per session | $0.0000 |
| Total spend | $0 |
| Input tokens served from cache | 78.1% |
| Weekly retention | - |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-09-23 | 72M | 26 | 422 |
| 2026-09-24 | 0 | 0 | 0 |
| 2026-09-25 | 0 | 0 | 0 |
| 2026-09-26 | 0 | 0 | 0 |
| 2026-09-27 | 0 | 0 | 0 |
| 2026-09-28 | 0 | 0 | 0 |
| 2026-09-29 | 0 | 0 | 0 |
| 2025-09-30 | 0 | 0 | 0 |
| 2025-10-01 | 0 | 0 | 0 |
| 2025-10-02 | 0 | 0 | 0 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | India | 0 | 18.7% |
| 2 | China | 0 | 15.5% |
| 3 | Russia | 0 | 7.2% |
| 4 | United States | 0 | 6.9% |
| 5 | Bangladesh | 0 | 6.4% |
| 6 | Indonesia | 0 | 5% |
| 7 | Australia | 0 | 3.1% |
| 8 | Germany | 0 | 3% |
| 9 | Nepal | 0 | 2.9% |
| 10 | Malawi | 0 | 2.6% |
| 11 | Poland | 0 | 2.5% |
| 12 | Canada | 0 | 2.4% |
| 13 | Azerbaijan | 0 | 2.2% |
| 14 | Japan | 0 | 2% |
| 15 | Singapore | 0 | 1.9% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 1 | [space-bunny](https://opencode.ai/data/unknown/space-bunny.md) | - | 57T |
| 2 | [deepseek-v4.1-flash](https://opencode.ai/data/deepseek/deepseek-v4-1-flash.md) | DeepSeek | 33T |
| 3 | [muse-spark-1.3-contributor](https://opencode.ai/data/meta/muse-spark-1-3-contributor.md) | Meta | 31T |
| 4 | [mimo-v2.6-flash](https://opencode.ai/data/xiaomi/mimo-v2-6-flash.md) | Xiaomi | 8.8T |
| 5 | [deepseek-v4-flash](https://opencode.ai/data/deepseek/deepseek-v4-flash.md) | DeepSeek | 5.7T |
| 6 | [nemotron-3-ultra](https://opencode.ai/data/nvidia/nemotron-3-ultra.md) | NVIDIA | 3.6T |
| 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 |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| Humanity's Last Exam | 42.7 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |
| Humanity's Last Exam | 60 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |
| Terminal-Bench | 87.9 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |
| NL2Repo | 61.5 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |
| CyberGym | 83.3 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |
| DeepSWE | 62.7 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |
| Toolathlon-Verified | 74.1 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |
| Agents' Last Exam | 25.7 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |
| AutomationBench | 31.8 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |
| DSBench-FullStack | 71.1 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |
| DSBench-Hard | 67.2 | score | https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813 |

## 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.