# Grok 4.7 Usage, Cost & Rank | OpenCode Data

> Grok 4.7 ranked #34 by tokens across OpenCode last week, with 0% of tokens over the past two months. Grok 4.7 costs $2.00 per 1M input tokens and $6.00 per 1M output tokens.

xAI's frontier model for long-running agents, coding, knowledge work, and visual projects

- Page: https://opencode.ai/data/xai/grok-4-7
- JSON: https://opencode.ai/data/xai/grok-4-7.json
- Model ID: xai/grok-4.7
- Lab: xAI
- Updated: 2026-10-02T08:39:37.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 500K |
| Max output | 500K |
| Knowledge cutoff | 2026-05 |
| Release date | 2026-09-21 |
| Input modalities | text, image, pdf |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | No |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $2.00 | $6.00 | $0.50 | - |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #34 |
| Tokens | 19B |
| Share of all tokens | 0% |
| Change vs previous 2 months | +100% |
| Unique users | 15K |
| Completed sessions | 33,217 |
| Average tokens per session | 564K |
| Average cost per session | $0.5402 |
| Total spend | $17,943 |
| Input tokens served from cache | 82.6% |
| Weekly retention | - |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-09-21 | 1.2B | 786 | 1,585 |
| 2026-09-22 | 2.5B | 1.8K | 4,538 |
| 2026-09-23 | 2.1B | 1.6K | 3,054 |
| 2026-09-24 | 1.8B | 1.4K | 3,032 |
| 2026-09-25 | 1.6B | 1.2K | 2,375 |
| 2026-09-26 | 1.3B | 1.2K | 2,657 |
| 2026-09-27 | 1.5B | 1.2K | 2,493 |
| 2026-09-28 | 1.7B | 1.6K | 3,596 |
| 2026-09-29 | 1.6B | 1.6K | 3,350 |
| 2026-09-30 | 1.7B | 1.4K | 2,935 |
| 2026-10-01 | 1.4B | 1.3K | 2,887 |
| 2026-10-02 | 356M | 403 | 715 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 3.4B | 17.9% |
| 2 | Germany | 1.2B | 6.2% |
| 3 | Brazil | 1.2B | 6.2% |
| 4 | Japan | 1B | 5.3% |
| 5 | Spain | 800M | 4.1% |
| 6 | India | 700M | 4% |
| 7 | Canada | 600M | 3% |
| 8 | United Kingdom | 500M | 2.8% |
| 9 | Singapore | 500M | 2.6% |
| 10 | Mexico | 400M | 2.3% |
| 11 | Hong Kong | 400M | 2.2% |
| 12 | Argentina | 400M | 2.1% |
| 13 | Türkiye | 400M | 2.1% |
| 14 | France | 400M | 2% |
| 15 | Colombia | 400M | 2% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 30 | [hy3](https://opencode.ai/data/tencent/hy3.md) | Tencent | 17B |
| 31 | [minimax-m2.7](https://opencode.ai/data/minimax/minimax-m2-7.md) | MiniMax | 15B |
| 32 | [hy4-preview](https://opencode.ai/data/tencent/hy4-preview.md) | Tencent | 13B |
| 33 | [omen-alpha](https://opencode.ai/data/unknown/omen-alpha.md) | - | 12B |
| 34 | [grok-4.7](https://opencode.ai/data/xai/grok-4-7.md) | xAI | 9.6B |
| 35 | [jev-1.13](https://opencode.ai/data/unknown/jev-1-13.md) | - | 6B |
| 36 | [kimi-k2.6](https://opencode.ai/data/moonshotai/kimi-k2-6.md) | Moonshot | 5.5B |
| 37 | [grok-4.6](https://opencode.ai/data/xai/grok-4-6.md) | xAI | 2.8B |
| 38 | [glm-5.1](https://opencode.ai/data/zhipuai/glm-5-1.md) | Zhipu | 2.5B |
| 39 | [qwen3.6-plus](https://opencode.ai/data/alibaba/qwen3-6-plus.md) | Qwen | 1.9B |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| CursorBench | 46.3 | score | https://x.ai/news/grok-4-7 |
| DeepSWE | 71 | score | https://x.ai/news/grok-4-7 |
| EEBench | 64 | score | https://x.ai/news/grok-4-7 |
| AA-Briefcase | 1657 | Elo | https://x.ai/news/grok-4-7 |
| Terminal-Bench | 37.6 | score | https://x.ai/news/grok-4-7 |
| Harvey Legal Agent Benchmark | 19.6 | score | https://x.ai/news/grok-4-7 |
| HealthBench | 56.7 | score | https://x.ai/news/grok-4-7 |
| GDPval | 1695 | Elo | https://x.ai/news/grok-4-7 |

## 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.