# GPT-5 Nano Usage, Cost & Rank | OpenCode Data

> GPT-5 Nano ranked #43 by tokens across OpenCode last week, with 0% of tokens over the past two months. GPT-5 Nano costs $0.05 per 1M input tokens and $0.40 per 1M output tokens.

Tiny GPT-5 lane for routing, extraction, classification, and bulk jobs

- Page: https://opencode.ai/data/openai/gpt-5-nano
- JSON: https://opencode.ai/data/openai/gpt-5-nano.json
- Model ID: openai/gpt-5-nano
- Lab: OpenAI
- Updated: 2026-10-02T08:39:37.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 400K |
| Max output | 128K |
| Knowledge cutoff | 2024-05-30 |
| Release date | 2025-08-07 |
| Input modalities | text, image |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | No |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $0.05 | $0.40 | $0.0050 | - |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #43 |
| Tokens | 7.6B |
| Share of all tokens | 0% |
| Change vs previous 2 months | +159% |
| Unique users | 16K |
| Completed sessions | 102,609 |
| Average tokens per session | 74K |
| Average cost per session | $0.0020 |
| Total spend | $209 |
| Input tokens served from cache | 73% |
| Weekly retention | - |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-11 | 69M | 264 | 0 |
| 2026-08-12 | 211M | 452 | 1 |
| 2026-08-13 | 116M | 433 | 5 |
| 2026-08-14 | 116M | 459 | 7 |
| 2026-08-15 | 77M | 326 | 7 |
| 2026-08-16 | 63M | 298 | 2 |
| 2026-08-17 | 128M | 472 | 2 |
| 2026-08-18 | 121M | 500 | 1,758 |
| 2026-08-19 | 231M | 470 | 2,853 |
| 2026-08-20 | 156M | 436 | 3,180 |
| 2026-08-21 | 160M | 421 | 3,096 |
| 2026-08-22 | 69M | 316 | 2,503 |
| 2026-08-23 | 166M | 280 | 2,391 |
| 2026-08-24 | 170M | 377 | 2,571 |
| 2026-08-25 | 144M | 393 | 3,417 |
| 2026-08-26 | 132M | 349 | 2,998 |
| 2026-08-27 | 122M | 349 | 2,478 |
| 2026-08-28 | 143M | 311 | 2,747 |
| 2026-08-29 | 149M | 221 | 2,148 |
| 2026-08-30 | 127M | 221 | 2,110 |
| 2026-08-31 | 153M | 313 | 2,344 |
| 2026-09-01 | 112M | 337 | 2,481 |
| 2026-09-02 | 152M | 349 | 2,546 |
| 2026-09-03 | 127M | 331 | 2,340 |
| 2026-09-04 | 146M | 307 | 2,472 |
| 2026-09-05 | 61M | 226 | 1,930 |
| 2026-09-06 | 63M | 207 | 1,838 |
| 2026-09-07 | 236M | 310 | 2,334 |
| 2026-09-08 | 113M | 322 | 2,623 |
| 2026-09-09 | 106M | 320 | 2,381 |
| 2026-09-10 | 216M | 328 | 2,453 |
| 2026-09-11 | 154M | 288 | 2,297 |
| 2026-09-12 | 105M | 199 | 2,838 |
| 2026-09-13 | 231M | 190 | 3,662 |
| 2026-09-14 | 277M | 265 | 3,814 |
| 2026-09-15 | 294M | 310 | 3,602 |
| 2026-09-16 | 245M | 286 | 3,791 |
| 2026-09-17 | 396M | 318 | 3,172 |
| 2026-09-18 | 328M | 320 | 2,338 |
| 2026-09-19 | 272M | 216 | 3,993 |
| 2026-09-20 | 168M | 237 | 2,346 |
| 2026-09-21 | 86M | 289 | 1,321 |
| 2026-09-22 | 186M | 298 | 1,516 |
| 2026-09-23 | 224M | 252 | 1,554 |
| 2026-09-24 | 97M | 201 | 1,229 |
| 2026-09-25 | 139M | 171 | 1,000 |
| 2026-09-26 | 34M | 132 | 595 |
| 2026-09-27 | 61M | 130 | 419 |
| 2026-09-28 | 29M | 169 | 654 |
| 2026-09-29 | 45M | 191 | 736 |
| 2026-09-30 | 80M | 153 | 784 |
| 2026-10-01 | 27M | 142 | 806 |
| 2026-10-02 | 13M | 61 | 126 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | China | 1.4B | 18.6% |
| 2 | United States | 1.2B | 15.1% |
| 3 | Japan | 800M | 10.3% |
| 4 | Canada | 700M | 9.7% |
| 5 | Brazil | 500M | 6.4% |
| 6 | Mexico | 500M | 6.4% |
| 7 | Germany | 200M | 3% |
| 8 | Ghana | 200M | 2.9% |
| 9 | Sweden | 200M | 2.5% |
| 10 | Australia | 200M | 2.4% |
| 11 | Portugal | 200M | 2% |
| 12 | Spain | 100M | 1.9% |
| 13 | Argentina | 100M | 1.7% |
| 14 | Georgia | 100M | 1.6% |
| 15 | Hong Kong | 100M | 1.5% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 39 | [qwen3.6-plus](https://opencode.ai/data/alibaba/qwen3-6-plus.md) | Qwen | 1.9B |
| 40 | [test-novita-dsf4.1](https://opencode.ai/data/deepseek/test-novita-dsf4-1.md) | DeepSeek | 986M |
| 41 | [qwen3.7-max](https://opencode.ai/data/alibaba/qwen3-7-max.md) | Qwen | 920M |
| 42 | [minimax-m2.5](https://opencode.ai/data/minimax/minimax-m2-5.md) | MiniMax | 720M |
| 43 | [gpt-5-nano](https://opencode.ai/data/openai/gpt-5-nano.md) | OpenAI | 289M |
| 44 | [test](https://opencode.ai/data/openai/test.md) | OpenAI | 218M |
| 45 | [exo](https://opencode.ai/data/unknown/exo.md) | - | 533K |
| 46 | [qwen3.8-27b](https://opencode.ai/data/alibaba/qwen3-8-27b.md) | Qwen | 62K |
| 47 | [test-novita-kimi k3](https://opencode.ai/data/moonshot/test-novita-kimi-k3.md) | Moonshot | 24K |
| 48 | [deepseek/deepseek-v4-flash](https://opencode.ai/data/deepseek/deepseek-deepseek-v4-flash.md) | DeepSeek | 16K |

## 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.