real-api-pricing README

The project's README from github.com/Fei2411/real-api-pricing. As one markdown file: README.md.

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Compare models, prices and allowances · English / 中文

English | 中文

Real API Pricing

Real unit price = monthly subscription fee ÷ monthly usable tokens.

Full adopted data is shown first, followed by one Pareto chart per leaderboard. Monthly figures default to four weeks of saturated use; vendor-defined monthly pools remain as defined (Kimi's monthly pool is 5× its weekly pool). Input, output and cache tokens are all included. Prices use a logarithmic axis, with cheaper points farther right.

Dollar/credit pools and three-part token prices are converted with one project-wide standard workload: 97.5% cache reads, 2.15% fresh input, and 0.35% output. This is a comparison convention, not a claim about any provider's actual workload. Measurements that already report total tokens—dashboard back-calculations, local usage logs, controlled saturation tests, and official absolute-token tables—are not normalized again. Where only total tokens and a cost-weighted percentage are available but the token-type split is unknown, the observed total is retained and the limitation is recorded rather than inventing a split. Cache writes are not modeled separately; where a provider charges for them, converted token allowances may be overstated. See conventions and the token-mix audit.

GLM Coding Plan is recomputed from Zhipu's official weekly credits and cache/input/output coefficients under the same standard workload. Peak, midpoint and off-peak scenarios are shown separately instead of copying the official 95%-cache example table. A Caijing saturation-cost test and community evidence are consistent in scale, but there is still no fully specified independent V3 Pro/Max saturation test. See the official-table archive and community-evidence review.

Each chart uses scores from its named leaderboard only. Code Arena here specifically means the WebDev Overall Arena Score, not general coding ability. OpenDesign Arena uses the 0–100 average task score (requirements 30 + design quality 70); its cost/speed-weighted recommendation score is not used. GPT-5.6 Luna now uses a ChatGPT Plus dashboard measurement: 112.67 million total tokens consumed about 6% of the weekly allowance, giving 7.511 billion tokens/month for Plus. The 5x and 20x plans are scaled from that measured Plus baseline, so the rightmost Luna point is 150.222 billion tokens/month at medium confidence rather than the superseded 240.24 billion Sol-credit derivation. Claude Max's 15.7 billion-token estimate applies to the permanent terms from September 14, 2026, not a promotional ceiling. Chinese charts use 100-million-token units: 77.37 in Chinese equals 7.737 billion in English.

All charts: English / 中文, SVG / PNG · English files · 中文文件

Data snapshot

AA Intelligence now uses Intelligence Index v4.3 (announced September 7, 2026); AA Coding Agent remains v1.4. The new intelligence methodology replaces the old snapshot as a whole: lower numerical scores are not evidence of model regression across index versions. All configurations within the selected snapshot are retained, including explicitly marked AA estimates. Historical evidence stays in data/research/.

Snapshot: 2026-09-09. Each row is one plan × actual served model; allowances of different models under the same plan are alternatives and must not be added together.

Coverage Rows
All adopted plan × model points 188
Subscription points with monthly allowance 175
Metered API baselines 13
OpenCode Go / Command Code GOAT / Ollama models 27 / 37 / 20
Code Arena / Agent Arena scored points 136 / 140
AA Intelligence / AA Coding Agent scored points 165 / 71
OpenDesign Arena scored points 65

Download the data: adopted values (CSV) · computed points (CSV) · computed points (JSON) · data notes and score coverage · dated evidence

Monthly allowance overview

The 175 subscription plan × model points are split by adopted USD monthly fee so GitHub can show them without packing every bar into one chart: $0–30 inclusive, >$30 and ≤$100, >$100–$300. Each band ranks monthly usable tokens independently. The undivided chart and hybrid-scale view stay in the chart index.

$0–30

English SVG · 中文 SVG · English PNG · 中文 PNG

Monthly allowance $0–30

Table: English TXT · 中文 TXT

>$30–$100

English SVG · 中文 SVG · English PNG · 中文 PNG

Monthly allowance >$30–$100

Table: English TXT · 中文 TXT

>$100 and ≤$300

English SVG · 中文 SVG · English PNG · 中文 PNG

Monthly allowance >$100 and ≤$300

Table: English TXT · 中文 TXT

Real unit price overview

All 188 subscription and API points on one comparable $/MTok scale.

English SVG · 中文 SVG · English PNG · 中文 PNG

Real unit price overview

Full table: English TXT · 中文 TXT

Pareto charts by leaderboard

Using Real API Pricing as a new baseline, we plot each leaderboard's scores on the Y-axis to redraw its Pareto frontier; the connected line represents that frontier. Subscriptions and metered APIs follow the same dominance rule and both participate in frontier selection.

Code Arena

English SVG · 中文 SVG · English PNG · 中文 PNG

Code Arena

Agent Arena

English SVG · 中文 SVG · English PNG · 中文 PNG

Agent Arena

AA Intelligence

English SVG · 中文 SVG · English PNG · 中文 PNG

AA Intelligence

AA Coding Agent

English SVG · 中文 SVG · English PNG · 中文 PNG

AA Coding Agent

OpenDesign Arena

English SVG · 中文 SVG · English PNG · 中文 PNG

OpenDesign Arena

OpenDesign's full 13-model quality ranking is archived. Eleven exact model identities map to current adopted points; GPT-6 Astra and Claude Fable 5.1 remain archive-only because this project has no exact adopted row for them. DeepSeek V4.1 Flash uses the official USD list price effective September 10: $0.003 cached input / $0.15 uncached input / $0.60 output off-peak, with a separate 2× peak point. The scores are OpenDesign Harness references, not measurements of each subscription/API channel.

AA Coding Agent scores describe tested harness × model × effort configurations. Static charts and points.* are explicitly highest archived configuration reference summaries. They are not measurements of each subscription/API channel; quota-measurement effort and product harness alignment remain unverified. Higher effort does not automatically change $/MTok; it can change tokens consumed per task.

All-configuration interactive view (Chinese) defaults to every archived configuration and offers the highest-score summary as an option. Download the HTML and open it locally with network access for Plotly. All configurations currently use reference mappings, not a verified product-configuration frontier.

The configuration archive (JSON) / CSV retains all 217 records, original labels, known harness/effort, source score intervals, and source task-cost records. The plan-to-configuration mappings (JSON) / CSV contains 933 explicit references, including lower-effort variants. Composer Standard/Fast require their own mode; a missing mode stays unscored. Unknown harnesses, efforts and intervals stay null.

Source mean and median task costs are separate fields, not subscription task costs. Score intervals are preserved and available in interactive hover details, but uncertainty does not yet change frontier membership. Numerical quota ranges, robust-frontier analysis and workload sensitivity remain follow-up work; qualitative confidence labels are not numerical error bars.

Method and reproduction

Build instructions · Data documentation · Sources and attribution

License and acknowledgements

Original software: MIT. Data references include Awesome Coding Plan (CC BY 4.0) and the Caijing article 《Token经济,中国账本》. See SOURCES.md for attribution, changes and third-party terms.

real-api-pricing (Fei2411/real-api-pricing)

Searching Database…

这是什么?这是一个数据与图表项目:用月费除以实际可用的 token 数,算出 AI 订阅方案的真实价格,再把这些真实单价与编程/智能体排行榜得分对照,绘制成帕累托前沿图。
这是需要安装的应用,还是只是一组图表和数据?它是一个静态网站,外加可下载的图表和数据表——你可以浏览交互式网站,或直接获取 SVG/PNG 图表和 CSV/JSON 数据,无需安装任何软件。
具体该怎么用它?打开交互式网站来对比模型、价格和额度,或者直接从仓库下载中英文 SVG 和 PNG 图表以及 CSV/JSON 数据表。
想自己重新生成图表需要准备什么?重新生成图表要用一个 Node 脚本(node scripts/render_svg.cjs),它依赖 'sharp' 包;仓库里也包含 Python 工具,但只是查看已发布的图表则什么都不需要。
这些价格对比有多准确?许多数字并非官方数据,而是在统一的工作负载假设下换算出来的(以缓存读取为主,输入/输出占比很小),部分排行榜得分也只是参考性的评测框架配置,并非经过核实的逐渠道实测。
每张帕累托图实际展示的是什么?每张图都把某个排行榜的得分与真实的 $/MTok 价格对照,并画出支配前沿线,订阅方案和按量计费的 API 在同一条前沿上竞争。
对比了哪些排行榜?Code Arena(WebDev Overall Arena Score)、Agent Arena、AA Intelligence、AA Coding Agent 和 OpenDesign Arena 各自都有对应的帕累托图。
数据集覆盖了多少方案和模型?最新快照(2026-09-09)涵盖 188 个已采用的方案×模型数据点,其中包括 175 个带月度额度的订阅方案点和 13 个按量计费 API 基准点。
为什么不直接报订阅费,而要统一换算成 token 价格?各家厂商的定价方式差别很大(美元/点数池、分级 token 费率、按周池与按月池等),因此在统一的工作负载假设下把它们都归一化为每百万 token 美元数,才能让各方案直接可比。
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Fei2411
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Updated
CommitOct 16
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Commits · 90 Days