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Deploy to Cloudflare Pages / deploy (push) Failing after 23s
- Task matrix (12 task families x 23 Go models) from Artificial Analysis evals - Price/value scatter and value ranking - Model guide: what each model is targeted for (agentic, coding, data analysis, image understanding, research, free tier) with pricing and caveats - MODELS.md markdown version, README, MIT license - wrangler.toml + Gitea Actions workflow deploying site/ to Cloudflare Pages
95 lines
3.8 KiB
Markdown
95 lines
3.8 KiB
Markdown
# OpenCodeGoEvals
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Benchmarks and plain-language notes for every model in the **OpenCode Go** catalog:
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a task-by-task performance matrix, cost/value analysis, and a description of what each
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model is actually targeted for (agentic work, coding, data analysis, image understanding,
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deep research, high-volume chores).
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Static site, no build step — it is plain HTML/CSS/ES modules, deployed to **Cloudflare Pages**.
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## What's here
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| Path | What it is |
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| `site/index.html` | The site: performance matrix, price/value charts, model guide |
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| `site/assets/data.js` | Model catalogue — pricing, context, modality, target descriptions, caveats |
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| `site/assets/evals.js` | Benchmark scores (0–100) per model per task family |
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| `site/assets/app.js` | Rendering logic for the matrix, charts and guide |
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| `site/assets/style.css` | Styling |
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| `MODELS.md` | The same model descriptions in Markdown |
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| `wrangler.toml` | Cloudflare Pages project configuration |
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| `.gitea/workflows/pages.yml` | CI: builds and deploys to Cloudflare Pages on every push to `main` |
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## Data sources
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- [OpenCode Go docs](https://opencode.ai/docs/go) — pricing, monthly allowances, privacy/retention, client support
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- [OpenCode Data](https://opencode.ai/data) — catalog metadata, context windows, usage and retention stats
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- [Artificial Analysis](https://artificialanalysis.ai) — benchmark scores and the Intelligence Index
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- Vendor announcements — Google (Gemini 4 Argon), Reflection AI (Beam), Meituan (LongCat),
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MiniMax (M3.1 Flash), xAI (Grok 4.7), StepFun (Step 5), Qwen, Zhipu, Moonshot, DeepSeek, Xiaomi, Tencent
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Data snapshot date: **2026-10-21**. Re-run the collection script to refresh.
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## Publishing to Cloudflare Pages
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### 1. Create the Pages project
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```bash
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npm install
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npx wrangler login # or export CLOUDFLARE_API_TOKEN
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npx wrangler pages project create opencodogoevals
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```
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### 2. Give CI the credentials
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Add these as repository secrets (Gitea: *Settings → Actions → Secrets*):
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| Secret | Value |
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| `CLOUDFLARE_API_TOKEN` | Cloudflare API token with **Cloudflare Pages: Edit** (and Account Settings: Read) |
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| `CLOUDFLARE_ACCOUNT_ID` | The Cloudflare account that owns the Pages project |
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The workflow in `.gitea/workflows/pages.yml` then deploys `site/` on every push to `main`.
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### 3. Deploy manually (optional)
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```bash
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npm run deploy # wrangler pages deploy site --project-name opencodogoevals
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npm run dev # local preview on http://localhost:8788
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```
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### Custom domain
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Set it in Cloudflare Pages → *Custom domains*, then add a CNAME record pointing at
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`<project>.pages.dev`.
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## Local development
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The site is static — open `site/index.html` directly, or use `npm run dev` for a
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Cloudflare-compatible local server.
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## Updating the data
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1. Benchmark scores come from each model's Artificial Analysis page
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(`https://artificialanalysis.ai/models/<slug>`) — the embedded JSON payload in the page
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contains the full evaluation set per model.
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2. Catalog facts (pricing, context window, modalities, release date) come from
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`https://opencode.ai/data/<lab>/<model>.json` and the Go docs.
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3. Edit `site/assets/evals.js` and `site/assets/data.js`, then regenerate `MODELS.md`
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from `data.js` and re-commit.
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## Caveats
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- Task-matrix cells use the best published evaluation per task family; when a model has
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not been run on a benchmark the column falls back to another one, so the underlying
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benchmark can differ between rows in a column. Hover a cell to see which one it is.
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- Free-tier models (MiniMax M3.1 Flash Free, Beam Free, LongCat 2.5 Preview Free) are
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limited-time offers.
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- Contributor-tier models (Gemini 4 Argon, Muse Spark 1.3) may use your prompts and
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completions for training — check the Go docs' privacy table.
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- No Go model generates images; they are all text-output.
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## License
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MIT — see [LICENSE](LICENSE).
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