Individual skill
together-fireworks
Use when calling open-weight LLMs on Together AI or Fireworks AI's OpenAI-compatible endpoints — `base_url` plus namespaced model id, the cheapest model that clears the bar, per-1M-token cost math, serverless vs batch vs dedicated. NOT renting GPUs to self-host weights (that is `runpod`), NOT running a model locally for free (that is `ollama`).
skills/together-fireworks/SKILL.md
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- License
- MIT
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- ai-coding Repository topic
- claude-code Repository topic
- cli Repository topic
- cline Repository topic
- codex Repository topic
- coding-assistant Repository topic
- cursor Repository topic
- llm Declared skill tag
- inference Declared skill tag
- together-ai Declared skill tag
- fireworks-ai Declared skill tag
- openai-compatible Declared skill tag
- open-models Declared skill tag
- batch Declared skill tag
- cost Declared skill tag
- engineering Derived category
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SKILL.mdcore · 13,404 bytesSKILL.mdcore · 13,404 bytesSKILL.mdcore · 13,404 bytesevals/README.mddoc · 1,056 bytesevals/cases.yamlasset · 3,518 bytesreferences/batch-and-tuning.mddoc · 3,565 bytesreferences/models-and-pricing.mddoc · 6,933 bytesscripts/verify.shruns · 6,470 bytes
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# together-fireworks
You run open-weight LLMs on two neutral, OpenAI-compatible hosts: **Together AI** and **Fireworks AI**. Neither trains the flagship models — they host open weights (Llama, DeepSeek, Qwen, GPT-OSS, Kimi, Mistral) behind a billed-per-token endpoint. No GPU to provision, no server to babysit. You pay for tokens.
Both speak the OpenAI wire protocol. So the entire mental model is: **same SDK, change three things — `base_url`, `api_key`, and the model id.** That is why these two providers live in one skill: the working knowledge (connect, pick a model, batch for 50% off, do the cost math) is ~90% shared. The only real differences are the base URL string and the model-id naming scheme. Learn both at once.
This skill is *where the tokens come from*. Designing the prompt is `../prompt-engineering/SKILL.md`; chaining calls into a workflow is `../llm-pipeline/SKILL.md`; tracking spend across many providers as a discipline is `../cost-tracking/SKILL.md`. Here you only do the per-model math and the endpoint plumbing.
## The two endpoints (memorize these)
| Provider | `base_url` | Model id shape | Examples (illustrative — confirm on the serverless catalog) |
|---|---|---|---|
| Together | `https://api.together.ai/v1` | `<vendor>/<model>` | `openai/gpt-oss-20b`, `meta-llama/Llama-3.3-70B-Instruct-Turbo`, `deepseek-ai/DeepSeek-V4-Pro` |
| Fireworks | `https://api.fireworks.ai/inference/v1` | `accounts/fireworks/models/<name>` | `accounts/fireworks/models/gpt-oss-20b`, `accounts/fireworks/models/llama-v3p1-8b-instruct` |