Published 2026-09-22 · Tested 2026-09-22
Together AI
ATogether AI received 10 PASS votes and passed 5 of five agent surface checks. The clearest finding came from the find the exact limits task.
Three AI models, GPT 5.6 Sol, Claude Opus 5, and DeepSeek v4 Flash, each read Together AI’s public documentation independently and attempted five first-hour developer jobs: make the first chat completion, find the exact limits, recover from a 429, authenticate from zero, use the Python SDK.
No accounts, API calls, or code execution were used. Every verdict came from public pages and every published quotation passed a live verification check. Preflight substitutions: T4: API key authentication end to end replaces webhooks, because Together AI's documentation describes no webhook interface.
Freshness
How rechecks work- Category
- AI models & inference
- Tested
- Quotes verified
- Surface rechecked
- Not yet rechecked
93.8% · 75/80 · AI Agent Readiness Score · reading 30 pts · surface 50 pts
Agent surface checks · 50/50
The Reading Test
| Task | GPT 5.6 Sol | Opus 5 | DeepSeek v4F | Consensus |
|---|---|---|---|---|
| Make the first chat completion | PARTIAL | PASS | PASS | PASS |
| Find the exact limits | PARTIAL | PARTIAL | PARTIAL | PARTIAL |
| Recover from a 429 | PASS | PASS | PASS | PASS |
| Authenticate from zero | PARTIAL | PASS | PASS | PASS |
| Use the Python SDK | PASS | PASS | PASS | PASS |
docs platform: Mintlify (unscored) · verified 2026-09-22
What to fix first
This fix could add up to 3 points to the AI Agent Readiness Score. The list ranks each fix by the points it would add. How the ranking works
- 1
Found: The serverless rate limits page publishes no numeric per-model limits, and it says successful responses carry no rate-limit headers while the billing page says every request returns them.
Fix: Correct the billing page to match the rate limits page on when rate-limit headers appear, and document how a caller reads their current dynamic limit.
Evidence: docs.together.ai/
docs/ serverless/ rate-limits (opens in a new tab)
What the docs get right
- Recover from a 429: 3 PASS votes. The rate-limit guide ties serverless 429s to exceeding a dynamic request or token rate and prescribes the reset interval, steadier traffic, and exponential backoff.
- Use the Python SDK: 3 PASS votes. The quickstart identifies the official Python package, installs it, and uses the same model and messages fields as its cURL REST example and API reference.
- 5 of 5 agent surface checks. Present: llms.txt, llms-full.txt, markdown mirrors, an MCP server, docs AI.
Make the first chat completion
PASSPASS consensus from 2 PASS, 1 PARTIAL.
The quickstart covers signup, key creation, SDK setup, and chat, but the required $5 credit purchase appears only on the credits page. One numbered path covers key creation, SDK install, and a runnable chat completion in Python, TypeScript, and cURL against MiniMaxAI/MiniMax-M3. One numbered sequence (create key, install SDK with pip together, run code) reaches a working chat completion in Python, TypeScript, and cURL against MiniMaxAI/MiniMax-M3.
Find the exact limits
PARTIALPARTIAL consensus from 3 PARTIAL.
The docs retire plan tiers and describe dynamic factors, but one page promises limit headers on every request while another says successful responses omit them. The rate limits page explains the dynamic mechanism but states no numeric values, and it contradicts the billing page about whether rate-limit headers are returned on every request. The two pages that describe rate-limit reporting directly contradict each other: the billing page says every response carries headers, but the rate limits page says success responses have none.
Recover from a 429
PASSPASS consensus from 3 PASS.
The rate-limit guide ties serverless 429s to exceeding a dynamic request or token rate and prescribes the reset interval, steadier traffic, and exponential backoff. The error codes table names the cause and the rate limits page gives the named error types, the retry header, exponential backoff, and the batch, provisioned, and dedicated alternatives. The error codes table names the cause, and the rate limits page gives the named subtypes, the x-ratelimit-reset retry header, exponential backoff guidance, and the fallback alternatives (batch inference, provisioned throughput, dedicated endpoints).
Authenticate from zero
PASSPASS consensus from 2 PASS, 1 PARTIAL.
Creation and bearer authentication are explicit, but standard project-key rotation or revocation has no step-by-step procedure. One page carries creation steps, the Authorization Bearer request form, expiration presets, revocation, and legacy key regeneration. The authentication page covers creation steps, the Authorization Bearer request form, expiration-date presets, immediate revocation, legacy-key regeneration, and best-practice rotation advice.
Use the Python SDK
PASSPASS consensus from 3 PASS.
The quickstart identifies the official Python package, installs it, and uses the same model and messages fields as its cURL REST example and API reference. The Python example in the API reference uses the same model, messages array, and Bearer key as the cURL sample on the same page, so the SDK and REST forms agree. The Python SDK example (from together import Together; client.chat.completions.create) uses the same model string, messages shape, and Bearer auth as the cURL variant on the same page, so the SDK and REST forms are consistent.
The receipt
Dynamic rate limits adjust with usage, so there are no fixed per-model limits published.
The docs retire plan tiers and describe dynamic factors, but one page promises limit headers on every request while another says successful responses omit them.
Agent surface notes
Initialize returned JSON-RPC protocol 2025-06-18 and serverInfo name Together AI docs.
Clicking the docs Toggle assistant panel control opened a Mintlify assistant with an "Ask a question..." input.
Show the score
Paste this into a readme:
[](https://docsforagents.com/reports/together-ai-docs-ai-agent-readiness/) Method note
This is a reading test of public documentation, not an execution test. No accounts were created and no API calls were run. The AI Agent Readiness Score counts fifteen reading votes at PASS 2, PARTIAL 1, and FAIL 0, for 30 possible points. Five agent surface checks add 10 points each. The total is 80. Consensus chips show each row majority and do not affect scoring. The panel split on 2 of five tasks. Quotes shown here were re-fetched and confirmed verbatim on 2026-09-22.
Put another docs site through the battery.
Nominate a docs site