Nemotron 3.5 Super VL vs SambaCloud

Nemotron 3.5 Super VL

NVIDIA's free endpoint takes OpenAI-format requests with typed tool calls, JSON Schema output and an OpenAPI 3.1 file, and the weights run anywhere under a permissive licence. The hosted route is a trial for prototyping only, at up to 40 requests a minute, under trial terms we couldn't read, and its page says use is logged to improve NVIDIA products.

SambaCloud

Per-token prices and the model list are readable without a key at /v1/models, and a free tier needs no card. Production models get two to three weeks' notice before removal, 13 model ids left between March and June 2026, and the docs disagree with the live catalogue on MiniMax-M2.7.

Nemotron 3.5 Super VL

1. Set chat_template_kwargs to {"enable_thinking": false} for short tasks. Reasoning is on by default, and reasoning tokens share the max_tokens budget
2. Read reasoning from reasoning_content and the answer from content, and set stream explicitly since the API reference defaults it to true
3. Don't send confidential or personal data to the hosted trial. The model page asks callers not to and says use is logged
4. Back off and retry on throttling. Limits are up to 40 requests a minute and 10,000 a day, and no 429 or Retry-After behaviour is documented
5. For production, use a partner endpoint or serve the weights. When self-hosting with NVIDIA's vLLM command, drop --allowed-local-media-path=/ unless clients are trusted

SambaCloud

1. Call GET https://api.sambanova.ai/v1/models at start-up and use only ids it returns. The docs name models the endpoint no longer lists
2. Read x-ratelimit-remaining-requests and x-ratelimit-remaining-requests-day on every response. The free tier allows 20 requests a day per model
3. Treat 429 queue_full and 503 maintenance as retryable after a delay, and 410 model_deprecated as a signal to change model
4. Validate JSON output yourself. Schema enforcement is best effort and strict: true changes nothing
5. Check max_completion_tokens per model. DeepSeek-V3.1 caps output at 7,168 tokens and Llama 3.3 70B at 3,072