The model
Inferam currently serves DeepSeek-V4.1-Flash-1017, an open-weights model, on Inferam’s own hardware. The served model can change; /v1/models always has the current ID.
| Model ID | DeepSeek-V4.1-Flash-1017 |
| Context window | 524,288 tokens (input plus output) |
| Default output limit | 32,768 tokens |
| Maximum output limit | 65,536 tokens |
| Tool calling | Yes, including parallel calls |
| Structured output | json_object and json_schema |
| Image input | Yes, as base64 data |
| Prompt caching | Automatic |
Reasoning
Section titled “Reasoning”The model thinks before answering by default, and its reasoning is returned separately from the answer. Each format has its own way to set the effort:
| Format | Parameter | Values |
|---|---|---|
| Chat Completions | reasoning_effort |
none, minimal, low, medium, high, xhigh, max |
| Responses | reasoning.effort |
the same |
| Messages | thinking, output_config.effort |
{"type": "disabled"} turns it off; budget_tokens caps it; effort low to max |
none (or "thinking": {"type": "disabled"}) turns thinking off entirely. minimal is treated as low. Reasoning tokens count toward the output limit, so on hard tasks leave room for them.
On tool-calling turns, send the model’s earlier reasoning back with the assistant message: reasoning_content in Chat Completions, reasoning items in Responses, thinking blocks in Messages. Coding agents do this already.
Images
Section titled “Images”The model accepts images as base64 data: URLs: an image_url part in Chat Completions, input_image in Responses, or an image block with a base64 source in Messages. Image URLs that point anywhere else are rejected with a 400, because Inferam never fetches URLs on your behalf. Audio and video aren’t supported.
Throughput
Section titled “Throughput”Generation runs at roughly 75 to 110 output tokens per second for a single stream, and prompt processing at about 2,000 tokens per second. The service runs a few generations at once. When all are busy, requests queue fairly for up to 30 seconds.
Known limitations
Section titled “Known limitations”- Quantised weights. The model runs as a 2.9-bit-per-weight quantisation to fit Inferam’s hardware. No published evaluation compares it with the full-precision model.
- No server-side tools. Web search, code execution and other provider-hosted tools aren’t available. Function tools you define work normally.
- No conversation state. Every request carries the whole conversation; Responses’
previous_response_idisn’t supported. - Token counting is an estimate.
/v1/messages/count_tokensapproximates the count; the usage in each response is exact. - Single region. The model runs in one location. Expect added network latency far from it, and see status.inferam.ai during incidents.