DeepSeek V4.1 Flash 1017 Now Available on Telnyx Inference

DeepSeek V4.1 Flash 1017, the post-training checkpoint of DeepSeek's open-weight DeepSeek-V4.1-Flash model, is now live on Telnyx Inference, hosted on Telnyx-owned GPUs.

What's new

• The updated checkpoint is live: DeepSeek released DeepSeek-V4.1-Flash-1017 as its post-training update to DeepSeek-V4.1-Flash. Per DeepSeek's model card, the checkpoint improves 11 benchmarks across coding, reasoning, and visual agent work, while producing shorter outputs and lower total runtime per task. DeepSWE v1.1 moves from 74.2 to 77.0, ahead of Claude Opus 5 at 74.0 and GPT-5.6 Sol at 73.0, and Terminal-Bench 4.0 (Claude Code harness) climbs from 31.2 to 42.3. These are DeepSeek's published results, not Telnyx measurements.
• Efficient long-context agent serving: The V4.1-Flash architecture behind this checkpoint activates 8B parameters during prefill and 16B during decode on a 552B backbone, and compresses the global KV cache to roughly one quarter of the size of the prior DeepSeek-V4-Flash generation (per the V4.1-Flash technical report). That's what makes long-running agent sessions and 1M-token document passes economical on the flash pricing line.
• Available now in the API: Call the model with model=deepseek-ai/DeepSeek-V4.1-Flash-1017 in the OpenAI-compatible Chat Completions API. You get a 1M-token context window, 64K max completion tokens, vision input, tool calling, and selectable reasoning effort, from none for fast paths to high for harder prompts. It is served in US and EU regions on the Default tier and in the US on the Flex tier. The September checkpoint remains available as deepseek-ai/DeepSeek-V4.1-Flash.

Why it matters

The gains in this checkpoint land exactly where agentic workloads live: coding agents, terminal automation, and long-context document pipelines. Open weights under the MIT license, a 1M-token context, a KV cache a quarter the size of the prior generation, and cached input rates that reward reuse all point the same way: more agent work per dollar. On Telnyx it runs on GPUs we own, in-region, with no cloud provider markup and zero data retention on prompts and responses. For current per-token rates, see Telnyx Inference pricing.

Example use cases

• Coding and terminal agents: With the DeepSWE and Terminal-Bench gains on the model card, coding-agent workflows get open-weights performance near the proprietary frontier, at flash-tier rates.
• Long-context review and RAG: A 1M-token window with the compressed KV cache keeps full-codebase passes and persistent multi-turn sessions cost-efficient as context grows.
• Document and screen understanding: Native vision input handles mixed text and image workloads, including contracts, invoices, screenshots, and UI flows.
• Throughput-tolerant background runs: Routes for offline evaluation and document processing are more efficient at Flex-tier rates in the US.

Getting started

1. Call the chat completions endpoint with model=deepseek-ai/DeepSeek-V4.1-Flash-1017:

curl -X POST https://api.telnyx.com/v2/ai/openai/chat/completions \
  -H "Authorization: Bearer $TELNYX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-V4.1-Flash-1017",
    "messages": [
      {"role": "user", "content": "Write a SQL query that computes month-over-month growth from an orders table."}
    ]
  }'

2. List available models through the model catalog API to see the hosted lineup, regions, and service tiers.

Learn more in the model documentation and in DeepSeek's 1017 model card.