DeepSeek V4.1 Flash 1017: Benchmarks, Changes and API Access

What changed in the 1017 release

DeepSeek describes DeepSeek-V4.1-Flash-1017 as a minor update to DeepSeek-V4.1-Flash — same architecture, same 1M-token context, same multimodal input — that delivers substantial improvements across reasoning, coding, agentic tasks and visual benchmarks. The mechanism is reinforcement learning with self-feedback, aimed at end-to-end project development rather than isolated snippets: the DeepSeek 1017 release notes demo a 3D model of the Forbidden City, a browser racing game, a Portal 2 recreation in the Godot engine, and a 3D scene tracing prehistory to the modern era.

The cost side matters as much as the scores. Alongside higher benchmarks, the 1017 build reduces task execution time and output length — so an agent loop finishes sooner and bills fewer tokens. DeepSeek frames this as advancing the performance-cost Pareto frontier across multiple agent harnesses. If you run long-horizon agents, the output-length reduction alone usually moves your bill more than the per-token rate does.

DeepSeek V4.1 Flash 1017 benchmarks

Numbers below are DeepSeek's own published results from the 1017 model card, compared against the previous V4.1-Flash build and against K3, GLM-5.3, GPT-5.6 Sol and Opus 5. Where the row is marked w/tools, the model was given tool access — those rows are not comparable to the tool-free rows above them.

Read the table by row groups. Agentic and coding rows are where 1017 separates from its predecessor most — Terminal-Bench 4.0 gains 11.1 points, ProgramBench gains 8.8, Apex Bench gains 7.5, Automation-Bench gains 5.7. The vision rows move 2.0 to 4.1 points. And the ceiling is still visible: Opus 5 wins on HLE, Terminal-Bench 4.0, NL2Repo-Bench, ProgramBench, Chartography and BabyVision, GPT-5.6 Sol takes ZeroBench-main, and 1017 leads this particular table on DeepSWE v1.1, Automation-Bench, Agents' Last Exam and Apex Bench v1.1 — a mixed picture, not a clean sweep.

1017 vs the previous V4.1-Flash build

If you already integrated V4.1-Flash, the migration question is whether the point release is worth a model-ID change. On DeepSeek's numbers, yes for agentic and coding work — every published row improved, none regressed, and cost per task moved in the right direction at the same time.

• Biggest absolute gains: Terminal-Bench 4.0 +11.1, ProgramBench +8.8, Apex Bench v1.1 +7.5, HLE +5.8, Automation-Bench v1.0.6 +5.7.
• Smallest gains: DeepSWE v1.1 +2.8, NL2Repo-Bench +2.8, ZeroBench-main +2.0 — near the top of the curve already, so diminishing returns there.
• Cost: shorter outputs and faster task completion mean fewer billed tokens per finished task, even at an unchanged per-token rate. DeepSeek's own chart plots pass rate against cost per task rather than pass rate alone.
• Nothing removed: 1M-token context, multimodal input and MIT-licensed open weights all carry over. This is a drop-in update, not a new family.

How to call DeepSeek-V4.1-Flash-1017 through TokenRa

The integration is the same OpenAI-compatible shape as every other model on TokenRa: create a key, point your client at the TokenRa base URL, and set the model field to the 1017 build. No separate DeepSeek provider account is needed, and the same key keeps working for GLM-5.3-Flash, Kimi and the rest of the catalog. For parameter details, see the DeepSeek API documentation, which uses the same OpenAI-compatible request format.

curl https://tokenra.io/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4.1-flash-1017",
    "messages": [
      {"role": "user", "content": "Build a single-file HTML racing game with keyboard controls. Return the full file."}
    ],
    "temperature": 1.0,
    "top_p": 1.0
  }'

DeepSeek's recommended sampling parameters for this build are temperature 1.0, top_p 1.0, a 1M-token context window and max_tokens of at least 256K. Those defaults are tuned for long agentic runs — do not carry over a temperature of 0.2 from a chat integration and conclude the model underperforms.

Using 1017 with opencode and Claude Code

Most people searching for this release are not calling the raw API — they are trying to get the model into the coding agent they already run. Both work through the OpenAI-compatible base URL, but the configuration surface differs.

• opencode: add a custom provider with the TokenRa base URL and your key, then set the model to the 1017 identifier. Keep the provider entry in your project config so the agent and your scripts share one credential.
• Claude Code: point the base URL at TokenRa through the OpenAI-compatible settings rather than the native Anthropic path, and verify that tool-calling round-trips before a long session. DeepSeek's own Terminal-Bench 4.0 figure was measured with the Claude Code harness, so this pairing is the one its published score describes.
• DeepSeek Harness: if you use DeepSeek's own harness, upgrade to v0.2.1-rc.1 or later — the 1017 build is explicitly optimized for it.
• Long sessions: set max_tokens high enough for agentic output (256K per DeepSeek's guidance) and stream, or you will hit client-side timeouts before the model finishes a project-scale task.

DeepSeek-V4.1-Flash-1017 pricing on TokenRa

TokenRa lists DeepSeek V4.1 Flash 1017 as its own line item, at the rates below. Confirm the live rate and the enabled model ID in the console before production use. DeepSeek's first-party rates are listed on the official DeepSeek pricing page.

When to pick 1017, and when not to

• Pick it for project-scale code generation, multi-step agent loops, terminal and tool-heavy automation, and long-context work where output length drives your bill.
• Pick GLM-5.3-Flash instead if you are optimizing purely on price per token — it is cheaper per million input tokens, but expect lower scores: on this table GLM-5.3 trails 1017 on every row it reports, including DeepSWE v1.1 (66.9 vs 77.0) and Apex Bench v1.1 (56.6 vs 72.1).
• Pick GPT-5.6 Sol or Opus 5 if your task is dominated by the rows where they still lead — HLE for both, plus ProgramBench and NL2Repo-Bench for Opus 5. The gap is real on repository-scale generation.
• Do not pick it for cheap high-volume chat classification. Flash-tier pricing is good, but a smaller model will do that job for a fraction of the output tokens.

Production checklist for the 1017 build

• Record the exact model ID in your config and logs — a dated release makes "which build produced this output" a real debugging question.
• Benchmark on your own workload before migrating. Vendor scores were measured on specific harnesses (Claude Code, Mini-SWE-V2); your harness is a different measurement.
• Set max_tokens for agentic output and stream responses; 256K-scale completions will not fit a default client timeout.
• Keep keys server-side, separate credentials per environment, and set application-level spend budgets — shorter outputs lower cost per task but do not cap a runaway loop.
• Review data handling and provider terms before sending sensitive or regulated input.

DeepSeek-V4.1-Flash-1017 FAQ

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