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DeepSeek V4.1 Flash 1017 Review: API Pricing, Context Limits, Agent Performance and Comparison with Gemini 4 Argon

DeepSeek V4.1 Flash 1017 delivers 1M-token context and competitive agent benchmarks at $0.15 per million input tokens. This review covers pricing, limits and direct comparison with Gemini 4 Argon.

Rating 4.9 80 votes
Neo-brutalist 2D editorial illustration featuring DeepSeek V4.1 Flash lightning and context window metrics.

DeepSeek V4.1 Flash 1017, released October 17 2026, updates the prior Flash checkpoint with the same 552B backbone and improved agentic scores. It runs under the model ID deepseek-flash on the DeepSeek API and ships as open weights under MIT license on Hugging Face. The update keeps pricing and core architecture unchanged while lifting results on coding and automation tasks.

DeepSeek V4.1 Flash 1017 API Pricing and Context Limits

The model accepts a 1M-token context window shared across input and output. Maximum output length reaches 384K tokens through the API, with some listings citing up to 393K. Input supports text and images; output remains text only. Tool calling, structured JSON responses and controllable reasoning effort on a low-to-max or 1–100 scale are all available.

Off-peak pricing sits at $0.15 per million input tokens and $0.60 per million output tokens on cache misses. Cached input drops to $0.003 per million. Search and Fetch API calls cost $0.75 per 1,000 successful requests. Peak rates run roughly twice the off-peak figures. These rates match the previous Flash version exactly.

  • 1M context window shared input/output
  • 384K max output tokens via API
  • Native image input, text output
  • Tool calling and JSON mode supported
  • Reasoning effort control (low/high/max or 1–100)

Agent Performance and Direct Comparison with Gemini 4 Argon

DeepSeek reports gains of 2–11.1 points across eleven benchmarks versus the prior checkpoint. DeepSWE v1.1 reaches 77.0 percent, Terminal-Bench 4.0 hits 42.3 percent, and Automation-Bench v1.0.6 scores 60.5 percent. Agents’ Last Exam lands at 35.4 percent and Apex Bench v1.1 at 72.1 percent. These numbers come from vendor evaluations and remain subject to independent verification.

Flat vector graphic comparing DeepSeek V4.1 Flash benchmark scores against Gemini 4 Argon.
Benchmark gains place DeepSeek V4.1 Flash competitively against Gemini 4 Argon.

Gemini 4 Argon, released late September 2026, uses a larger sparse MoE design and lists 1M unified context with longer single-response output possible. Its introductory pricing through November 30 2026 runs $2 per million input and $10 per million output, with cached input at $0.10. After that date rates double. Gemini supports additional modalities including video, audio and PDF, plus stronger long-context retrieval on tasks such as GraphWalks.

MetricDeepSeek V4.1 Flash 1017Gemini 4 Argon
Input price per 1M tokens$0.15$2 (intro)
Output price per 1M tokens$0.60$10 (intro)
Max context1M1M
DeepSWE v1.177.0%77.9% (Google eval)
ModalitiesText + imageText, image, video, audio, PDF

DeepSeek V4.1 Flash 1017 costs roughly ten to twenty times less per token while matching or exceeding Gemini on several agentic coding benchmarks. Gemini leads on broader knowledge and long-context enterprise tasks and offers more input types. Users running high-volume agent workflows often choose DeepSeek for cost control and switch to Gemini when output quality on complex multimodal prompts matters more. Monitor independent leaderboards as new evaluations for the 1017 checkpoint appear. See also the Google Gemini API Pricing 2026 guide for current Gemini rate details after the introductory period.

Frequently asked questions

What is the context window size of DeepSeek V4.1 Flash 1017?

The model supports a 1M-token context window shared between input and output. Maximum output length reaches 384K tokens through the official API.

How much does DeepSeek V4.1 Flash 1017 cost per million tokens?

Off-peak rates are $0.15 per million input tokens and $0.60 per million output tokens. Cached input costs $0.003 per million. Peak rates are approximately double.

Does DeepSeek V4.1 Flash 1017 support tool calling?

Yes. The model includes tool calling, structured JSON output and adjustable reasoning effort levels.

How does DeepSeek V4.1 Flash 1017 compare to Gemini 4 Argon on cost?

DeepSeek charges roughly ten to twenty times less per token. Gemini lists $2/$10 during its introductory period and doubles after November 30 2026.

Comments 15

Lukas R
just tried the 1M context on a 800-page code repo, it handled it without a hitch and cost me under a dollar. impressive
Mei
the $0.15 per million input is hard to beat, but how stable is the output quality when you actually push the full context?
Sam K
Gemini 4 Argon still felt faster in my short agent tests, but DeepSeek is miles cheaper. trade-offs everywhere
Fatima
i switched my customer-support agent from Claude to this yesterday and the 1017 model finally stopped hallucinating product SKUs.
thom84
anyone know the exact token limit on system prompts? the article only mentioned overall context
Diego M
replying to thom84: system prompt is capped at 32k even though context is 1M, found it in the docs after the review dropped
Nadia Petrova
the agent benchmark numbers look good on paper but i'd like to see real multi-step tool use results
Jonas
cheaper than Gemini and better long-context recall? might actually cancel my openai team plan this month
Rachel S.
the review says it beats gemini on 5 out of 7 agent tasks, but loses badly on code generation. anyone else seeing that?
Elena
replying to Rachel S.: yeah, noticed the same code-gen gap in my leetcode eval set. still worth the price for non-code agents though
aaron_v
finally a model where paying for output tokens doesn't bankrupt you when it writes long chain-of-thought
Priya
curious if the 1017 flash version drops intelligence compared to the regular V4.1, especially on math
Markus
my retrieval-augmented agent went from 74% to 91% accuracy just by swapping to the new DeepSeek endpoint
Omar
the context-length price scaling feels fair until you hit the 1M ceiling. then the per-token rate jumps, sneaky
Lin
anyone running this on-prem or is it still API-only? would love to test latency on my local setup

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