A Guide To The DeepSeek V4.1 Flash 1017 Agent Price And Performance

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DeepSeek released DeepSeek-V4.1-Flash-1017 on October 17, replacing the September version across its website, app and API while keeping the listed price unchanged. The company reports higher scores on all 11 benchmarks it published, with the biggest gains in multi-step agent tasks; those results have not been independently reproduced.

The update is served under the API name deepseek-flash; calls using that name now reach 1017 automatically. DeepSeek also routes the older names deepseek-v4-flash and deepseek-v4-flash-vision-exp to the updated model. Its weights are available under the MIT license, according to the supplied source material.

In DeepSeek’s model-card figures, Terminal-Bench 4.0 rose from 31.2 to 42.3, while ProgramBench increased from 20.3 to 29.1. The company also reports a smaller gain on DeepSWE v1.1, from 74.2 to 77.0. The scores are benchmark results reported by DeepSeek, not independently verified findings.

The model’s architecture and scale are described differently across the cited materials. A technical report describes a sparse Mixture of Experts design, a 1-million-token context window and a compressed KV cache; the reported parameter count also differs by counting method. DeepSeek’s report does not specify what changed in training. The Decoder attributes the improvement to reinforcement learning with self-feedback, but that account is not a training change confirmed in the supplied DeepSeek description.

Lower Costs for Agent Tasks

The update’s main practical pitch is cost per completed task, rather than a claim that the model leads every benchmark. DeepSeek’s listed API prices are $0.15 per million input tokens on cache misses and $0.60 per million output tokens off-peak, rising to $0.30 and $1.20 during peak hours. Cached input is listed at $0.003 off-peak and $0.006 at peak.

At those off-peak rates, the supplied source estimates that a task using 1 million uncached input tokens and 200,000 output tokens would cost $0.27. It compares that with $4 at the stated rates of $2 per million input tokens and $10 per million output tokens. The comparison illustrates how token pricing can affect workloads, but actual bills depend on token use, caching and timing.

The Decoder reports DeepSeek’s claim that 1017 reached 77% on DeepSWE for just under $0.20 per task, while a lower-effort setting scored above 72% at about $0.10. These figures are not enough to establish results across all real-world tasks. For developers, the reported combination of low API rates and stronger agent benchmarks makes testing the model more relevant; reliability, speed and task-specific performance still need evaluation.

How the Benchmark Compares

DeepSeek calls 1017 a minor update to V4.1 Flash, released on September 10. Its benchmark table compares the new version with that September model and four other systems: Kimi K3, GLM-5.3, GPT-5.6 Sol and Claude Opus 5. On that table, 1017 leads four of 11 rows. Claude Opus 5 leads six, including HLE, ProgramBench and NL2Repo-Bench; GPT-5.6 Sol leads the remaining row.

The benchmark setups are not identical across every result. The source notes that Terminal-Bench was run in the Claude Code harness and DeepSWE in Mini-SWE-V2. It also says DeepSeek’s HLE score covers the full set, while another comparison uses a text-only subset. Those distinctions matter when interpreting score differences.

DeepSeek’s comparison omits newer systems named in the source, including GPT-6.1 Sol, GPT-6 Astra, Opus 5.5 and Gemini 4 Argon. Kingy AI separately reports Terminal-Bench results of 57 to 66 for current frontier models, but those evaluations are described as non-matching, so they do not provide a direct head-to-head comparison.

“minor update”

— DeepSeek, as described in its model materials

What the Results Cannot Show

Independent verification is absent for the benchmark table in the supplied reporting. DeepSeek has not explained what changed in training, and the source’s account of reinforcement learning with self-feedback is attributed to The Decoder rather than established here as a company-confirmed detail.

The Kingy AI pilot is limited: it covers seven small tasks, each run twice. All three tested models passed the 14 runs, and one 1017 task reportedly took about 27 seconds compared with about three seconds for Sonnet. The pilot therefore offers a small cost-and-task datapoint, not a general performance ranking.

The source material ends as it begins describing DeepSeek’s developer search tools, saying the search engine was opened through two agent tools and that Search returns real-time results. It does not provide the rest of the tool details, so their functions and availability cannot be fully assessed here. The benchmark table also does not compare 1017 directly with several newer rival models.

Developer Testing and Tool Details

Developers can access the updated model through DeepSeek’s listed endpoints and test its performance against their own workloads. DeepSeek recommends its DeepSeek Harness version 0.2.1-rc.1 or later; the source also says the model was tuned for Tencent’s CodeBuddy and WorkBuddy. No subsequent benchmark release or independent evaluation date is given in the material.

Further reporting would need to establish whether independent tests reproduce the reported gains, how the model performs on newer head-to-head comparisons and how task costs vary in production. Details on the announced Search and Fetch agent tools are also incomplete in the source supplied.

Key Questions

What changed with DeepSeek V4.1 Flash-1017?

DeepSeek says 1017 improves scores across its 11 published benchmarks, with larger reported gains on Terminal-Bench 4.0 and ProgramBench. It replaced the September model on DeepSeek’s website, app and API on October 17, 2026.

Did DeepSeek change the API price?

No price change is reported. The supplied material lists off-peak rates of $0.15 per million input tokens for cache misses and $0.60 per million output tokens, with higher peak rates. The bill for a particular task depends on token use and caching.

Are the benchmark gains independently verified?

Not in the supplied reporting. The benchmark figures come from DeepSeek’s own model card, and the source says they have not been independently reproduced.

Does 1017 outperform every model in DeepSeek’s chart?

No. It leads four of the 11 benchmark rows in the chart. Claude Opus 5 leads six and GPT-5.6 Sol leads one, according to the figures provided.

What remains uncertain about the update?

Independent benchmark results, the exact training changes behind the update and direct comparisons with newer rival models remain unavailable in the supplied material. The source also cuts off before detailing DeepSeek’s announced Search and Fetch tools.

Source: ThorstenMeyerAI.com