DeepSeek V4.1 Flash 1017: What The Price And Performance Table Reveals

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DeepSeek released V4.1 Flash 1017 on October 17, replacing the September version as the default model on its website, app and API while keeping the listed token prices unchanged. The company’s benchmark table reports gains on all 11 tests it published, including larger increases on some agent-work benchmarks, but the results are DeepSeek’s own and have not been independently reproduced.

The updated model is served as deepseek-flash; the older names deepseek-v4-flash and deepseek-v4-flash-vision-exp are also routed to it, according to the source report. DeepSeek describes the release as a minor update. Its published table shows Terminal-Bench 4.0 rising from 31.2 for the September model to 42.3 for 1017, while ProgramBench increases from 20.3 to 29.1. DeepSWE v1.1, another coding benchmark, rises by 2.8 points, from 74.2 to 77.0.

DeepSeek says 1017 produces shorter outputs, which can reduce completion time and token costs. The model card lists API prices per million tokens of $0.15 for uncached input and $0.60 for output 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. The company’s peak pricing applies on weekdays from 01:00–04:00 and 06:00–10:00 UTC, excluding Chinese public holidays.

The model weights are available under the MIT license, according to the source, and the model accepts image input. The technical details reported there describe a sparse Mixture of Experts architecture and a context window of up to one million tokens; the hosted API is said to allow up to 384,000 output tokens. These specifications and performance figures are reported claims, not results independently verified in the supplied material.

Lower Costs, Uneven Benchmark Gains

The release’s main practical case is not that 1017 tops every benchmark. In DeepSeek’s own table, it leads four of 11 rows; Claude Opus 5 leads six, and GPT-5.6 Sol leads one. Opus 5 scores higher on the table’s HLE reasoning test, ProgramBench, NL2Repo-Bench and two vision tests. The results suggest 1017’s gains are concentrated in certain agent workflows rather than representing a broad lead across all measured capabilities.

Cost may matter more to teams running coding agents repeatedly. The source report says a DeepSeek chart puts 1017 at 77% on DeepSWE for just under $0.20 per task, while a lower-effort setting reaches more than 72% at about $0.10. Those are figures reported from DeepSeek’s evaluation, and cost per task depends on the workload and evaluation setup. The listed token rates offer one way to estimate API spending, but do not by themselves establish how much a real application will cost.

An early Kingy AI pilot provides a limited outside check: across seven small tasks run twice, the report says 1017, GPT-6.1 Sol and Claude Sonnet 5.5 each passed all 14 runs, with 1017 costing roughly a quarter as much. The small test did not distinguish the three models’ success rates, and one task reportedly took about 27 seconds on 1017 compared with about three seconds on Sonnet. This is evidence from a small pilot, not a broad independent benchmark.

How the October Update Compares

DeepSeek introduced the preceding V4.1 Flash version on September 10, 2026. The October 17 release replaces it in the company’s services, so users calling the default model name receive 1017 without changing the identifier. DeepSeek’s table compares the update with that September model and four selected competitors. As the source report points out, those rivals are not the newest models named in the wider market, which limits what the table can establish about 1017’s standing against current systems.

Benchmark conditions also vary by test. The source says Terminal-Bench used the Claude Code harness and DeepSWE used Mini-SWE-V2. DeepSeek’s HLE result is for the full set, while the text-only subset score differs; the GLM-5.3 figure shown is text-only. Some cells are marked unavailable. These distinctions matter because scores from different versions, subsets or evaluation environments may not be directly comparable.

The source also reports that a separate technical account attributed the gains to post-training with reinforcement learning and self-feedback, but says DeepSeek did not explain what changed in training. It describes the model as tuned for DeepSeek Harness and for Tencent’s CodeBuddy and WorkBuddy. Those details point to intended agent use, but do not establish how the model performs across other tools or production workloads.

Independent Tests Still Needed

No broad independent reproduction of DeepSeek’s 11 benchmark results is described in the supplied material. The company has not said what changed in training, while the reported attribution to self-feedback reinforcement learning comes from a secondary account. The exact evaluation prompts, run-to-run variation and degree to which results transfer to other agent setups are not established here.

The cost-per-task comparison also depends on how each task is defined, how many attempts are allowed and which harness is used. The Kingy AI pilot covered seven small tasks run twice; it cannot settle performance across larger codebases, longer jobs or more varied real-world requests. The supplied source ends while describing DeepSeek’s search and fetch tools, so their full behavior, availability and pricing are not clear from the material provided.

Watch for Replication and Tool Details

The next useful evidence will be independent evaluations that publish comparable prompts, harnesses, task counts and costs, including tests against newer rival models. Developers considering the update can also compare it with the September version on their own workloads, tracking success rate, latency, token use and failure modes rather than relying on a single benchmark score.

DeepSeek’s API now routes the default Flash name to 1017, according to the source report. Further details are needed about the search and fetch tools mentioned alongside the release, including their capabilities, access conditions and any separate pricing. Until those details and broader test results are available, the strongest confirmed development is the model replacement and the publication of DeepSeek’s own benchmark and price information.

Key Questions

What is DeepSeek V4.1 Flash 1017?

It is an October 17 update to DeepSeek’s V4.1 Flash model. The source report says it replaced the September version on DeepSeek’s website, app and API, where the default identifier is deepseek-flash.

Did DeepSeek raise its API prices for 1017?

No price increase is reported. The source says the model launched at unchanged listed rates: off-peak prices include $0.15 per million uncached input tokens and $0.60 per million output tokens, with higher peak rates.

Does 1017 lead every benchmark in DeepSeek’s table?

No. DeepSeek’s table shows 1017 leading four of 11 rows. Claude Opus 5 leads six and GPT-5.6 Sol leads one, according to the source material.

Have independent tests confirmed DeepSeek’s benchmark gains?

The supplied report says the 11 published benchmark results have not been independently reproduced. It cites a small Kingy AI pilot, but that test covered only seven tasks run twice and cannot establish broad performance.

What should developers check before using the model?

They can test 1017 on their own tasks and compare accuracy, latency, token use and cost with alternatives. Results may vary by workload, tool harness and pricing period; AI outputs can be unreliable, so important results should be checked.

Source: ThorstenMeyerAI.com