DeepSeek 4.1 Flash 1017: Benchmarks, Pricing, Real-World Tests and How to Use It

DeepSeek 4.1 Flash 1017 — officially DeepSeek-V4.1-Flash-1017 — is an updated checkpoint of DeepSeek's open-weight V4.1 Flash model, released on October 17, 2026 (the "1017" in the name is the release date). It uses the same architecture as the original V4.1 Flash from September 10, but better post-training makes it score higher on every benchmark DeepSeek published, finish tasks with fewer tokens, and cost exactly the same. DeepSeek describes the result as a Pareto improvement: better performance at the same or lower cost.

This guide pulls together DeepSeek's own model card and changelog, the V4.1 Flash technical paper, live pricing data, and hands-on results from nine independent YouTube reviewers who tested the model on launch day. Where sources disagree, we say so.

Key takeaways

• What it is: a post-training refresh of DeepSeek V4.1 Flash, not a new architecture. Existing V4.1 Flash deployments can swap in the new weights.
• Biggest gains: agentic coding and terminal work. Terminal-Bench 4.0 rose from 31.2 to 42.3, and DeepSWE v1.1 reached 77.0, the top score in DeepSeek's comparison table.
• Efficiency: testers measured roughly 40–50% fewer generated tokens and noticeably fewer agent turns per task.
• Price: unchanged, about $0.15 input and $0.60 output per million tokens off-peak on DeepSeek's API.
• Limits: it still trails Claude Opus 5 on hard reasoning (HLE 42.6 vs 56.3), and long agentic jobs can take a long time in wall-clock terms.

DeepSeek V4.1 Flash 1017 at a glance

Item	Detail
Official name	DeepSeek-V4.1-Flash-1017
Release date	October 17, 2026
Type	Updated checkpoint (post-training refresh) of DeepSeek V4.1 Flash
Architecture	Multimodal Mixture-of-Experts, same as V4.1 Flash
Parameters	763B total on Hugging Face; 552B backbone per the V4.1 Flash paper
Active parameters	16B per token when decoding, 8B during prefill (paper)
Context window	1 million tokens
Input	Text and images (native visual understanding)
License	MIT (open weights)
API model name	deepseek-flash (DeepSeek), deepseek/deepseek-v4.1-flash-1017 (OpenRouter)
API price (off-peak)	$0.15 / 1M input, $0.003 / 1M cached input, $0.60 / 1M output
Recommended sampling	temperature 1.0, top_p 1.0, max_tokens of at least 256K

What is DeepSeek V4.1 Flash 1017?

DeepSeek V4.1 Flash is the smallest model in DeepSeek's newest architecture family. When it launched on September 10, 2026, DeepSeek pitched it as "smarter, faster, more efficient," with native visual understanding and an architecture designed for faster inference, higher throughput and scaling to larger models.

The 1017 release is a new set of weights for that same model. According to DeepSeek's Hugging Face model card, it is "a minor update to DeepSeek-V4.1-Flash" that improves reasoning, coding, agentic and visual benchmarks while reducing task execution time and output length. Reviewers were quick to point out that the gains look anything but minor. Several said the jump was large enough to justify a "V4.2" label.

The DeepSeek Flash release timeline

The 1017 checkpoint is the latest step in a fast release cadence. Dates below come from DeepSeek's changelog and OpenRouter's model listings.

Date	Release	Note
April 2026	DeepSeek V4 Flash	First V4-generation Flash model
July 31, 2026	DeepSeek V4 Flash 0731	Updated V4 Flash checkpoint
September 10, 2026	DeepSeek V4.1 Flash	New architecture family; native vision; API name changed to deepseek-flash
October 17, 2026	DeepSeek V4.1 Flash 1017	Post-training refresh; web, app and API all switched over

When V4.1 Flash launched, DeepSeek retired the older deepseek-v4-flash and deepseek-v4-flash-vision-exp names and temporarily routed them to V4.1 Flash for compatibility.

What changed in the 1017 update?

Same architecture, better post-training

Under the hood, nothing structural changed. The parameter count, layer design and inference code are identical to the September model. One reviewer who maintains a local inference fork confirmed that the existing engine and toolbox load the new weights without any code changes. What changed is post-training, the reinforcement learning and fine-tuning applied after pretraining.

Trained by building whole projects

DeepSeek says it focused this round of training on agentic coding and complete project development, using reinforcement learning with self-feedback. The model builds an entire project, such as a game, an app or a 3D scene, checks its own work, and is rewarded when the result is good. Because V4.1 Flash can see images, it can look at what it rendered instead of only reading its own code.

That explains why launch-day demos leaned so heavily on 3D scenes, browser games, motion graphics and voxel art. Those are exactly the tasks DeepSeek trained toward, and they are popular test prompts on social media right now.

Shorter outputs and fewer agent turns

The most practical change is efficiency. The earlier V4.1 Flash was criticized for thinking at great length and making two to three times as many tool calls as rival models, which made it cheap per token but slow per task. The 1017 checkpoint is tuned to finish tasks in fewer turns with shorter answers.

Independent measurements back this up:

• On a local run of a "Terminal-Bench Mini" suite, one tester saw 44% fewer generated tokens and 42% less time per task compared with the previous checkpoint, with fewer turns on average. These were early results from the first eight to nine tasks.
• Another reviewer found that on agentic tool-calling tasks the new model scored better while using about half the tokens of the original.

For anyone running the model on their own hardware, where token generation speed is the bottleneck, this matters more than any benchmark gain.

New recommended sampling settings

The model card now recommends temperature 1.0 and top_p 1.0, with a generous max_tokens budget of at least 256K. The original V4.1 Flash recommended top_p 0.95, so update your configuration if you set that explicitly. DeepSeek also asks users of its own agent harness to upgrade to DeepSeek Harness 0.2.1-rc.1 or later for the best results with the new checkpoint.

DeepSeek V4.1 Flash 1017 architecture and specs

The 1017 update inherits everything from the V4.1 Flash design, which DeepSeek described in the paper DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression (arXiv, September 2026):

• Mixture-of-Experts backbone of 552B parameters, activating only 16B parameters per token during decoding and 8B during prefill. The small active footprint is the main reason the model is so cheap to serve.
• Aggressive KV-cache compression. Cross-layer KV reuse in "Compressed Sparse Attention 2" combined with FP4 KV caching brings the global KV cache down to about 890 bytes per token, roughly a quarter of what DeepSeek V4 Flash needed.
• SWA Bounded Replay, a deployment optimization that shrinks the persistent cache kept on SSD or host memory to about one-eighth of V4 Flash's.
• One-million-token context and native image input.
• Pretraining on about 45 trillion tokens of multimodal data.

A note on parameter counts: The Hugging Face listing for the 1017 weights shows 763B parameters, while the paper cites 552B backbone parameters. Both numbers circulate in coverage. The paper does not break down the difference, but the "backbone" wording suggests the larger figure counts components outside the core language model. Either way, this is a data-center-scale model. Open weights do not mean it will run on a laptop.

Official benchmarks: how DeepSeek V4.1 Flash 1017 compares

DeepSeek's model card compares the 1017 checkpoint with the original V4.1 Flash, two open-weight rivals (Kimi K3 and GLM-5.3) and two closed models (GPT-5.6 Sol and Claude Opus 5). Bold marks the best score in each row.

Benchmark	V4.1 Flash 1017	V4.1 Flash (Sep)	Kimi K3	GLM-5.3	GPT-5.6 Sol	Claude Opus 5
HLE (no tools)	42.6	36.8	43.5	42.0	44.5	56.3
Terminal-Bench 4.0	42.3	31.2	12.6	37.9	39.9	51.8
DeepSWE v1.1	77.0	74.2	67.5	66.9	73.0	74.0
ProgramBench	29.1	20.3	17.5	19.0	23.0	37.0
NL2Repo-Bench	68.2	65.4	58.0	58.0	56.8	75.3
Automation-Bench v1.0.6	60.5	54.8	46.7	48.8	45.8	50.3
Agents' Last Exam	35.4	31.8	27.6	28.5	26.7	28.6
Apex Bench v1.1	72.1	64.6	47.8	56.6	54.3	65.8
Chartography (with tools)	83.0	78.9	68.1	—	79.9	84.0
BabyVision (with tools)	92.5	89.6	85.7	—	88.9	94.1
ZeroBench-main (with tools)	51.0	49.0	41.0	—	53.0	52.0

Source: DeepSeek-V4.1-Flash-1017 model card. Terminal-Bench 4.0 was run in the Claude Code harness and DeepSWE v1.1 in the Mini-SWE-V2 harness. On the text-only HLE subset, 1017 scores 44.0 and the September model 39.1. GLM-5.3's HLE figure is also text-only.

How to read these numbers

1. It improved on every row. Against the September checkpoint, 1017 is higher on all 11 benchmarks. The biggest jumps are Terminal-Bench 4.0 (+11.1 points), ProgramBench (+8.8), Apex Bench (+7.5) and HLE (+5.8).
2. It leads on agentic work, not on raw reasoning. 1017 has the best score in 4 of 11 rows: DeepSWE, Automation-Bench, Agents' Last Exam and Apex Bench. Claude Opus 5 wins six rows, including the hard-reasoning HLE (56.3 vs 42.6) and repository-scale coding (ProgramBench, NL2Repo). GPT-5.6 Sol wins one.
3. It dominates open-weight rivals. It beats Kimi K3 and GLM-5.3 on every row where they report a score, while being far smaller than Kimi K3. One reviewer put Kimi K3's weights at over 1.5 TB, compared with roughly 500 GB for DeepSeek.
4. The closed-model comparison is dated. Opus 5 and GPT-5.6 Sol shipped in the summer of 2026. The models most developers use today, such as Claude Opus 5.5, Sonnet 5.5 and GPT-6 Astra, are missing from DeepSeek's table. You need independent benchmarks for that comparison.

A useful mental model, suggested by one reviewer: think of Opus as the "brain" you bring in for hard problems, and DeepSeek V4.1 Flash 1017 as a capable, inexpensive "worker" that executes well-specified tasks.

Independent tests: what reviewers found

Vendor benchmarks only go so far. Here is what nine independent reviewers reported in the first 24 hours. Most tests ran through DeepSeek's API or OpenRouter, often at off-peak prices.

Visual Forge: second of 18 models for 71 cents

The Visual Forge benchmark gives every model the same eight interactive 3D web tasks and scores the resulting pages in a real browser. DeepSeek V4.1 Flash 1017, run on high effort, scored 7.50, which put it second of 18 models, 0.01 points behind GPT-6 Astra and ahead of Claude Opus 5.5, Sonnet 5.5 and Gemini 4 Argon.

Model	Score	Avg. cost per test	Avg. time per test
GPT-6 Astra	7.51	$3.77	~25 min
DeepSeek V4.1 Flash 1017	7.50	~$0.09	~11 min
Claude Opus 5.5	lower	$5.18	~30 min
Claude Sonnet 5.5	lower	$4.10	—

The whole eight-test run cost $0.71. DeepSeek did not get there by writing less. It produced about 121,000 output tokens per test, more than three times GPT-6 Astra's 37,000, and still finished faster and far cheaper. About 99% of its 29 million input tokens were cache hits, which is a big part of the low bill.

Two caveats from the tester: DeepSeek ranked high mainly through consistency, since its lowest score was 6.85 while Opus and Sonnet each had one weak result, and it topped only one of the eight tests outright. Visually, Opus 5.5 and Sonnet 5.5 still produced the most polished pages. In its own price bracket, though, 1017 beat GPT-6 Luna and Claude Haiku 5.5 on seven of eight tests. The September checkpoint had scored 5.91 on the same board, although the test setup has changed since then.

AI Coding Daily: first Chinese model in that leaderboard's top 10

A leaderboard built around real coding tickets, code-quality reviews and bug hunts placed DeepSeek V4.1 Flash 1017 at max effort inside its top 10. The reviewer called it the first Chinese model there since Kimi K3, in a top group otherwise made up of Anthropic and OpenAI models. AI-judged code quality came in at a steady 86–87 out of 100 for back-end code and close to 90 for front-end code, and run-to-run results were noticeably more stable than earlier DeepSeek models.

At high effort, the gain was small: roughly the same as the old checkpoint. Running the model inside DeepSeek Harness instead of OpenCode lifted the max-effort score slightly further, mainly on bug hunting and code review. Typical cost stayed under about 10 cents per prompt off-peak.

A mixed result: not every benchmark improved

Not every tester saw gains. One reviewer's own coding benchmark placed the new checkpoint sixth, a regression from the original V4.1 Flash on that suite, even though it used about half the tokens on agentic tool-calling tasks. The same reviewer measured around 243 tokens per second through DeepSeek's own endpoint. Another reviewer's internal benchmark ranked the model around 10th to 11th overall, with agentic coding as its standout strength.

The takeaway: 1017 is clearly better at long, multi-step agentic tasks, but gains on short, single-shot prompts are smaller and less consistent.

Price-versus-quality comparisons

Reviewers ran many side-by-side tests. Costs below are what they reported for single runs, so treat them as anecdotes rather than benchmarks:

Task	DeepSeek V4.1 Flash 1017	Rival	Verdict
Same creative coding prompt	~$0.23, about 1 hour	Claude Opus 5.5: ~$6, about 20 min	Opus clearly better; DeepSeek about 26x cheaper
Minecraft-style sandbox game	~$0.80, ~1.5 hours	Kimi K3: ~$35, ~2.5 hours	Kimi K3 richer; DeepSeek about 44x cheaper
Animated SVG scene	~$0.40, ~50 min	Gemini 4 Argon: ~$1.20, ~13 min	Close; Gemini better overall and faster
3D Tesla Roadster scene	~$0.89	GPT-6 Luna: ~$1.07	Luna slightly better
Three web builds in an agent harness	~$0.38 total	Same token count at Opus API rates: ~$21	"Comparable, not equal" quality

A clear pattern emerges. DeepSeek V4.1 Flash 1017 gets surprisingly close to frontier quality at a small fraction of the price, but on large creative jobs it often takes longer in wall-clock time.

Practical wins

• Real-world 3D modeling: One reviewer used it to redesign a 3D-printable camera mount for a robotic arm, adding thicker walls and an enlarged cable opening from rough marked-up images. It needed one follow-up and one correction. A competing open model, Qwen 3.8 Flash Next, had failed the same job.
• Visibly better 3D output: Side-by-side comparisons of the same voxel-art and 3D-globe prompts showed cleaner, more detailed and less glitchy results than the September checkpoint.
• Fast app building in agent harnesses: In the Hermes agent framework, one tester rebuilt a working copy of a client portal in about five minutes, with an average build time of about 14 minutes across several projects.

DeepSeek V4.1 Flash 1017 pricing

DeepSeek's changelog states that pricing is unchanged with the 1017 update. DeepSeek uses time-of-day pricing, with off-peak at half the peak rate.

deepseek-flash (per 1M tokens)	Off-peak	Peak
Input, cache hit	$0.003	$0.006
Input, cache miss	$0.15	$0.30
Output	$0.60	$1.20

According to third-party pricing trackers, peak hours are 01:00–04:00 and 06:00–10:00 UTC on weekdays. Weekends and Chinese public holidays are billed off-peak all day. Many launch-day tests ran on a weekend, so their bills reflect off-peak rates.

For context, reviewers quoted output prices of roughly $10 per million tokens for Sonnet 5.5 and Gemini 4 Argon, $20 for Opus and $50 for GPT-6 Astra. Only GPT-6 Luna and Claude Haiku 5.5, at about $0.50, are cheaper on output.

On OpenRouter, the 1017 checkpoint is listed as deepseek/deepseek-v4.1-flash-1017 at $0.15 per million input tokens, $0.60 per million output tokens and $0.003 per million cached input tokens. The original V4.1 Flash remains available from many third-party providers, often at lower prices. That is why some reviews quote figures such as $0.09 input and $0.55 output. Check which checkpoint a provider actually serves.

New: DeepSeek Search and Fetch APIs

On the same day, DeepSeek opened its in-house web Search API and Fetch API to all developers. Both are billed per successful request at $0.75 per 1,000 calls, deducted from the same balance as token usage. DeepSeek says the infrastructure behind them handles hundreds of millions of requests a day. For a limited launch period, search and fetch calls made through supported agent harnesses such as Claude Code, Codex and DeepSeek Harness are free. Reviewers cited an end date of around October 31 to November 1, 2026, so check DeepSeek's pricing page for the exact cutoff. Testers reported that letting the model search the web noticeably improved results on reference-heavy builds.

How to use DeepSeek V4.1 Flash 1017

1. Chat: chat.deepseek.com and the DeepSeek mobile app switched to the 1017 checkpoint on release day.
2. DeepSeek API: keep using the model name deepseek-flash. It now resolves to the 1017 checkpoint. The API works with both OpenAI-compatible and Anthropic-compatible client libraries.
3. OpenRouter: request deepseek/deepseek-v4.1-flash-1017 to pin the new checkpoint explicitly. One reviewer noted that OpenRouter will not route to DeepSeek's own endpoint unless you allow it in your privacy settings, because that provider may use requests for training. Review that setting before sending sensitive data.
4. Agent harnesses: DeepSeek says the model is optimized for DeepSeek Harness (version 0.2.1-rc.1 or later). Reviewers also ran it successfully in OpenCode, Claude Code and Hermes Agent, and found that the harness choice visibly changes output quality.
5. Open weights: download the MIT-licensed weights from Hugging Face at deepseek-ai/DeepSeek-V4.1-Flash-1017.

Tips from early testers

• Use max reasoning effort for agentic coding. Several leaderboards only showed a big jump at max effort, and it is still cheap.
• Give clear, specific instructions. The model performs best as an executor of well-defined tasks.
• Turn on web search and fetch for projects that need references.
• For long jobs, such as multi-hour Blender or game builds, plan to run them in the background or overnight. They are cheap but not always fast.
• Watch for hallucinations. Testers noted the V4.1 family can still state wrong facts confidently.

Running DeepSeek V4.1 Flash 1017 locally

Because the architecture is unchanged, any setup that runs the original V4.1 Flash can load the 1017 weights after requantization. No engine update is needed. A community quantization by one reviewer showed what is realistic on high-memory AMD workstations:

• A 2-bit (Q2) quantization runs on one AMD "Gorgon Halo"-class machine or split across two Strix Halo machines. A 4-bit build needs two Gorgon Halo machines.
• Prefill: about 230–380 tokens per second, rising past 400 tokens per second with the SWA Bounded Replay option.
• Generation: about 14–15 tokens per second, or 17–18 tokens per second with speculative decoding.

At those generation speeds, the 1017 checkpoint's 40–50% reduction in output tokens translates directly into tasks that finish much sooner. For most people, though, a model of this size is something to call through an API rather than host.

Strengths and weaknesses

Where DeepSeek V4.1 Flash 1017 shines

• Agentic coding, terminal tasks and multi-step automation
• Front-end, 3D, game and motion-design generation, the domains DeepSeek trained toward
• Consistent results across runs
• Price-performance that reviewers repeatedly called unmatched, typically 17 to 50 times cheaper than frontier closed models on comparable tasks
• Open weights under the MIT license, a 1M-token context and native image input

Where it falls short

• Hard reasoning: it still trails Claude Opus 5 by a wide margin on HLE
• Peak visual polish: Opus 5.5 and Sonnet 5.5 still produce the best-looking results
• Wall-clock time on large creative jobs can stretch past an hour
• Mixed results on some short-task coding benchmarks
• Not practical to self-host without data-center-class or high-memory workstation hardware

Several reviewers also noticed that the model's design choices often resemble those of OpenAI models, and some reported it occasionally identifying itself as an OpenAI model during long reasoning. That has fueled speculation about distillation in its training. There is no public evidence either way, and DeepSeek has not commented.

Should you switch to DeepSeek V4.1 Flash 1017?

Use this quick decision guide:

• Already using V4.1 Flash? Yes. It is a free upgrade: same price, same API name and fewer tokens per task.
• Running high-volume agent or coding pipelines on a budget? Strongly consider it. It is the strongest result reviewers have seen in its price bracket, well ahead of GPT-6 Luna and Claude Haiku 5.5 on agentic and 3D web tasks.
• Need the best possible reasoning or visual polish? Keep a frontier model such as Claude Opus 5.5 or GPT-6 Astra for planning and hard problems, and consider DeepSeek V4.1 Flash 1017 as the cheaper sub-agent that does the implementation work.
• Need fast single-shot answers? Benchmark it on your own workload first. Gains on short tasks are smaller, and max-effort reasoning can be slow.

The bigger story is the trend. In about six months, DeepSeek has shipped V4 Flash, V4.1 Flash and now the 1017 refresh, and each step has pushed open-weight models closer to the frontier on agentic work while keeping prices low. If the next refresh closes the reasoning gap as quickly as this one closed the coding gap, the case for paying 20 to 80 times more per token will get harder to make for everyday tasks.

Sources

• DeepSeek-V4.1-Flash-1017 model card (Hugging Face)
• DeepSeek API changelog
• DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression (arXiv)
• DeepSeek V4.1 Flash on OpenRouter
• DeepSeek API pricing and peak hours (NiharDaily)
• DeepSeek Search and Fetch API pricing (SessionWatcher)
• Video reviews: WorldofAI, Donato Capitella, Julian Goldie SEO, AI Coding Daily, Bart Slodyczka, Venelin Valkov, Codedigipt, AI with Eric, AIex The AI Workbench

Frequently asked questions

What is DeepSeek 4.1 Flash 1017?

DeepSeek 4.1 Flash 1017 (official name DeepSeek-V4.1-Flash-1017) is an updated checkpoint of DeepSeek V4.1 Flash released on October 17, 2026. It keeps the same architecture as the September 10 model but has improved post-training, which raises benchmark scores and shortens outputs at the same price.

Is DeepSeek V4.1 Flash 1017 a new model or an update?

It is an update. DeepSeek calls it a minor update to DeepSeek-V4.1-Flash. The architecture, parameter count and inference code are unchanged, so it is a drop-in replacement for the earlier V4.1 Flash weights.

How much does DeepSeek V4.1 Flash 1017 cost?

On DeepSeek's API the price did not change with this update: about $0.15 per million input tokens (cache miss), $0.003 per million cached input tokens and $0.60 per million output tokens off-peak, with peak-hour prices doubled to $0.30, $0.006 and $1.20. OpenRouter lists the 1017 checkpoint at $0.15 input and $0.60 output.

What model name do I use in the DeepSeek API?

Use deepseek-flash. DeepSeek's changelog says the API calling method is unchanged and that deepseek-flash now points to DeepSeek-V4.1-Flash-1017. On OpenRouter the model ID is deepseek/deepseek-v4.1-flash-1017.

Is DeepSeek V4.1 Flash 1017 open source?

The weights are open and published on Hugging Face under the MIT license. The Hugging Face listing shows 763B parameters in total, and the DeepSeek-V4.1-Flash paper describes a 552B-parameter backbone that activates 16B parameters per token during decoding.

Can I run DeepSeek V4.1 Flash 1017 locally?

Only on serious hardware. Because the architecture is unchanged, existing V4.1 Flash inference setups can load the new weights. One tester runs a 2-bit quantization on a single high-memory AMD Strix Halo-class workstation at roughly 14 to 18 tokens per second for generation, but a typical laptop or single consumer GPU cannot run it.

Is DeepSeek V4.1 Flash 1017 better than Claude Opus 5.5 or GPT-6?

Not overall. DeepSeek only compared it against older models such as Claude Opus 5 and GPT-5.6 Sol, and it still trails Opus 5 on hard reasoning benchmarks. Independent testers found it competitive with much more expensive frontier models on agentic coding and 3D web tasks, but Opus 5.5 still produced visibly better results in most side-by-side tests.

What sampling settings should I use?

The model card recommends temperature 1.0, top_p 1.0 and a max_tokens budget of at least 256K. If you used top_p 0.95 with the earlier checkpoint, change it to 1.0.