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DeepSeek V4.1-Flash-1017, released on October 17, 2026, is a minor update that DeepSeek says improves reasoning, coding, agentic and visual workloads while reducing execution time and output length. The practical result is a better performance-to-cost trade-off than the previous V4.1-Flash, with the API available under the model name deepseek-flash.
What changed in V4.1-Flash-1017
DeepSeek positions 1017 as an incremental refresh rather than a new architecture generation. Its release statement describes “stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost.” The model card attributes the gains to stronger reasoning, coding, agentic and visual performance, while also reporting shorter outputs and lower task-execution time.
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The model belongs to a 552-billion-parameter mixture-of-experts family. DeepSeek says 8 billion parameters are active for input processing and 16 billion for output generation. It also reports that V4.1-Flash uses one-quarter of the previous generation’s HBM capacity for its key-value cache and one-eighth of the SSD storage, which can matter when serving many concurrent sessions.
Published benchmark results
The official model card reports these scores for V4.1-Flash-1017:
| Benchmark | 1017 score | What it tests |
|---|---|---|
| Terminal-Bench 4.0 | 42.3 | Terminal-based software tasks |
| DeepSWE v1.1 | 77.0 | Software-engineering agents |
| ProgramBench | 29.1 | Programming tasks |
| NL2Repo-Bench | 68.2 | Natural-language-to-repository work |
| Automation-Bench v1.0.6 | 60.5 | Computer-use automation |
| Agents’ Last Exam | 35.4 | Agentic knowledge and execution |
| Apex Bench v1.1 | 72.1 | Agent performance |
| Chartography with tools | 83.0 | Chart and tool-use tasks |
| BabyVision with tools | 92.5 | Visual understanding with tools |
| ZeroBench-main | 51.0 | General zero-shot evaluation |
Selected figures in DeepSeek’s comparison table show where the update leads and where it does not:
| Benchmark | V4.1-Flash-1017 | Previous V4.1-Flash | GPT-5.6 Sol | Opus 5 |
|---|---|---|---|---|
| DeepSWE v1.1 | 77.0 | 74.2 | 73.0 | 74.0 |
| Terminal-Bench 4.0 | 42.3 | 31.2 | 39.9 | 51.8 |
On DeepSWE v1.1, 1017 is ahead of the previous Flash, GPT-5.6 Sol and Opus 5 in the published table. Terminal-Bench 4.0 tells a different story: 1017 improves substantially over the previous Flash and edges GPT-5.6 Sol, but remains below Opus 5. These are publisher-reported results, not an independent replication.
Why the test setup matters
Terminal-Bench 4.0 was run with the Claude Code harness, while DeepSWE v1.1 used Mini-SWE-V2. Humanity’s Last Exam values marked with a dagger in the official table come from the text-only subset. A score therefore describes a particular benchmark, harness, subset and metric—not a universal ranking of coding agents.
API capabilities and limits
For hosted use, the current API model identifier is deepseek-flash; the pricing page identifies that endpoint as DeepSeek-V4.1-Flash-1017. The API provides:
- 1-million-token context window
- Maximum output of 384,000 tokens
- JSON output
- Tool calls
- Responses API compatibility
- Anthropic API compatibility
- Native vision input
- Concurrency limit of 2,500
Vision and tool support make the model suitable for agents that inspect screenshots, charts or documents while taking actions through external functions. The context and output ceilings are limits, not guarantees that every request will be accepted at maximum size; your client, account and workload still need to stay within service limits.
Token pricing and per-task cost
DeepSeek charges separately for cached input, cache-miss input and output. Rates depend on UTC time:
| Token type | Off-peak | Peak |
|---|---|---|
| Cached input | $0.003 per 1M tokens | $0.006 per 1M tokens |
| Cache-miss input | $0.15 per 1M tokens | $0.30 per 1M tokens |
| Output | $0.60 per 1M tokens | $1.20 per 1M tokens |
Peak pricing applies from 01:00–04:00 and 06:00–10:00 UTC, Monday through Friday, excluding Chinese public holidays. All other hours, including weekends and Chinese public holidays, are off-peak.
Calculating a task
Use this formula: (cached input × cached rate) + (cache-miss input × cache-miss rate) + (output × output rate), with token counts divided by 1,000,000.
For example, an off-peak request using 100,000 cache-miss input tokens and 20,000 output tokens costs $0.027: $0.015 for input plus $0.012 for output. The same request at peak rates costs $0.054. A workload that reuses 100,000 cached input tokens instead would pay $0.0003 for that cached portion at off-peak pricing. Actual agent runs can make multiple calls, so multiply the calculation by the number of model turns and include any search or fetch requests.
Search, Fetch and server-side billing
The October 17 release also announces Search and Fetch APIs priced at $0.75 per 1,000 successful requests. Calls made server-side through DeepSeek Harness, Claude Code or Codex are currently free for DeepSeek API users, but billing is scheduled to begin on October 31, 2026, at 16:00 UTC. Teams with automated agents should account for that date when estimating recurring costs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you run it locally?
Yes. DeepSeek documents an open-weight route through Hugging Face and ModelScope. The Hugging Face repository is marked MIT licensed and includes serving examples for vLLM, SGLang and Docker, including OpenAI-compatible local endpoints.
Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →- Obtain the open-weight model files from the documented Hugging Face or ModelScope repository.
- Choose a supported serving stack such as vLLM, SGLang or the supplied Docker configuration.
- Expose the local OpenAI-compatible endpoint and point your client or agent framework at that endpoint.
- Validate memory use, context length, batching, latency and tool-call behavior with your own workload before production use.
The published architecture figures explain why deployment economics vary: although the family has 552B total parameters, only 8B input and 16B output parameters are active per token, and the model uses reduced HBM for its KV cache. The documentation does not provide a single hardware requirement that fits every quantization, context length or batch size, so self-hosting cost depends on GPU memory, storage, power, orchestration and engineering time.
How it compares with V4 Pro and other models
There is no directly published V4 Pro comparison in the supplied official results, so the benchmark table cannot establish that 1017 is better or worse for every coding-agent workload. For a real choice, run both models through the same agent harness, repository set, tool permissions, timeout and success metric.
Against the named rivals in DeepSeek’s table, 1017’s clearest published strengths are DeepSWE v1.1, several automation evaluations and the two visual-with-tools tests. Its Terminal-Bench result is competitive but not the table leader. Compare models on task success, harness, context and output limits, tool and vision support, runtime, cache behavior, peak schedule, token price, deployment control and license—not on a single raw score.
Who should use V4.1-Flash-1017?
- API users running many agent turns: low off-peak token rates, caching and a 2,500 concurrency limit can make it attractive when your prompts repeat substantial context.
- Coding-agent teams: the DeepSWE and NL2Repo results justify testing it on repository tasks, but Terminal-Bench performance shows that harness choice still matters.
- Vision-and-tool workflows: native vision, JSON output and tool calls support agents that combine images or charts with actions.
- Operators seeking control: the MIT-licensed open-weight release and vLLM, SGLang and Docker examples provide a self-hosting path, subject to your infrastructure budget.
Prices, model routing, files and billing policies can change, so confirm the live API documentation and model repository before committing a production system.
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