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DeepSeek V4.1-Flash brings native image understanding to an open-weight reasoning model. For enterprises, it opens a useful evaluation question: can one model handle visual evidence and complex text at an acceptable cost per verified result?
All Model Families →Introduction transcript. Qubi: Some work needs the words and the picture: a chart, a screenshot, a diagram. This open model wakes only a few of its many experts for each request. We price the whole answer: retries, corrections, and the time it takes to check. Then we replay your real work on it, and a person decides before traffic moves.
The DeepSeek release this page leads with is DeepSeek V4.1-Flash (deepseek-flash), released 10 September 2026. When we last reviewed it, DeepSeek's other current models were DeepSeek V4-Pro-0813.
Reviewed against DeepSeek's own documentation on 19 September 2026 (source). We check the public model catalogues every week; when a new release appears, we review it against the provider's announcement and update this page.
What DeepSeek has shipped most recently. Each has its own page with specifications, independent ratings at every reasoning setting and listed prices.
Sort by any column. Select a model for its full page.
| DeepSeek V4.1 Flash 1017 open | 17 Oct 2026 | 32.5 | 159 | 1.05M | $0.150 | $0.600 |
|---|---|---|---|---|---|---|
| DeepSeek V4.1 Flash open | 10 Sep 2026 | — | — | 1.05M | $0.045 | $0.500 |
| DeepSeek V4 Pro 0813 open | 13 Aug 2026 | 23.1 | 77 | 1.05M | $0.660 | $1.98 |
| DeepSeek V4 Flash 0731 open | 31 Jul 2026 | — | — | 1.05M | $0.119 | $0.238 |
| DeepSeek V4 Flash 0423 open | 31 Jul 2026 | — | — | 1.05M | $0.091 | $0.182 |
| DeepSeek V4 Pro 0423 open | 13 Aug 2026 | 23.1 | 77 | 1.05M | $0.948 | $1.90 |
| DeepSeek V3.2 open | 1 Dec 2025 | — | — | 164k | $0.260 | $0.380 |
| DeepSeek V3.1 Terminus open | 22 Sep 2025 | — | — | 164k | $0.270 | $1.00 |
| DeepSeek V3.1 open | 21 Aug 2025 | — | — | 164k | $0.250 | $0.950 |
| R1 0528 open | 28 May 2025 | — | — | 164k | $0.500 | $2.15 |
| DeepSeek V3 0324 open | 25 Mar 2025 | — | — | 164k | $0.290 | $1.14 |
| DeepSeek V3 open | 26 Dec 2024 | — | — | 164k | $0.257 | $1.03 |
Intelligence and coding: Artificial Analysis indices at the model's best reasoning setting, and its measured output speed. Context and prices: the OpenRouter catalogue list price in USD per million tokens. Released: the provider's date where Artificial Analysis records one, otherwise the date the catalogue first listed it. Refreshed weekly; not measured by QQuantum.ai. Select a heading to sort.
Released on 10 September 2026, V4.1-Flash is a multimodal mixture-of-experts model. DeepSeek describes a causal encoder–decoder with 8B active parameters for input and 16B for output. Its launch reports lower KV-cache memory requirements than the previous generation. That is a claim about inference memory, not a promise that your application runs four times faster. DeepSeek launch announcement.
The practical opportunity is a document workflow that needs both the words and the picture: a chart in a report, a screenshot attached to a support case, or a diagram alongside a maintenance note. The model can propose an interpretation; the application still needs to verify important values against the original evidence.
Provider-published specifications as of 19 September 2026, not figures we measured. Model families move quickly — check the provider's own documentation before committing to a number.
Sources: official model card and licence; current API specifications. The backbone count is the model-card figure, not a hardware-sizing estimate.
Third-party scores and listed prices, refreshed every week. Useful for building a shortlist; your own evaluation decides.
Composite index across their reasoning, knowledge, maths and coding evaluations · Higher is better · Linear scale from 0 to 66.0
This family's highest-rated models, highlighted, against the best-rated model of each other family we cover. One composite score cannot show cost, latency, data residency or how a model handles your task.
Source: Artificial Analysis. Checked 30 October 2026. Measured independently by Artificial Analysis, not by QQuantum.ai or the providers.
USD per million output tokens · The newest releases in the family · Linear scale from 0 to $3.00
Input is priced separately: DeepSeek V4.1 Flash 1017 $0.15, DeepSeek V4.1 Flash $0.05, DeepSeek V4 Pro 0813 $0.66, DeepSeek V4 Flash 0731 $0.12, DeepSeek V4 Flash 0423 $0.09 per million tokens. Catalogue list prices; the provider's own rates, caching and batch discounts can differ.
Source: OpenRouter model catalogue. Checked 30 October 2026. Listed by OpenRouter, not measured by QQuantum.ai.
USD per million input tokens · Peak rate · Linear scale from 0 to $1.50
Same provider and token category. Cache hits and off-peak traffic have separate rates. This is a price comparison, not a quality or speed benchmark.
Source: DeepSeek pricing. Checked 19 September 2026. Provider-reported, not independently measured by QQuantum.ai.
USD per million output tokens · Peak rate · Linear scale from 0 to $4.00
Compare complete token usage, including reasoning and retries. A lower rate does not establish a lower cost per accepted result.
Source: DeepSeek pricing. Checked 19 September 2026. Provider-reported, not independently measured by QQuantum.ai.
Illustrative calculation: a batch totalling 10 million uncached input tokens and 1 million output tokens costs $4.20 on Flash or $17.16 on Pro at these peak rates. Calculation: input millions × input rate + output millions × output rate. This assumes equal token counts; it excludes tools, retries, infrastructure, tax and human review. Off-peak prices are half the peak rates. Pricing and current peak-hour schedule.
For a real decision, replay the same work on both models. Count rejected answers, additional reasoning, retries and manual corrections. A cheaper answer that takes longer to check can be the more expensive outcome. Our Jev model-router project applies this principle at request level: choose an eligible model, then verify the result.
DeepSeek recommends deepseek-flash. The old V4-Flash and V4-Flash-Vision-Exp aliases now resolve to V4.1-Flash. The current documentation also says V4 Pro remains available after 14 September, superseding the original launch plan to reroute it. Current API migration notice.
Treat an alias change as a model change: replay representative prompts, verify image handling and structured outputs, and compare the returned usage. API-format compatibility does not prove identical tool behaviour. Keep the previous approved route available until the acceptance criteria pass.
Self-hosting is a separate infrastructure project. Active parameters do not tell you how much memory the full deployment needs. Size weights, context cache, concurrency and the serving stack together. Confirm runtime support for this exact release before buying hardware.
Model provenance, provider jurisdiction and data flow are separate decisions. For a hosted service, review the actual processing locations, retention terms and contract. For self-hosting, inspect outbound connections, telemetry, tools and update mechanisms; hosting weights locally alone does not prove that the whole application is isolated.
Pin the model artefact, retain its licence, and evaluate behaviour on the topics your customers actually ask about. Our model supply-chain approach covers that review. If the organisation cannot approve the provider or operate the serving infrastructure, choose another eligible model before comparing scores.
No. The September release is V4.1-Flash. Current API documentation lists V4-Pro-0813 separately and says Pro remains available after 14 September.
The official model card describes image and text input with text output. This is visual understanding, not an image-generation service.
Start with a replay evaluation on your own tasks. Compare accepted results, response times, total spend and review effort before changing live traffic.
What we built, what stage it is at, and a link to the live thing wherever there is one.
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Bring representative documents or requests. We will scope an evaluation around quality, data constraints and cost per accepted result.