DeepSeekLanguage model

DeepSeek V4 Flash Vision Exp

DeepSeek V4 Flash Vision Exp has no overall index value yet because there are too few independent benchmark results.

PopularityRank 225 of 785

43out of 100-3 vs. last month

based on search, usage and attention

Summary

DeepSeek V4 Flash Vision Exp was an experimental multimodal V4-Flash checkpoint for visual agents. Publisher metadata totals about 304.6 billion parameters and shows image-to-text processing through the V4 inference environment. DeepSeek presented it as a visual extension of the text model.

The first-party API route was discontinued on September 10, 2026, and routed to DeepSeek-V4.1-Flash. The released weights remain a separate experiment.

DeepSeek V4 Flash Vision Exp in our own test

SVG short test

One SVG task with the same fixed settings for every model, without hidden thinking.

DeepSeek V4 Flash Vision Exp

Earlier tests

Pelican on a bicycle

Prompt

Generate an SVG of a pelican riding a bicycle

Oct 1, 2026 · 66 s · $0.0050

Test details

Run date: Oct 1, 2026
API provider: openrouter
Requested API model: deepseek/deepseek-v4-flash-vision-exp
Reasoning setting: high
Token limit including reasoning: 8,192 tokens
Output tokens: 7,744 tokens
Cost of the attempt: $0.0050
Response time: 66.4 s
API model reported in response: deepseek/deepseek-v4-flash-vision-exp
Pinned endpoint: DeepInfra | deepseek/deepseek-v4-flash-vision-exp-20260821
Temperature: Provider default
Test protocol: community-visual-tests-v1
Technical checks: Passed, not a quality rating

Visually reviewed. This individual test does not establish overall model quality.

Task origin: Simon Willison

All benchmark results

Every published result with its source, test setup and comparison models.

Show all 63 published benchmark results

Measurements without matching peer values

These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.

ForceBench c@3 (Show measurement, test conditions, and source)

Source value: 52
Score: 52
Metric: Share of tasks solved within three attempts (c@3), with feedback after a failed attempt.
Unit: %
Benchmark version: v0.2.0
Direction: Higher is better
Lower confidence bound: 47
Upper confidence bound: 57
Source type: independent-evaluator
Evaluator: Forcebench
Status: active
Retrieved at: 2026-10-24
Methodology: Independent evaluation. Forcebench v0.2.0. Local board; experimental; high effort; FP8 + FP4 experts; vLLM on 2× NVIDIA DGX Spark (GB10, 128 GB each). The source lists one Local configuration. c@3 gives feedback from deployment, tests or tools after failure and allows up to three attempts. Shared method is zero-shot, one user turn and one reply per attempt, vendor-recommended thinking sampling settings, and a 32,768-token output budget including reasoning. The leaderboard warns that runs may be partial; 272 is the whole benchmark task count, not a per-model sample count.

Source Forcebench

Roboflow Vision Evals overall score (publisher-reported composite) (Show measurement, test conditions, and source)

Source value: 47.2
Score: 47.2
Metric: unweighted mean of the available task headline scores; low-effort main table
Unit: %
Benchmark version: Roboflow Vision Evals board updated 2026-10-26, low-effort main table
Category: vision
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Roboflow Vision Evals
Status: active
Retrieved at: 2026-10-28
Methodology: Independent evaluation. Exact leaderboard row `DeepSeek V4 Flash Vision Exp` is a literal display-name match to current profile ID `deepseek-deepseek-v4-flash-vision-exp`. This is Roboflow’s displayed low-effort Overall score. The publisher defines Overall Average as the unweighted mean of available task headline scores; this exact table value is copied, not recalculated from rounded task cells. The benchmark uses real images for visual understanding, not video understanding or generated-video quality.
Context: The leaderboard has no linked model detail page for this row; its model route, revision, and run status are not published. The main table does not publish a sample count or 95% confidence interval. Any published ± is half the observed run range, not a 95% confidence interval.

Source Roboflow Vision Evals

Roboflow Vision Evals, Object Detection (Show measurement, test conditions, and source)

Source value: 45.3
Score: 45.3
Metric: mAP@50
Unit: %
Benchmark version: Roboflow Vision Evals board updated 2026-10-26, low-effort main table
Category: vision
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Roboflow Vision Evals
Status: active
Retrieved at: 2026-10-28
Methodology: Independent evaluation. Exact leaderboard row `DeepSeek V4 Flash Vision Exp` is a literal display-name match to current profile ID `deepseek-deepseek-v4-flash-vision-exp`. The main-table Object Detection cell is the low-effort image-understanding result. Roboflow says it uses the same real-world image samples across models and tasks. This is visual understanding of images, not video understanding or generated-video quality. The leaderboard does not link a model detail page for this row, so the provider route and model revision are not published.
Context: Roboflow methodology describes the current protocol as three runs per task and effort tier, but says legacy rows remain single-run until rerun. This main table does not identify this row’s run status, so no run count is inferred. Any published ± is half the observed range, not a 95% confidence interval. This row has no linked model detail page in the leaderboard, so no provider route, model revision, or row-specific run status is available. The main table does not publish this task’s sample count, model revision, or serving route.

Source Roboflow Vision Evals

Roboflow Vision Evals, Counting (Show measurement, test conditions, and source)

Source value: 46
Score: 46
Metric: LLM-judged accuracy
Unit: %
Benchmark version: Roboflow Vision Evals board updated 2026-10-26, low-effort main table
Category: vision
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Roboflow Vision Evals
Status: active
Retrieved at: 2026-10-28
Methodology: Independent evaluation. Exact leaderboard row `DeepSeek V4 Flash Vision Exp` is a literal display-name match to current profile ID `deepseek-deepseek-v4-flash-vision-exp`. The main-table Counting cell is the low-effort image-understanding result. Roboflow says it uses the same real-world image samples across models and tasks. This is visual understanding of images, not video understanding or generated-video quality. The leaderboard does not link a model detail page for this row, so the provider route and model revision are not published.
Context: Roboflow methodology describes the current protocol as three runs per task and effort tier, but says legacy rows remain single-run until rerun. This main table does not identify this row’s run status, so no run count is inferred. Any published ± is half the observed range, not a 95% confidence interval. This row has no linked model detail page in the leaderboard, so no provider route, model revision, or row-specific run status is available. The main table does not publish this task’s sample count, model revision, or serving route.

Source Roboflow Vision Evals

Roboflow Vision Evals, Identification (Show measurement, test conditions, and source)

Source value: 68.8
Score: 68.8
Metric: LLM-judged accuracy
Unit: %
Benchmark version: Roboflow Vision Evals board updated 2026-10-26, low-effort main table
Category: vision
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Roboflow Vision Evals
Status: active
Retrieved at: 2026-10-28
Methodology: Independent evaluation. Exact leaderboard row `DeepSeek V4 Flash Vision Exp` is a literal display-name match to current profile ID `deepseek-deepseek-v4-flash-vision-exp`. The main-table Identification cell is the low-effort image-understanding result. Roboflow says it uses the same real-world image samples across models and tasks. This is visual understanding of images, not video understanding or generated-video quality. The leaderboard does not link a model detail page for this row, so the provider route and model revision are not published.
Context: Roboflow methodology describes the current protocol as three runs per task and effort tier, but says legacy rows remain single-run until rerun. This main table does not identify this row’s run status, so no run count is inferred. Any published ± is half the observed range, not a 95% confidence interval. This row has no linked model detail page in the leaderboard, so no provider route, model revision, or row-specific run status is available. The main table does not publish this task’s sample count, model revision, or serving route.

Source Roboflow Vision Evals

Roboflow Vision Evals, OCR (Show measurement, test conditions, and source)

Source value: 44
Score: 44
Metric: deterministic composite OCR score
Unit: %
Benchmark version: Roboflow Vision Evals board updated 2026-10-26, low-effort main table
Category: vision
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Roboflow Vision Evals
Status: active
Retrieved at: 2026-10-28
Methodology: Independent evaluation. Exact leaderboard row `DeepSeek V4 Flash Vision Exp` is a literal display-name match to current profile ID `deepseek-deepseek-v4-flash-vision-exp`. The main-table OCR cell is the low-effort image-understanding result. Roboflow says it uses the same real-world image samples across models and tasks. This is visual understanding of images, not video understanding or generated-video quality. The leaderboard does not link a model detail page for this row, so the provider route and model revision are not published.
Context: Roboflow methodology describes the current protocol as three runs per task and effort tier, but says legacy rows remain single-run until rerun. This main table does not identify this row’s run status, so no run count is inferred. Any published ± is half the observed range, not a 95% confidence interval. This row has no linked model detail page in the leaderboard, so no provider route, model revision, or row-specific run status is available. The main table does not publish this task’s sample count, model revision, or serving route.

Source Roboflow Vision Evals

Roboflow Vision Evals, Reasoning (Show measurement, test conditions, and source)

Source value: 31.8
Score: 31.8
Metric: LLM-judged accuracy
Unit: %
Benchmark version: Roboflow Vision Evals board updated 2026-10-26, low-effort main table
Category: vision
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Roboflow Vision Evals
Status: active
Retrieved at: 2026-10-28
Methodology: Independent evaluation. Exact leaderboard row `DeepSeek V4 Flash Vision Exp` is a literal display-name match to current profile ID `deepseek-deepseek-v4-flash-vision-exp`. The main-table Reasoning cell is the low-effort image-understanding result. Roboflow says it uses the same real-world image samples across models and tasks. This is visual understanding of images, not video understanding or generated-video quality. The leaderboard does not link a model detail page for this row, so the provider route and model revision are not published.
Context: Roboflow methodology describes the current protocol as three runs per task and effort tier, but says legacy rows remain single-run until rerun. This main table does not identify this row’s run status, so no run count is inferred. Any published ± is half the observed range, not a 95% confidence interval. This row has no linked model detail page in the leaderboard, so no provider route, model revision, or row-specific run status is available. The main table does not publish this task’s sample count, model revision, or serving route.

Source Roboflow Vision Evals

Terminal Bench 2.1 (Show measurement, test conditions, and source)

Source value: 83.9
Score: 83.9
Metric: score
Unit: No unit provided
Benchmark version: Terminal Bench 2.1
Category: text-agent
Direction: Higher is better
Source type: vendor-reported
Evaluator: DeepSeek V4 Flash Vision Exp official model card
Status: active
Retrieved at: 2026-10-15
Methodology: Vendor-reported result. Exact DeepSeek-V4-Flash-Vision-Exp (text-agent evaluation) table row. The card states text-agent results use minimal DeepSeek Harness, max reasoning effort, temperature 1.0, top-p 0.95. Benchmark prompt, sampling/decoding settings, reasoning effort, quantization, and task run date are not stated in the cited result; none are inferred.
Context: The source presents a raw score without an explicit unit or scale; the published value is retained without percentage conversion.

Source DeepSeek V4 Flash Vision Exp official model card

NL2Repo (Show measurement, test conditions, and source)

Source value: 57.7
Score: 57.7
Metric: score
Unit: No unit provided
Benchmark version: NL2Repo
Category: text-agent
Direction: Higher is better
Source type: vendor-reported
Evaluator: DeepSeek V4 Flash Vision Exp official model card
Status: active
Retrieved at: 2026-10-15
Methodology: Vendor-reported result. Exact DeepSeek-V4-Flash-Vision-Exp (text-agent evaluation) table row. The card states text-agent results use minimal DeepSeek Harness, max reasoning effort, temperature 1.0, top-p 0.95. Benchmark prompt, sampling/decoding settings, reasoning effort, quantization, and task run date are not stated in the cited result; none are inferred.
Context: The source presents a raw score without an explicit unit or scale; the published value is retained without percentage conversion.

Source DeepSeek V4 Flash Vision Exp official model card

CyberGym (Show measurement, test conditions, and source)

Source value: 75.3
Score: 75.3
Metric: score
Unit: No unit provided
Benchmark version: CyberGym
Category: text-agent
Direction: Higher is better
Source type: vendor-reported
Evaluator: DeepSeek V4 Flash Vision Exp official model card
Status: active
Retrieved at: 2026-10-15
Methodology: Vendor-reported result. Exact DeepSeek-V4-Flash-Vision-Exp (text-agent evaluation) table row. The card states text-agent results use minimal DeepSeek Harness, max reasoning effort, temperature 1.0, top-p 0.95. Benchmark prompt, sampling/decoding settings, reasoning effort, quantization, and task run date are not stated in the cited result; none are inferred.
Context: The source presents a raw score without an explicit unit or scale; the published value is retained without percentage conversion.

Source DeepSeek V4 Flash Vision Exp official model card

DeepSWE (Show measurement, test conditions, and source)

Source value: 59.3
Score: 59.3
Metric: score
Unit: No unit provided
Benchmark version: DeepSWE
Category: text-agent
Direction: Higher is better
Source type: vendor-reported
Evaluator: DeepSeek V4 Flash Vision Exp official model card
Status: active
Retrieved at: 2026-10-15
Methodology: Vendor-reported result. Exact DeepSeek-V4-Flash-Vision-Exp (text-agent evaluation) table row. The card states text-agent results use minimal DeepSeek Harness, max reasoning effort, temperature 1.0, top-p 0.95. Benchmark prompt, sampling/decoding settings, reasoning effort, quantization, and task run date are not stated in the cited result; none are inferred.
Context: The source presents a raw score without an explicit unit or scale; the published value is retained without percentage conversion.

Source DeepSeek V4 Flash Vision Exp official model card

Toolathlon-Verified (Show measurement, test conditions, and source)

Source value: 75.9
Score: 75.9
Metric: score
Unit: No unit provided
Benchmark version: Toolathlon-Verified
Category: text-agent
Direction: Higher is better
Source type: vendor-reported
Evaluator: DeepSeek V4 Flash Vision Exp official model card
Status: active
Retrieved at: 2026-10-15
Methodology: Vendor-reported result. Exact DeepSeek-V4-Flash-Vision-Exp (text-agent evaluation) table row. The card states text-agent results use minimal DeepSeek Harness, max reasoning effort, temperature 1.0, top-p 0.95. Benchmark prompt, sampling/decoding settings, reasoning effort, quantization, and task run date are not stated in the cited result; none are inferred.
Context: The source presents a raw score without an explicit unit or scale; the published value is retained without percentage conversion.

Source DeepSeek V4 Flash Vision Exp official model card

Page 1 of 6

Popularity compared

How often the model is searched for, used and mentioned, compared with every model of its category.

Popularity index

The 100 most popular models of this category, 0 to 100, higher is more popular

20 of 101 models

GPT-6 Astra83

Claude Haiku 5.575

GPT-6 Sol73

GPT-573

DeepSeek V4.1 Flash 101769

Kimi K367

MiniCPM-V 4.7 35B-A3B63

GPT-6.1 Sol63

Claude Opus 563

Qwen 3.8 Flash62

DeepSeek V4 Flash Vision Exp43

DeepSeek V4.1 Flash 1017

MiniCPM-V 4.7 35B-A3B

GPT-6.1 Sol

Claude Opus 5

Qwen 3.8 Flash

DeepSeek V4 Flash Vision…

As of November 2026 · Source: gradually.ai popularity index

Over time

Weekly values from October 18, 2026 to November 8, 2026

DeepSeek V4 Flash Vision ExpMixtral 8x22B 43Laguna XS 2.1 43Llama 3.1 70B Instruct 43Qwen 3.5 35B A3B 43

Method changes in the timeline, a jump at these points is no market event:

1November 1, 2026: Value only with search or usage data, 30-day attention
2November 1, 2026: Method version 4 (common scale, shrinkage)

What is gaining popularity right now, across models, vendors, harnesses and AI subscriptions, is collected in All AI trends.

Similar models

More models of the same kind as DeepSeek V4 Flash Vision Exp, chosen by model line, developer, size, price and release date.

All specifications

Architecture

Parameters: 305 billion; Source: Parameters

Input and output

Context window: 1,000,000 tokens; Source: Context window
Maximum output: 384,000 tokens; Source: Maximum output
Inputs: Text, Image; Source: Inputs
Outputs: Text; Source: Outputs
Reasoning: Yes; Source: Reasoning
Tool calling: Yes; Source: Tool calling

Watermarking and provenance

AI watermarking refers to invisible signals embedded in the output. Visible marks and provenance metadata are listed separately. Unknown means there is insufficient evidence for that particular field.

AI watermarking: Unknown; no source
Watermarking technique: Not known; no source

Licence and access

Weights: Open weights; Source: Weights
Access: API; Source: Access
License: MIT; Source: License
Shutdown: DeepSeek API: retired, no date published (weights remain available); Source: Shutdown

Prices

Not included in any subscription in Germany.

All plans in the AI subscription comparison

Frequently asked questions about DeepSeek V4 Flash Vision Exp

How large is the context window of DeepSeek V4 Flash Vision Exp?

1 M tokens. That is how much text DeepSeek V4 Flash Vision Exp reads at once, about 1,500 A4 pages.

Page count as a rule of thumb from Artificial Analysis

Context windowTokens the model reads at once · higher is better

DeepSeek V4 Flash Vision ExpRank 31 of 1031M

Claude Opus 5.51M

Gemini 4 Carbon1M

Claude Fable 5.11M

Claude Sonnet 5.51M

Muse Wonder Full1M

Claude Opus 51M

Can I run DeepSeek V4 Flash Vision Exp locally?

Yes, the weights are open. With 305B parameters it needs a lot of memory, more a server than a laptop.

Stated by DeepSeek

Open weights: yes
License: MIT
Parameters: 305B
active per token: n/a

Sources and data date

Show all 20 sources

Benchmark sources at a glance

Which independent evaluators tested this model. This is coverage, not a quality ranking.

Evaluator	Test series	Composite indices	Recorded active measurements
LiveBench	0	0	38

Without vendor claims and historical or disputed results; those stay labelled in the single values. 38 measurements are not yet assigned to a test series.

Type	Evidence and data date
Input	Documented modalities. Retrieved October 27, 2026. Source: models.dev catalog · models.dev catalog (retrieved October 27, 2026) · DeepSeek model catalog snapshot · Editorial description reviewed October 2, 2026
Output	Documented modalities. Retrieved October 27, 2026. Source: models.dev catalog · models.dev catalog (retrieved October 27, 2026) · DeepSeek model catalog snapshot · Editorial description reviewed October 2, 2026
Research status	Research date October 15, 2026. 12 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Not published	task-run-dates-and-multimodal-setup Note The card states setup for text-agent tests only. It does not give the date/decoding protocol for the multimodal-agent table; units remain unlabeled.
Additional source	Hugging Face (retrieved October 15, 2026) · DeepSeek V4 Flash Vision Exp model card · Editorial description reviewed October 2, 2026
Additional source	LiveBench (retrieved October 16, 2026)1 (unavailable on November 2, 2026) https://livebench.ai/table_2026_09_02.csv2 (unavailable on November 2, 2026) https://livebench.ai/breakdown_2026_09_02.json
Additional source	u/Correct_Tomato1871, self-reported MindTrial run (retrieved October 18, 2026)
Additional source	PixelML CMP 170HX experiment (retrieved October 21, 2026)
Additional source	Forcebench (retrieved October 24, 2026)
Additional source	Roboflow Vision Evals (retrieved October 28, 2026)
Additional source	Model metadata (retrieved October 16, 2026)12 (unavailable on November 2, 2026) https://livebench.ai/categories_2026_09_02.json3 (unavailable on November 2, 2026) https://livebench.ai/notes_2026_09_02.json4 (unavailable on November 2, 2026) https://livebench.ai/static/js/main.d1b91202.js
Additional source	DeepSeek V4 Flash Vision Exp repository metadata · Editorial description reviewed October 2, 2026
Additional source	DeepSeek V4.1 lifecycle notice · Editorial description reviewed October 2, 2026
Additional source	OpenRouter: Popularity index (derived), CC BY 4.0
Additional source	Vercel AI Gateway: Popularity index (derived), CC BY 4.0
Additional source	Ollama: cumulative downloads for the popularity index
Additional source	Replicate: cumulative runs for the popularity index
Additional source	Google Trending Now: limited feed selection for Germany and the US
Additional source	Creative Commons Attribution 4.0