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ModelCap

DeepSeek V4 Flash Vision Exp

Official publisher releaseOpen weights

DeepSeek·#31 of 337 on the current board·cheaper than #30 DeepSeek V4 Pro 0813

DeepSeek V4 Flash Vision Exp is a language model from DeepSeek. It ranks #31 of 337 on ModelCap's current board, with an Index score of 72.9, modeled from the publisher's launch results against measured models. Its score range of 64.5–81.3 spans positions #13–#62. It is listed at $0.216 per million input tokens and $0.647 per million output tokens across 4 providers. It reads up to 1M tokens of context, and its weights are openly downloadable.

Figures as of 6 Oct 2026, 17:18 UTC · download the public dataset · methodology

ModelCap Index · Current board
72.9#31
Modeled from the publisher's launch results against measured models · range 64.5–81.3
Market Gravity
44.7Decreased by 0.4
Secondary market signal
Input
$0.216
per 1M tokens
Output
$0.647
per 1M tokens
Context
1M
tokens
Providers
4
endpoints

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
DeepInfrafp8Cheapest$0.216$0.6471Mfp899.75%
GMICloudfp8$0.44$1.321Mfp899.87%
Novita$0.44$1.321M—100.0%
SiliconFlowfp8$0.44$1.321Mfp8100.0%

Hugging Face Inference Providers

Exact repository identity

Provider economics and telemetry published by the Hugging Face Router. These are operational signals, not benchmark results.

ProviderInputOutputContextTTFTThroughputFeatures
deepinfra
live
$0.44$1.321M544 ms84.3 tok/stools · structured
novita
live
$0.44$1.321M594 ms128.5 tok/stools

Reported evaluation leads (3)

Not independent measurement

Structured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. Rows marked as used were laddered against independently measured peers inside the launch-card channel of this model's modeled placement, with the replay-measured optimism haircut applied; the others played no part.

Dataset / taskMetricValueRevisionProvenance
datacurve/deep-swe
deep_swe
—59.3
Unpinned
not published
Reported
Used in launch estimate
harborframework/terminal-bench-2.1
terminalbench_2_1
—83.9
Unpinned
not published
Reported
Used in launch estimate
hkust-nlp/Toolathlon
toolathlon_verified
—75.9
Unpinned
not published
Reported
Not used in launch estimate

ModelCap Index · Current board

Methodology
72.9
Modeled from the publisher's launch results against measured models
Current board
#31 of 337
Score range
64.5–81.3
Rank range
#13–#62
Technical details
Public rank basis
Modeled · peer benchmarks (architecture)
Evidence label
Modeled · peer benchmarks · publisher-corpus-prior over 232 held-out anchors (8%); launch card against 2 resolved peers on 7 rows (92%); exceeds every named peer on 1 of 7 rows; no cross-lab optimism probe available; shrunk 1.1 toward the measured corpus
Index support
33.0% · limited
Identity binding
Exact catalogue product · deepseek/deepseek/deepseek-v4-flash-vision-exp/fp8 · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata deepseek-ai/DeepSeek-V4-Flash-Vision-Exp@6821d6ad3681a4b137b066b76094fa82ebd0a380 (not the evaluation revision)
Index observations used
0
Qualified Index evidence point
72.9
How this position was produced · fused launch placement

No general-family board lists this model yet. Its point estimate is the precision-weighted mean of every channel that spoke for it; each channel’s share is the weight it carried. Independent measurements replace this placement as boards list the model.

Fused estimate
72.9 ± 6.6
Independent share
0%
Shrink toward corpus
-1.1
Peer ceiling share
14%
Optimism haircut
None applied
Method
precision-weighted-v1
Channels fused into this model’s launch placement
ChannelEstimateShare
Publisher corpus prior · not independent
publisher-corpus-prior over 232 held-out anchors
60.7 ± 22.88%
Launch card against resolved peers · not independent
launch card against 2 resolved peers on 7 rows
74.0 ± 6.992%
  • · Launch card row automationbench: inside the resolved peer range
  • · Launch card row cybergym: claims below every resolved peer
  • · Launch card row deepswe: claims above every resolved peer
  • · Launch card row dsbench-hard: inside the resolved peer range
  • · Launch card row nl-2-repo: inside the resolved peer range
  • · Launch card row terminal-bench-2-1: inside the resolved peer range
  • · Launch card row toolathlon-verified: inside the resolved peer range
Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability
—
Evidence status
No admitted measured evidence
General preference
—
Coding
—
Agents & tools
—
Reasoning
—
Evidence breadth
0%
Evidence coverage
0%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
23.7
Liquidity (providers × uptime)
65.5
Open reach (HF downloads)
73.6
Surface (context / tools / modalities)
100.0
Freshness
83.9
  • · OpenRouter popularity #86
  • · 4 live providers
  • · HF 30d downloads 835578
  • · Catalogue: context 1048576, reasoning, tools, modalities Text/Image
  • · First seen 2026-08-21

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Experimental capability estimate

Estimate policy
72.2
57.5–86.9 uncertainty interval
Excluded from rank
Confidence
76%
Training support
242 models · 138 lineages
Out-of-domain check
in domain
Feature coverage
100%
Lineage-held-out error
9.1 MAE

Experimental estimate. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.

Deployment readiness

Metadata index
75.5
strong · 76% metadata coverage
Not capability
Artifact reproducibility
85.0
Access & legal clarity
100.0
Deployability
100.0
Evaluation provenance
0.0

Not yet published: the base model it derives from, admitted benchmark results, results across several capability areas, results from more than one source, a confident match to its benchmark entries, pinned evaluation dataset versions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage
Market Gravity
44.7
OpenRouter weekly popularity
#86 of 343

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

No matched Arena rating

This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.

Market Gravity breakdown

Usage55%
23.7

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
65.5

Independent providers versus the model's open or closed cohort, weighted by uptime.

Open reach15%
73.6

Hugging Face 30-day downloads for open models; neutral for closed models.

Freshness5%
83.9

Time since first public availability, on a six-month half-life.

Specification

Model ID
deepseek/deepseek-v4-flash-vision-exp
Context window
1M tokens
Declared output cap
262K tokens

OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. OpenRouter metadata

Inputs
Text, Image
Outputs
Text
Tokenizer
DeepSeek
Cached input tokens
$0.0069 / 1M
First seen on OpenRouter
21 Aug 2026

Capabilities

  • Supported: Reasoning
  • Supported: Tool use
  • Supported: Structured output
  • Supported: Response format
  • Not supported: Moderated

Weights & access

Open weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.

Access
Open weights
Licence
mit
Pinned revision
6821d6ad3681
Parameters
304.6B
Architecture
DeepseekV4ForCausalLM
Model type
deepseek_v4
Hugging Face downloads (30d)
836K
Downloads (all time)
1.1M
Likes
932
Repository updated
1 Sept 2026, 09:22 UTC

Alternatives to DeepSeek V4 Flash Vision Exp, by the numbers

Ranked neighbours

Models within 3 places of #31 on the ModelCap Index.

Cheaper at a similar or higher Index

Index score within 3 points of, or above, this model's, with a lower listed blended price (3:1 input:output).

Other ranked DeepSeek models

The same publisher's other current models with a public Index position.

DeepSeek V4 Flash Vision Exp: common questions

How does DeepSeek V4 Flash Vision Exp rank among AI models?

DeepSeek V4 Flash Vision Exp holds ModelCap Index position #31 of 337 ranked language models as of 6 October 2026, with an Index score of 72.9 (interval 64.5–81.3); evidence: modeled · peer benchmarks. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

How much does DeepSeek V4 Flash Vision Exp cost per 1M tokens?

DeepSeek V4 Flash Vision Exp is listed at $0.216 per 1M input tokens and $0.647 per 1M output tokens as of 6 October 2026 across 4 providers. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $0.755 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.

What is the context window of DeepSeek V4 Flash Vision Exp?

DeepSeek V4 Flash Vision Exp has a published context window of 1M tokens (1,048,576). The snapshot lists a 262,144 token catalogue-declared route output cap. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. The providers table lists each endpoint's context limit.

Is DeepSeek V4 Flash Vision Exp open-weight?

Open weights (mit). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is deepseek-ai/DeepSeek-V4-Flash-Vision-Exp on Hugging Face.

Which API providers serve DeepSeek V4 Flash Vision Exp?

4 providers serve DeepSeek V4 Flash Vision Exp through OpenRouter as of 6 October 2026: DeepInfra, GMICloud, SiliconFlow and Novita. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has DeepSeek V4 Flash Vision Exp been evaluated on?

DeepSeek V4 Flash Vision Exp's Index position rests on the evidence listed on this page; it has no result on the public boards ModelCap tracks as separate leaderboards as of 6 October 2026. The evidence panel names each source and its publication date.

When was DeepSeek V4 Flash Vision Exp released?

DeepSeek V4 Flash Vision Exp first appeared in the catalogue on 21 Aug 2026. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.