DeepSeek V4 Flash 0731 API model profile

Evidence snapshot:

DeepSeek V4 Flash 0731 is a DeepSeek model profile with a 1,310,720-token context window. The dated catalog snapshot records text modalities and interfaces for coding, prompt caching, reasoning, reasoning controls, structured output.

DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows. This is the GA release of DeepSeek V4 Flash.

DeepSeek V4 Flash 0731 is worth reaching for when you need a route priced in the cheapest quarter of the catalog and carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support.

DeepSeek V4 Flash 0731 is available under the Kendr alias kc-deepseek-v4-flash-0731. Current account availability and customer credit quotes come from Kendr's live public model API and applicable account policy.

DeepSeek V4 Flash 0731 accepts text and returns text. The snapshot records coding, prompt caching, reasoning, reasoning controls, structured output, text generation, tool calling, tools, and web search as capabilities or interfaces.

The dated reference snapshot lists $0.045 input and $0.09 output per million tokens. Cached input is $0.0028 per million tokens. The separately published provider-route reference is $0.20 / $0.40 ($0.04/M implicit cached input).

The 2026-09-06 snapshot includes 3 Artificial Analysis indexes, 7 Design Arena categories, and AutoExacto results for 36 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.

DeepSeek V4 Flash 0731 ranked #4 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.

Profile facts

Profile type
Kendr API model
Kendr API alias
kc-deepseek-v4-flash-0731
Model developer
DeepSeek
Kendr route provider
DeepSeek
Context window
1M
Snapshot date
2026-09-06
Knowledge cutoff
Not disclosed
Reference catalog ID
deepseek/deepseek-v4-flash-0731

When to pick DeepSeek V4 Flash 0731

DeepSeek V4 Flash 0731 is worth reaching for when you need a route priced in the cheapest quarter of the catalog and carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support.

Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.

  • Reach for it when cost is the binding constraint. At $0.045 in and $0.09 out per 1M tokens, combined token price sits in the cheapest quarter of the 146 models publishing both rates in this snapshot.
  • Reach for it when a whole corpus has to fit in one prompt. The 1.31M-token context window is in the top quarter of the 163 models publishing a limit here, so long documents can go in whole rather than chunked.
  • Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
  • Reach for it when you want to trade depth against cost per call. Reasoning effort is selectable across max, high, low, so one route can serve both cheap and deep work.
  • Look elsewhere when the task is hard reasoning. Its 40.8 Artificial Analysis intelligence index is below the median of the 32 models carrying that field. Scores from different suites are not interchangeable, so treat this as one signal rather than a ranking.

Context, modalities, and identity

DeepSeek V4 Flash 0731 accepts text and returns text. The snapshot records coding, prompt caching, reasoning, reasoning controls, structured output, text generation, tool calling, tools, and web search as capabilities or interfaces.

Model developer
DeepSeek
Kendr route provider
DeepSeek
Context window
1M
Maximum output
131,072 tokens
Input modalities
text
Output modalities
text
Knowledge cutoff
Not disclosed

Model overview

DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows. This is the GA release of DeepSeek V4 Flash.

Reference model ID
deepseek/deepseek-v4-flash-0731
Canonical version
deepseek/deepseek-v4-flash-20260731
Hugging Face ID
deepseek-ai/DeepSeek-V4-Flash-0731

Capabilities and supported controls

Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.

Supported parameters
frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, parallel_tool_calls, presence_penalty, reasoning, reasoning_effort, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_a, top_k, top_logprobs, top_p
  • coding
  • prompt caching
  • reasoning
  • reasoning controls
  • structured output
  • text generation
  • tool calling
  • tools
  • web search

Dated pricing snapshot

The dated reference snapshot lists $0.045 input and $0.09 output per million tokens. Cached input is $0.0028 per million tokens. The separately published provider-route reference is $0.20 / $0.40 ($0.04/M implicit cached input).

Reference figures are dated 2026-09-06; the live Kendr quote can differ by selected provider route, context tier, caching, tools, region, and current rate card.

Price date
2026-09-06
Input
$0.045 per 1M tokens
Cached input
$0.0028 per 1M tokens
Output
$0.09 per 1M tokens

Provider routes, performance, and uptime

The snapshot retains 29 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.

Active provider endpoints
27
Best p50 latency
0.48 s
Best p50 throughput
167 tok/s
Availability with routing
99.82% over the sampled window
Availability without routing
93.06% over the sampled window
Performance date
2026-09-06
ProviderQuantizationInput / 1MOutput / 1MCache read / 1MContextp50 latencyp50 throughputUptime (1d)
Relacefp4$0.045 / 1M$0.09 / 1M$0.009 / 1M1,048,576 tokens0.91 s83 tok/s97.97%
Baidu Qianfanfp8$0.04998 / 1M$0.09996 / 1M$0.009996 / 1M1,048,576 tokens0.69 s126 tok/s99.94%
OpenInferencefp8$0.05 / 1M$0.16 / 1M$0.013 / 1M1,048,576 tokens1.29 s21 tok/s98.55%
DeepInfrafp8$0.06 / 1M$0.18 / 1M$0.015 / 1M1,048,576 tokens0.77 s22 tok/s99.58%
Sail Researchfp4$0.065 / 1M$0.18 / 1M$0.02 / 1M1,048,576 tokens1.37 s28 tok/s99.91%
DigitalOceanunknown$0.079996 / 1M$0.252 / 1M$0.0252 / 1M1,048,576 tokens0.61 s28 tok/s99.93%
StreamLakefp8$0.088 / 1M$0.264 / 1M$0.0028 / 1M1,024,000 tokens0.99 s167 tok/s99.75%
Makoraunknown$0.09 / 1M$0.195 / 1M$0.0196 / 1M1,000,000 tokens0.65 s87 tok/s98.50%
Waferunknown$0.1 / 1M$0.25 / 1M$0.05 / 1M1,048,576 tokens0.50 s133 tok/s99.76%
Reka AIfp4$0.11 / 1M$0.66 / 1M$0.007 / 1M262,144 tokens0.64 s160 tok/s99.81%
Morphbf16$0.123438 / 1M$0.3475 / 1M$0.03125 / 1M1,048,576 tokens1.26 s17 tok/s99.84%
Baseten (US)fp8$0.13 / 1M$0.26 / 1M$0.028 / 1M1,048,576 tokens0.50 s85 tok/s99.88%
Basetenfp8$0.13 / 1M$0.26 / 1M$0.028 / 1M1,048,576 tokens0.48 s73 tok/s99.91%
Inceptronfp4$0.13 / 1M$0.28 / 1M$0.03 / 1M1,048,576 tokens0.64 s39 tok/s99.90%
CoreWeavefp8$0.13 / 1M$0.28 / 1M$0.07 / 1M262,144 tokens0.49 s109 tok/s99.99%
Togetherunknown$0.14 / 1M$0.28 / 1M$0.03 / 1M1,048,576 tokens0.64 s36 tok/s99.81%
Parasailfp8$0.14 / 1M$0.28 / 1M$0.05 / 1M1,048,576 tokens0.65 s29 tok/s99.93%
Veniceunknown$0.175 / 1M$0.35 / 1M$0.035 / 1M1,000,000 tokens1.07 s36 tok/s98.70%
Alibaba Cloud Int.unknown$0.176 / 1M$0.528 / 1M$0.0176 / 1M1,000,000 tokens1.31 s65 tok/s99.83%
Fireworksunknown$0.22 / 1M$0.66 / 1M$0.007 / 1M1,048,576 tokens0.84 s61 tok/s99.00%
SiliconFlowfp8$0.22 / 1M$0.66 / 1M$0.028 / 1M1,048,576 tokens1.42 s72 tok/s99.77%
DeepSeekunknown$0.22 / 1M$0.66 / 1M$0.007 / 1M1,048,576 tokens0.67 s90 tok/s100.00%
NextBitfp8$0.4 / 1M$1.2 / 1M$0.01 / 1M1,048,576 tokens2.06 s57 tok/s99.93%
NovitaAIfp8$0.4092 / 1M$1.2276 / 1M$0.02604 / 1M1,048,576 tokens1.42 s89 tok/s100.00%
Phalaunknown$0.44 / 1M$1.32 / 1M$0.028 / 1M1,048,576 tokens0.71 s60 tok/s95.69%
AtlasCloudfp4$0.44 / 1M$1.32 / 1M$0.028 / 1M1,048,576 tokens1.81 s49 tok/s99.19%
Cloudflareunknown$0.44 / 1M$1.32 / 1M$0.014 / 1M1,310,720 tokens0.72 s55 tok/s99.99%
Mancerfp8$0.15 / 1M$0.5 / 1MNo verified rate1,048,576 tokens0.68 s29 tok/s97.78%
GMICloudfp8$0.352 / 1M$1.056 / 1M$0.0112 / 1M1,048,575 tokens1.83 s87 tok/s82.89%

Benchmark evidence and limitations

The 2026-09-06 snapshot includes 3 Artificial Analysis indexes, 7 Design Arena categories, and AutoExacto results for 36 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.

Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

Intelligence Index
40.8
Coding Index
69.1
Agentic Index
41.9
AutoExacto coverage
36 provider observations over 32 days
Design Arena categoryEloWin rateRank
3d124850.4%#34
codecategories124847.2%#38
dataviz120241.5%#49
gamedev124746.4%#33
svg121845.7%#25
uicomponent125947.3%#32
website125247%#36

AutoExacto provider benchmarks

Rolling provider observations from the cited third-party benchmark view. The lookback is 32 days; GPQA Diamond and TAU-Bench Airline measure different abilities and should not be blended into one score.

ProviderGPQA DiamondTAU-Bench AirlineRuns
AkashML88.52%72.46%5
Alibaba Cloud Int.89.42%76.83%3
Ambient86.45%73.48%4
AtlasCloud88.32%73.6%5
auto-routing84.8%70.82%5
Baidu Qianfan89.58%74.76%5
Baseten89.34%—2
Baseten88.04%—5
Cloudflare88.45%74.99%5
CoreWeave86.73%75.82%6
Decart87.9%73.29%3
DeepInfra89.16%73.86%5
DeepSeek90.24%81.33%1
DigitalOcean75.35%58.36%4
Fireworks88.89%—5
GMICloud88.99%75.83%5
Inceptron87.93%74.12%5
io.net84.12%74.51%3
Ionstream87.21%78%1
Makora86.92%71.69%3
Mancer85.52%70.81%5
Morph85.77%74.85%4
Nebius Token Factory75.59%65.28%1
NextBit89.86%76.6%3
NovitaAI89.3%76.01%5
OpenInference70.49%70.75%4
Parasail88.98%75.41%5
Phala88.21%74.47%5
Reka AI89.14%75.41%4
Relace87.19%71.69%4
Sail Research70.78%75.17%6
SiliconFlow90%75.43%5
StreamLake87.92%75.68%6
Together86.91%74.96%5
Venice86.85%74.19%5
Wafer84.1%76.05%6

Popularity and market context

DeepSeek V4 Flash 0731 ranked #4 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Third-party catalog rank
#4
Global market share
Not inferred
Observation date
2026-09-06

OpenAI-compatible API example

This example applies to the published Kendr alias on this hosted profile. Check the live public catalog before use.

curl https://api.kendr.org/v1/chat/completions \
  -H "Authorization: Bearer $KENDR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"kc-deepseek-v4-flash-0731","messages":[{"role":"user","content":"Hello"}]}'

Frequently asked questions

When should I use DeepSeek V4 Flash 0731?

DeepSeek V4 Flash 0731 is worth reaching for when you need a route priced in the cheapest quarter of the catalog and carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support. Reach for it when cost is the binding constraint: At $0.045 in and $0.09 out per 1M tokens, combined token price sits in the cheapest quarter of the 146 models publishing both rates in this snapshot. Reach for it when a whole corpus has to fit in one prompt: The 1.31M-token context window is in the top quarter of the 163 models publishing a limit here, so long documents can go in whole rather than chunked. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose. Reach for it when you want to trade depth against cost per call: Reasoning effort is selectable across max, high, low, so one route can serve both cheap and deep work. Look elsewhere when the task is hard reasoning: Its 40.8 Artificial Analysis intelligence index is below the median of the 32 models carrying that field. Scores from different suites are not interchangeable, so treat this as one signal rather than a ranking.

What is DeepSeek V4 Flash 0731?

DeepSeek V4 Flash 0731 is a DeepSeek model profile with a 1,310,720-token context window. The dated catalog snapshot records text modalities and interfaces for coding, prompt caching, reasoning, reasoning controls, structured output.

What context window does DeepSeek V4 Flash 0731 have?

The dated profile lists 1M of context and up to 131,072 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.

What benchmark evidence is available for DeepSeek V4 Flash 0731?

The 2026-09-06 snapshot includes 3 Artificial Analysis indexes, 7 Design Arena categories, and AutoExacto results for 36 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

What is the Kendr API alias for DeepSeek V4 Flash 0731?

Use kc-deepseek-v4-flash-0731 as the model value. The live public model API and signed-in catalog remain authoritative for route availability and customer credit quotes.

How is DeepSeek V4 Flash 0731 priced on Kendr?

The dated reference snapshot lists $0.045 input and $0.09 output per million tokens. Cached input is $0.0028 per million tokens. The separately published provider-route reference is $0.20 / $0.40 ($0.04/M implicit cached input). Kendr applies one 5% markup to configured provider model cost; the live quote and settled routing receipt are authoritative for a request.

Sources and evidence dates

  1. DeepSeek V4 Flash 0731 third-party catalog record (catalog, checked 2026-09-06)
  2. Third-party model catalog methodology (methodology, checked 2026-09-06)
  3. DeepSeek official model documentation (primary, checked 2026-09-06)
  4. Artificial Analysis capability indices methodology (benchmark, checked 2026-09-06)
  5. Design Arena leaderboard and methodology (benchmark, checked 2026-09-06)
  6. DeepSeek V4 Flash 0731 third-party catalog record (benchmark, checked 2026-09-06)
  7. DeepSeek V4 Flash 0731 provider or route reference (primary, checked 2026-09-06)