DeepSeek V4 Pro 0813 model profile
Evidence snapshot:
DeepSeek V4 Pro 0813 is a DeepSeek model profile with a 1,048,576-token context window. The dated catalog snapshot records text modalities and interfaces for prompt caching, reasoning controls, structured output, text generation, tool calling.
DeepSeek V4 Pro 0813 is a large-scale mixture-of-experts model from DeepSeek. This is the GA release of DeepSeek V4 Pro.
DeepSeek V4 Pro 0813 is a reference profile: DeepSeek publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
DeepSeek V4 Pro 0813 is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt.
DeepSeek V4 Pro 0813 accepts text and returns text. The snapshot records prompt caching, reasoning controls, structured output, text generation, and tool calling as capabilities or interfaces.
The dated reference snapshot lists $0.57948 input and $1.73844 output per million tokens. Cached input is $0.019316 per million tokens.
The 2026-09-06 snapshot includes 3 Artificial Analysis indexes and AutoExacto results for 19 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 Pro 0813 ranked #23 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
- Model research profile
- Provider or publisher
- DeepSeek
- Context window
- 1,048,576 tokens
- Snapshot date
- 2026-09-06
- Knowledge cutoff
- Not disclosed
- Reference catalog ID
- deepseek/deepseek-v4-pro-0813
When to pick DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 is a reference profile: DeepSeek publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
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 a whole corpus has to fit in one prompt. The 1.05M-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 you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
- Look elsewhere when the task is hard reasoning. Its 42.1 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 Pro 0813 accepts text and returns text. The snapshot records prompt caching, reasoning controls, structured output, text generation, and tool calling as capabilities or interfaces.
- Provider or publisher
- DeepSeek
- Context window
- 1,048,576 tokens
- Maximum output
- 384,000 tokens
- Input modalities
- text
- Output modalities
- text
- Knowledge cutoff
- Not disclosed
Model overview
DeepSeek V4 Pro 0813 is a large-scale mixture-of-experts model from DeepSeek. This is the GA release of DeepSeek V4 Pro.
- Reference model ID
- deepseek/deepseek-v4-pro-0813
- Canonical version
- deepseek/deepseek-v4-pro-20260813
- Hugging Face ID
- deepseek-ai/DeepSeek-V4-Pro-0813
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, presence_penalty, reasoning, reasoning_effort, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
- prompt caching
- reasoning controls
- structured output
- text generation
- tool calling
Dated pricing snapshot
The dated reference snapshot lists $0.57948 input and $1.73844 output per million tokens. Cached input is $0.019316 per million tokens.
These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.
- Price date
- 2026-09-06
- Input
- $0.57948 per 1M tokens
- Cached input
- $0.019316 per 1M tokens
- Output
- $1.73844 per 1M tokens
Provider routes, performance, and uptime
The snapshot retains 19 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.
- Active provider endpoints
- 19
- Best p50 latency
- 0.55 s
- Best p50 throughput
- 76 tok/s
- Availability with routing
- 99.86% over the sampled window
- Availability without routing
- 98.17% over the sampled window
- Performance date
- 2026-09-06
| Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) |
|---|---|---|---|---|---|---|---|---|
| StreamLake | unknown | $0.57948 / 1M | $1.73844 / 1M | $0.019316 / 1M | 1,024,000 tokens | 3.77 s | 44 tok/s | 99.89% |
| Alibaba Cloud Int. | unknown | $0.5808 / 1M | $1.7424 / 1M | $0.05808 / 1M | 1,000,000 tokens | 1.40 s | 52 tok/s | 99.85% |
| DeepSeek | unknown | $0.66 / 1M | $1.98 / 1M | $0.022 / 1M | 1,048,576 tokens | 1.04 s | 22 tok/s | 100.00% |
| Baidu Qianfan | fp8 | $1.11936 / 1M | $3.35808 / 1M | $0.111936 / 1M | 1,048,576 tokens | 0.83 s | 60 tok/s | 99.99% |
| GMICloud | fp8 | $1.122 / 1M | $3.366 / 1M | $0.0374 / 1M | 1,048,575 tokens | 6.11 s | 32 tok/s | 99.94% |
| DeepInfra | fp8 | $1.3 / 1M | $2.6 / 1M | $0.1 / 1M | 1,048,576 tokens | 0.80 s | 59 tok/s | 99.38% |
| CoreWeave | fp8 | $1.31 / 1M | $3.96 / 1M | $0.044 / 1M | 1,048,576 tokens | 0.87 s | 76 tok/s | 99.90% |
| NextBit | fp8 | $1.32 / 1M | $3.96 / 1M | $0.045 / 1M | 1,048,576 tokens | 3.41 s | 40 tok/s | 99.86% |
| Sail Research | fp4 | $1.32 / 1M | $3.96 / 1M | $0.044 / 1M | 1,048,576 tokens | 1.90 s | 27 tok/s | 99.95% |
| Baseten (US) | fp4 | $1.32 / 1M | $3.96 / 1M | $0.132 / 1M | 1,048,576 tokens | 0.62 s | 41 tok/s | 99.92% |
| Parasail | fp8 | $1.32 / 1M | $3.96 / 1M | $0.044 / 1M | 1,048,576 tokens | 1.07 s | 51 tok/s | 99.73% |
| Together | unknown | $1.32 / 1M | $3.96 / 1M | $0.13 / 1M | 1,048,576 tokens | 0.93 s | 63 tok/s | 99.72% |
| DigitalOcean | unknown | $1.32 / 1M | $3.96 / 1M | $0.044 / 1M | 1,048,576 tokens | 0.80 s | 52 tok/s | 99.59% |
| NovitaAI | fp8 | $1.32 / 1M | $3.96 / 1M | $0.132 / 1M | 1,048,576 tokens | 1.17 s | 53 tok/s | 99.99% |
| SiliconFlow | fp8 | $1.32 / 1M | $3.96 / 1M | $0.044 / 1M | 1,048,576 tokens | 1.44 s | 51 tok/s | 99.86% |
| Baseten | fp4 | $1.32 / 1M | $3.96 / 1M | $0.132 / 1M | 1,048,576 tokens | 0.55 s | 34 tok/s | 99.76% |
| Cloudflare | unknown | $1.32 / 1M | $3.96 / 1M | $0.044 / 1M | 1,048,576 tokens | 2.33 s | 51 tok/s | 99.50% |
| Fireworks | unknown | $1.32 / 1M | $3.96 / 1M | $0.044 / 1M | 1,048,576 tokens | 1.60 s | 62 tok/s | 99.74% |
| Phala | unknown | $1.45 / 1M | $4.36 / 1M | $0.15 / 1M | 1,048,576 tokens | 1.31 s | 59 tok/s | 99.97% |
Benchmark evidence and limitations
The 2026-09-06 snapshot includes 3 Artificial Analysis indexes and AutoExacto results for 19 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
- 42.1
- Coding Index
- 68.8
- Agentic Index
- 42.5
- AutoExacto coverage
- 19 provider observations over 32 days
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.
| Provider | GPQA Diamond | TAU-Bench Airline | Runs |
|---|---|---|---|
| Alibaba Cloud Int. | 89.78% | 77.19% | 4 |
| auto-routing | 89.33% | 77.79% | 4 |
| Baidu Qianfan | 91.08% | — | 2 |
| Baseten | 88.21% | — | 5 |
| Baseten | 90.88% | 74.67% | 2 |
| Cloudflare | 87.29% | 76.28% | 5 |
| CoreWeave | 87.68% | 76% | 3 |
| DeepInfra | 78.72% | 81.33% | 3 |
| DigitalOcean | 81.59% | 67.42% | 4 |
| Fireworks | 87.76% | — | 3 |
| GMICloud | 85.45% | 76.48% | 5 |
| NextBit | 92.14% | 72.67% | 2 |
| NovitaAI | 88.53% | — | 5 |
| Parasail | 88.3% | 76.36% | 4 |
| Phala | 88.15% | 77.55% | 4 |
| Sail Research | 87.72% | 72% | 4 |
| SiliconFlow | 89.8% | 79.86% | 5 |
| StreamLake | 88.73% | 76.8% | 4 |
| Together | 87.48% | 73.27% | 4 |
Popularity and market context
DeepSeek V4 Pro 0813 ranked #23 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
- #23
- Global market share
- Not inferred
- Observation date
- 2026-09-06
Frequently asked questions
When should I use DeepSeek V4 Pro 0813?
DeepSeek V4 Pro 0813 is a reference profile: DeepSeek publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Reach for it when a whole corpus has to fit in one prompt: The 1.05M-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 you need to call it through Kendr today: This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr. Look elsewhere when the task is hard reasoning: Its 42.1 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 Pro 0813?
DeepSeek V4 Pro 0813 is a DeepSeek model profile with a 1,048,576-token context window. The dated catalog snapshot records text modalities and interfaces for prompt caching, reasoning controls, structured output, text generation, tool calling.
What context window does DeepSeek V4 Pro 0813 have?
The dated profile lists 1,048,576 tokens of context and up to 384,000 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.
What benchmark evidence is available for DeepSeek V4 Pro 0813?
The 2026-09-06 snapshot includes 3 Artificial Analysis indexes and AutoExacto results for 19 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.
Is DeepSeek V4 Pro 0813 available through Kendr?
This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases.
Does the popularity rank represent DeepSeek V4 Pro 0813's global market share?
DeepSeek V4 Pro 0813 ranked #23 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. No global market-share percentage is inferred when the source does not publish one.
Sources and evidence dates
- DeepSeek V4 Pro 0813 third-party catalog record (catalog, checked 2026-09-06)
- Third-party model catalog methodology (methodology, checked 2026-09-06)
- DeepSeek official model documentation (primary, checked 2026-09-06)
- Artificial Analysis capability indices methodology (benchmark, checked 2026-09-06)
- DeepSeek V4 Pro 0813 third-party catalog record (benchmark, checked 2026-09-06)