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BenchLM

Data as of September 27, 2026 · How the score is built

CurrentOpen WeightReasoning

Released Aug 13, 20261M context

DeepSeek V4 Pro 0813

Decision readingDeepSeek V4 Pro 0813 scores 63.5 out of 100 and ranks #33 of 194. This profile shows 42 source-displayable benchmark rows; its strongest eligible category is Agentic at #29. API pricing is $1.32 input and $3.96 output per million tokens, with cached input at $0.044.

Released Aug 13, 2026 — see all recent releases

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

63.5/100

field median 50.2#33 of 194 ranked models

Public

#33of 194

Verified —

Price

$1.32input / $3.96 output

input median $0.97cached $0.044 · blended $2.64

Speed

66tok/s

field median 91 tok/sFirst token 31.79 s

Context

1Mtokens

field median 256,000Maximum output length is tracked separately

Strongest published evidence

Agentic ranks #29. Particularly useful for coding agents, browser research, and computer-use workflows.

Validate before choosing

42 published rows leave some tracked benchmark slots empty. Coding is its lowest eligible category at #39.

Source-linked · 42 displayable benchmark rows

Follow model changes

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #29 of 105Percentile 73rdWeight 22%11 benchmarksVerified
55.0
CodingRank #39 of 135Percentile 72ndWeight 20%15 benchmarksVerified
50.3
ReasoningRank Not rankedWeight 17%4 benchmarksVerified
56.9
MultimodalWeight 12%0 benchmarksNot measured
Not measured
KnowledgeRank #29 of 158Percentile 82ndWeight 12%8 benchmarksVerified
63.4
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathRank Not rankedWeight 5%4 benchmarksVerified
80.2

42 of 486 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic11/11 verified
  2. Coding15/15 verified
  3. Reasoning4/4 verified
  4. MultimodalNot measured
  5. Knowledge8/8 verified
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. Math4/4 verified
Verified sourceProvisionalNot measured

Capability shape

Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.

DeepSeek V4 Pro 0813 category percentile values

  • Agentic73rd percentile
  • Coding72nd percentile
  • ReasoningNot eligible
  • MultimodalNot eligible
  • Knowledge82nd percentile
  • MultilingualNot eligible
  • Instruction followingNot eligible
  • MathNot eligible

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#29/105
  2. Coding#39/135
  3. ReasoningNot ranked
  4. MultimodalNot ranked
  5. Knowledge#29/158
  6. MultilingualNot ranked
  7. Inst. FollowingNot ranked
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible

Benchmark ledger

Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Coding15 rows
Coding benchmark values, best verified comparison, weight, and source status
SWE-bench ProScore55.4%Versus best verified row

Best verified: Claude Opus 5.5 · 89.9%

Gap34.5 behindWeight26% ref. weight
LiveCodeBench (Vals)LiveCodeBench, Vals AI runScore87.5%Versus best verified row

Best verified: Claude Fable 5.1 · 90.5%

Gap3 behindWeight8% ref. weight
SWE MultilingualScore76.2%Versus best verified row

Best verified: Claude Opus 5.5 · 93.9%

Gap17.7 behindWeight5% ref. weight
Terminal-Bench 2.0Score67.9%Versus best verified row

Best verified: GPT-5.5 · 82.0%

Gap14.1 behindWeightScored in Agentic
DeepSWEScore62.7%Versus best verified row

Best verified: Muse Spark 1.3 · 75.4%

Gap12.7 behindWeightDisplay only
LiveCodeBench Pass@1-COTLiveCodeBench Pass@1 with Chain-of-ThoughtScore93.5%Versus best verified row

Best verified: DeepSeek V4 Pro 0813 · 93.5%

GapBest verifiedWeightDisplay only
CodeforcesCodeforces RatingScore3206.0Versus best verified row

Best verified: DeepSeek V4.1 Flash · 3471.0

Gap265 behindWeightDisplay only
SWE-bench VerifiedSoftware Engineering Benchmark VerifiedScore80.6%Versus best verified row

Best verified: Claude Opus 5 · 96%

Gap15.4 behindWeightDisplay only
Vibe Code BenchVibe Code Bench v1.1Score49.93%Versus best verified row

Best verified: Claude Opus 4.7 · 71.00%

Gap21.1 behindWeightDisplay only
Terminal-Bench 2.1Score87.9%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap4.9 behindWeightDisplay only
NL2RepoScore61.5%Versus best verified row

Best verified: DeepSeek V4.1 Flash · 65.4%

Gap3.9 behindWeightDisplay only
DSBench-FullStackDeepSeek DSBench FullStackScore71.1%Versus best verified row

Best verified: DeepSeek V4 Pro 0813 · 71.1%

GapBest verifiedWeightDisplay only
DSBench-HardDeepSeek DSBench HardScore67.2%Versus best verified row

Best verified: DeepSeek V4 Pro 0813 · 67.2%

GapBest verifiedWeightDisplay only
OpenHarmony BenchOpenHarmony Bench v1.0Score59.0%Versus best verified row

Best verified: Qwen3.8 Max · 60.8%

Gap1.8 behindWeightDisplay only
SWE-bench (Vals)SWE-bench, Vals AI runScore96.4%Versus best verified row

Best verified: Claude Opus 5 · 97.0%

Gap0.6 behindWeightDisplay only
Agentic11 rows
Agentic benchmark values, best verified comparison, weight, and source status
BrowseCompScore83.4%Versus best verified row

Best verified: Atria Dawn Preview · 92.5%

Gap9.1 behindWeight8% ref. weight
Terminal-Bench 2.0Score67.9%Versus best verified row

Best verified: GPT-5.5 · 82%

Gap14.1 behindWeight7% ref. weight
MCP AtlasScore73.6%Versus best verified row

Best verified: Muse Spark 1.1 · 88.1%

Gap14.5 behindWeight4% ref. weight
Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI runScore54.7%Versus best verified row

Best verified: GPT-6 Astra · 87.3%

Gap32.6 behindWeight3% ref. weight
HLE w/ toolsHumanity's Last Exam with toolsScore60.0%Versus best verified row

Best verified: Claude Opus 5.5 · 67.7%

Gap7.7 behindWeight3% ref. weight
ToolathlonScore51.8%Versus best verified row

Best verified: Muse Spark 1.1 · 75.6%

Gap23.8 behindWeight3% ref. weight
Terminal-Bench 2.1Score87.9%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap4.9 behindWeightDisplay only
AutomationBenchScore31.8%Versus best verified row

Best verified: DeepSeek V4.1 Flash · 54.8%

Gap23 behindWeightDisplay only
Toolathlon-VerifiedScore74.1%Versus best verified row

Best verified: Claude Opus 5 · 80.6%

Gap6.5 behindWeightDisplay only
Agents' Last ExamScore25.7%Versus best verified row

Best verified: GPT-6 Astra · 59.3%

Gap33.6 behindWeightDisplay only
CyberGymScore83.3%Versus best verified row

Best verified: MiMo-V2.6-Flash · 95.1%

Gap11.8 behindWeightDisplay only
Reasoning4 rows
Reasoning benchmark values, best verified comparison, weight, and source status
ARC-AGI-2Abstraction and Reasoning Corpus for AGI v2Score61.3%Versus best verified row

Best verified: GPT-6 Astra · 95%

Gap33.7 behindWeightWeighted 25%
MRCR 1MScore83.5%Versus best verified row

Best verified: DeepSeek V4 Pro 0813 · 83.5%

GapBest verifiedWeightDisplay only
CorpusQA 1MScore62.0%Versus best verified row

Best verified: DeepSeek V4 Pro 0813 · 62.0%

GapBest verifiedWeightDisplay only
ARC-AGI-1ARC-AGI-1 Semi-Private EvaluationScore90.00%Versus best verified row

Best verified: GPT-6 Astra · 98.50%

Gap8.5 behindWeightDisplay only
Knowledge8 rows
Knowledge benchmark values, best verified comparison, weight, and source status
HLEHumanity's Last ExamScore42.7%Versus best verified row

Best verified: Claude Fable 5.1 · 65%

Gap22.3 behindWeight44% ref. weight
MMLU-ProMassive Multitask Language Understanding ProfessionalScore87.5%Versus best verified row

Best verified: Qwen3.7 Max · 89.6%

Gap2.1 behindWeight6% ref. weight
MMLU-Pro (Vals)MMLU-Pro, Vals AI runScore87.0%Versus best verified row

Best verified: Claude Fable 5.1 · 92.4%

Gap5.4 behindWeight6% ref. weight
GPQAGraduate-Level Google-Proof Q&AScore90.1%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap5.9 behindWeight3% ref. weight
GPQA Diamond (Vals)GPQA Diamond, Vals AI runScore92.4%Versus best verified row

Best verified: Gemini 3.1 Pro · 95.5%

Gap3.1 behindWeight2% ref. weight
SimpleQAMeasuring Short-Form Factuality in Large Language ModelsScore57.9%Versus best verified row

Best verified: DeepSeek V4 Pro 0813 · 57.9%

GapBest verifiedWeightDisplay only
Chinese-SimpleQAScore84.4%Versus best verified row

Best verified: DeepSeek V4 Pro 0813 · 84.4%

GapBest verifiedWeightDisplay only
GPQA-DGPQA DiamondScore90.1%Versus best verified row

Best verified: GPT-6 Astra · 96.0%

Gap5.9 behindWeightDisplay only
Math4 rows
Math benchmark values, best verified comparison, weight, and source status
HMMT Feb 2026Harvard-MIT Mathematics Tournament February 2026Score95.2%Versus best verified row

Best verified: Qwen3.7 Max · 97.1%

Gap1.9 behindWeightWeighted 25%
IMOAnswerBenchScore89.8%Versus best verified row

Best verified: dots3-note Preview · 90.9%

Gap1.1 behindWeightDisplay only
ApexScore38.3%Versus best verified row

Best verified: Hy4 preview · 74.2%

Gap35.9 behindWeightDisplay only
Apex ShortlistScore90.2%Versus best verified row

Best verified: DeepSeek V4 Pro 0813 · 90.2%

GapBest verifiedWeightDisplay only

What it costs to get this score

Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.

Current modelExplore all models

DeepSeek V4 Pro 0813 · 63.5 score · $2.64 blended per million tokens

The chart opens on the current model. Scroll horizontally to inspect the full price axis.

30405060708090100$0.50$1$5$10$25$50$100↘ frontierDeepSeek V4 Pro 0813

Horizontal: blended price per million tokens, log scale · Vertical: public score

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Aug 13, 2026 · you are here

    DeepSeek V4 Pro 0813

    Score 63.5 · $1.32 / $3.96

pro-reasoning · 0813

Radar

DeepSeek V4 Pro 0813 release history

Full release history

Radar confirmed these at the source. Use DeepSeek V4 Pro 0813 in your work? Explore Radar to follow supported changes and choose your alerts.

Radar

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

API model ID
deepseek-v4-proDeepSeek models and pricing
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Parameters
Not sourced yet
Cloud regions
Not tracked yet
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Published at $0.044 per million cached input tokensDeepSeek: Models & Pricing
Self-host
Open weights available; hardware estimate not sourced
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

DeepSeek V4 Pro 0813 ranks #33 of 194 on the public leaderboard with a score of 63.48/100. It does not yet have enough sourced coverage for a verified position.

DeepSeek V4 Pro 0813 is a open weight model with a 1M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

Served as `deepseek-v4-pro` on the DeepSeek API. DeepSeek's September 10, 2026 V4.1 Flash announcement said `deepseek-v4-pro` requests would route to DeepSeek-V4.1-Flash and bill at the V4.1 Flash rate from 04:00 UTC on September 14, 2026 until V4.1 Pro launches. DeepSeek's pricing page, read September 17, 2026, says V4 Pro API service continues after September 14, 2026 with the billing method unchanged, and that DeepSeek will give notice of any change; the September 10 post had not been edited when read the same day (https://api-docs.deepseek.com/news/news260910, https://api-docs.deepseek.com/quick_start/pricing).

Rolling profile for the current DeepSeek-V4-Pro-0813 API. It retains the earlier DeepSeek V4 Pro Max snapshot and overlays DeepSeek's newer August 13 max-effort results where the provider published a stronger value. HLE rises from 37.7 to 42.7 and HLE with tools rises from 48.2 to 60.0; the newly published agent and coding benchmarks are added without removing prior coverage. DeepSeek has not published weights for the 0813 hosted checkpoint.

Its explicit predecessor is DeepSeek V4 Pro 0813. 42 of 486 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Agentic at #29, while its lowest eligible position is Coding at #39. particularly useful for coding agents, browser research, and computer-use workflows.

Last updated September 27, 2026. Runtime fields remain blank until a sourced snapshot exists.

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.

Estimate VRAM from known parameters

Questions

How does DeepSeek V4 Pro 0813 perform overall in AI benchmarks?

DeepSeek V4 Pro 0813 ranks #33 out of 194 models on the public BenchAlign leaderboard, with a score of 63.48/100. Its evidence status is Estimated, and this profile shows 42 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is DeepSeek V4 Pro 0813 good for knowledge and understanding?

DeepSeek V4 Pro 0813 ranks #29 out of 158 eligible models for knowledge and understanding, with a public category score of 63.4/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is DeepSeek V4 Pro 0813 good for coding and programming?

DeepSeek V4 Pro 0813 ranks #39 out of 135 eligible models for coding and programming, with a public category score of 50.3/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is DeepSeek V4 Pro 0813 good for mathematics?

DeepSeek V4 Pro 0813 has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is DeepSeek V4 Pro 0813 good for reasoning and logic?

DeepSeek V4 Pro 0813 has source-displayable benchmark coverage for reasoning and logic, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is DeepSeek V4 Pro 0813 good for agentic tool use and computer tasks?

DeepSeek V4 Pro 0813 ranks #29 out of 105 eligible models for agentic tool use and computer tasks, with a public category score of 55/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is DeepSeek V4 Pro 0813 open source?

DeepSeek V4 Pro 0813 is an open-weight model from DeepSeek. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

Does DeepSeek V4 Pro 0813 have full benchmark coverage on BenchLM?

No. DeepSeek V4 Pro 0813 currently has 60 source-displayable rows across 486 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of DeepSeek V4 Pro 0813?

DeepSeek V4 Pro 0813 has a documented context window of 1M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.

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Compare DeepSeek V4 Pro 0813 with every tracked model507 comparisons