Updated November 11, 2026. Rank says DeepSeek V4 Pro 0813 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
DeepSeek V4 Pro 0813 has the higher public point estimate, 62.43 versus 48.08. Their conditional score ranges overlap. These ranges do not establish rank confidence. 14 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
DeepSeek V4 Pro 0813: $1.32 input / $3.96 output
Inkling: $1.87 input / $4.68 output
DeepSeek V4 Pro 0813: 1M
Inkling: 1M
DeepSeek V4 Pro 0813: August 13, 2026
Inkling: July 15, 2026
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Code generation, repair, and software-engineering tasks
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the higher public coding point estimate, 47.9 to 33.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the higher public agentic point estimate, 55 to 36.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
DeepSeek V4 Pro 0813 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
BrowseCompAgentic
Normalized gap 6.3HLEKnowledge
Normalized gap 3.3SWE-bench VerifiedCoding
Normalized gap 3.0GPQAKnowledge
Normalized gap 2.2LiveCodeBench (Vals)Coding
Normalized gap 2.0Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | DeepSeek V4 Pro 0813 | Inkling | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.0Supported · #44/132 | 36.7Supported · #80/132 | Like-for-likeBenchAlign v5.8 lane · 12 vs 5 public rows | DeepSeek V4 Pro 0813 leads |
| Coding | 47.9Supported · #53/158 | 33.6Supported · #96/158 | Like-for-likeBenchAlign v5.8 lane · 15 vs 6 public rows | DeepSeek V4 Pro 0813 leads · intervals overlap |
| Reasoning | 58.1Estimated · #39/158 | 37.7Estimated · #94/158 | Directional onlyBenchAlign v5.8 lane · 4 vs 0 public rows | Directional only |
| Knowledge | 62.5Estimated · #41/179 | 50.4Supported · #79/179 | Directional onlyBenchAlign v5.8 lane · 7 vs 6 public rows | Directional only |
| Multimodal | Not ranked | 50.0Estimated · #56/67 | Not comparableBenchAlign v5.8 lane · 0 vs 3 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 87.6#40/157 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 84.4Unranked · 4 rankable rows | 54.6#9/12 | Not comparableProvisional lane · 1 vs 3 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
DeepSeek V4 Pro 0813
Inkling
1M
DeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingInkling
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V4 Pro 0813
$0.044 per 1M cached input tokens
DeepSeek: Models & PricingInkling
$0.374 per 1M cached input tokens
DeepSeek V4 Pro 0813
Inkling
text, image, audio
Inkling model cardDeepSeek V4 Pro 0813
Inkling
DeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricing, V4 Pro continuation footnoteInkling
Not sourced
DeepSeek V4 Pro 0813
Reasoning
Inkling
Hybrid
DeepSeek V4 Pro 0813
Open Weight
Inkling
Open Weight
DeepSeek V4 Pro 0813
Open Weight
Inkling
Open Weight
DeepSeek V4 Pro 0813
2026-08-13
Inkling
2026-07-15
DeepSeek V4 Pro 0813 has the higher public point estimate, 62.43 versus 48.08. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
DeepSeek V4 Pro 0813 has the higher public coding point estimate, 47.9 to 33.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
DeepSeek V4 Pro 0813 has the higher public agentic tasks point estimate, 55 to 36.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0033 on DeepSeek V4 Pro 0813 and $0.00421 on Inkling; repository review costs $0.07788 and $0.10754; the cache-heavy agent loop costs $0.0748 and $0.159. Costs use the listed standard API rates.
Both models list the same context window, 1M.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Finch
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 0813 leads this result
BrowseComp
DeepSeek V4 Pro 0813 leads this result
HLE w/ tools
Not directly comparable
MCP Atlas
Inkling leads this result
Toolathlon
Not directly comparable
CyberGym
Not directly comparable
Toolathlon-Verified
Not directly comparable
Agents' Last Exam
Not directly comparable
AutomationBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V4 Pro 0813 leads this result
CWE-bench v1
Not directly comparable
LiveCodeBench Pass@1-COT
Not directly comparable
Codeforces
Not directly comparable
SWE-bench Verified
DeepSeek V4 Pro 0813 leads this result
SWE-bench Pro
DeepSeek V4 Pro 0813 leads this result
SWE Multilingual
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 0813 leads this result
NL2Repo
Not directly comparable
DeepSWE
Not directly comparable
DSBench-FullStack
Not directly comparable
DSBench-Hard
Not directly comparable
OpenHarmony Bench
Not directly comparable
LiveCodeBench (Vals)
DeepSeek V4 Pro 0813 leads this result
SWE-bench (Vals)
DeepSeek V4 Pro 0813 leads this result
FrontierSWE v2
Not directly comparable
MMLU-Pro
Not directly comparable
Chinese-SimpleQA
Not directly comparable
GPQA
DeepSeek V4 Pro 0813 leads this result
GPQA-D
DeepSeek V4 Pro 0813 leads this result
HLE
Inkling leads this result
GPQA Diamond (Vals)
DeepSeek V4 Pro 0813 leads this result
MMLU-Pro (Vals)
DeepSeek V4 Pro 0813 leads this result
HLE w/o tools
Not directly comparable
IFBench
Not directly comparable
HMMT Feb 2026
Not directly comparable
IMOAnswerBench
Not directly comparable
Apex
Not directly comparable
Apex Shortlist
Not directly comparable
AIME26
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
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Last updated November 11, 2026
