Updated October 15, 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.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
DeepSeek V4 Pro 0813 has the higher public point estimate, 63.19 versus 62.64. Their conditional score ranges overlap. These ranges do not establish rank confidence. 21 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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, 47.2 to 44.1, 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, 54.6 to 40.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests 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.
SWE-bench ProCoding
Normalized gap 5.2GPQAKnowledge
Normalized gap 2.3MMLU-Pro (Vals)Knowledge
Normalized gap 2.3SWE MultilingualCoding
Normalized gap 2.1MMLU-ProKnowledge
Normalized gap 2.1Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, 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 | Qwen3.7 Max | Basis | Reading |
|---|---|---|---|---|
| Agentic | 54.6Supported · #43/127 | 40.8Supported · #66/127 | Like-for-likeBenchAlign v5.8 lane · 11 vs 10 public rows | DeepSeek V4 Pro 0813 leads · intervals overlap |
| Coding | 47.2Supported · #50/150 | 44.1Supported · #55/150 | Like-for-likeBenchAlign v5.8 lane · 15 vs 10 public rows | DeepSeek V4 Pro 0813 leads · intervals overlap |
| Knowledge | 63.4Estimated · #41/184 | 61.2Supported · #46/184 | Directional onlyBenchAlign v5.8 lane · 7 vs 9 public rows | Directional only |
| Reasoning | 60.7Unranked · 4 rankable rows | 82.3Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 100.0#1/17 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.8#16/146 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 87.4Unranked · 4 rankable rows | 79.0#2/12 | Not comparableProvisional lane · 1 vs 3 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, 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
Qwen3.7 Max has no comparable published API token rate.
50K fresh input + 3K output tokens
Qwen3.7 Max has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Qwen3.7 Max has no comparable published API token rate.
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
Qwen3.7 Max
1M
DeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingQwen3.7 Max
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 & PricingQwen3.7 Max
No comparable hosted API rate
DeepSeek V4 Pro 0813
Qwen3.7 Max
Not sourced
DeepSeek V4 Pro 0813
Qwen3.7 Max
Not sourced
DeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricing, V4 Pro continuation footnoteQwen3.7 Max
Not sourced
DeepSeek V4 Pro 0813
Reasoning
Qwen3.7 Max
Reasoning
DeepSeek V4 Pro 0813
Open Weight
Qwen3.7 Max
Proprietary
DeepSeek V4 Pro 0813
Open Weight
Qwen3.7 Max
Proprietary
DeepSeek V4 Pro 0813
2026-08-13
Qwen3.7 Max
2026-05-16
DeepSeek V4 Pro 0813 has the higher public point estimate, 63.19 versus 62.64. 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, 47.2 to 44.1, 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, 54.6 to 40.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Qwen3.7 Max leads this result
Terminal-Bench 2.1
Not directly comparable
BrowseComp
Not directly comparable
HLE w/ tools
DeepSeek V4 Pro 0813 leads this result
MCP Atlas
Qwen3.7 Max 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)
Qwen3.7 Max leads this result
QwenClawBench
Not directly comparable
Claw-Eval
Not directly comparable
BFCL v4
Not directly comparable
VITA-Bench
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
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
Qwen3.7 Max leads this result
SWE Multilingual
Qwen3.7 Max leads this result
Terminal-Bench 2.0
Qwen3.7 Max leads this result
Vibe Code Bench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
NL2Repo
DeepSeek V4 Pro 0813 leads this result
DeepSWE
Not directly comparable
DSBench-FullStack
Not directly comparable
DSBench-Hard
Not directly comparable
OpenHarmony Bench
Shared sourceDeepSeek V4 Pro 0813 leads this result
LiveCodeBench (Vals)
DeepSeek V4 Pro 0813 leads this result
SWE-bench (Vals)
DeepSeek V4 Pro 0813 leads this result
SciCode
Not directly comparable
LiveCodeBench v6
Not directly comparable
MRCR 1M
Not directly comparable
CorpusQA 1M
Not directly comparable
ARC-AGI-1
Not directly comparable
ARC-AGI-2
Not directly comparable
MRCRv2
Not directly comparable
CritPt
Not directly comparable
MMLU-Pro
Qwen3.7 Max leads this result
Chinese-SimpleQA
Not directly comparable
GPQA
Qwen3.7 Max leads this result
GPQA-D
Qwen3.7 Max leads this result
HLE
DeepSeek V4 Pro 0813 leads this result
GPQA Diamond (Vals)
DeepSeek V4 Pro 0813 leads this result
MMLU-Pro (Vals)
Qwen3.7 Max leads this result
MMLU-Redux
Not directly comparable
SuperGPQA
Not directly comparable
MMMLU
Not directly comparable
MMLU-ProX
Not directly comparable
NOVA-63
Not directly comparable
INCLUDE
Not directly comparable
MAXIFE
Not directly comparable
PolyMath
Not directly comparable
HMMT Feb 2026
Qwen3.7 Max leads this result
IMOAnswerBench
Qwen3.7 Max leads this result
Apex
Qwen3.7 Max leads this result
Apex Shortlist
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
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Last updated October 15, 2026
