Updated September 27, 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 score estimate, 63.48 versus 50.15, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 3 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 leads on the public coding lane, 50.3 to 47.1, with Supported evidence for both models, although the 90% intervals overlap.
Prompts that approach the documented context limit
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the larger documented context window.
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. GPT-5.2-Codex has no published cached-input rate, so cached tokens use its listed input rate.
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.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.2-Codex is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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 v5.7
DeepSeek V4 Pro 0813 leads the like-for-like coding row, although the 90% intervals overlap.
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.
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.
LiveCodeBench (Vals)Coding
Normalized gap 0.5Each row shows the public-lane category score for both models: the BenchAlign v5.7 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 | GPT-5.2-Codex | Basis | Reading |
|---|---|---|---|---|
| Coding | 50.3Supported · #39/135 | 47.1Supported · #43/135 | Like-for-likeBenchAlign v5.7 lane · 15 vs 3 public rows | DeepSeek V4 Pro 0813 leads · intervals overlap |
| Agentic | 55.0Supported · #29/105 | 34.2Estimated · #57/105 | Directional onlyBenchAlign v5.7 lane · 11 vs 2 public rows | Directional only |
| Knowledge | 63.4Estimated · #29/158 | 52.0Estimated · #54/158 | Directional onlyBenchAlign v5.7 lane · 8 vs 0 public rows | Directional only |
| Reasoning | 56.9Unranked · 4 rankable rows | 78.6Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 72.5Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.4#3/124 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 80.2Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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
GPT-5.2-Codex has no published cached-input rate, so cached tokens use its listed input 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
GPT-5.2-Codex
400K
DeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingGPT-5.2-Codex
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 & PricingGPT-5.2-Codex
Not published
DeepSeek V4 Pro 0813
GPT-5.2-Codex
Not sourced
DeepSeek V4 Pro 0813
GPT-5.2-Codex
Not sourced
DeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricing, V4 Pro continuation footnoteGPT-5.2-Codex
Not sourced
DeepSeek V4 Pro 0813
Reasoning
GPT-5.2-Codex
Reasoning
DeepSeek V4 Pro 0813
Open Weight
GPT-5.2-Codex
Proprietary
DeepSeek V4 Pro 0813
Open Weight
GPT-5.2-Codex
Proprietary
DeepSeek V4 Pro 0813
2026-08-13
GPT-5.2-Codex
2025-12-18
DeepSeek V4 Pro 0813 has the higher public score estimate, 63.48 versus 50.15, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
DeepSeek V4 Pro 0813 leads the public coding lane, 50.3 to 47.1, with Supported evidence for both models, although the 90% intervals overlap.
DeepSeek V4 Pro 0813 scores higher for agentic tasks on the public lane, 55 to 34.2. GPT-5.2-Codex is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.0033 on DeepSeek V4 Pro 0813 and $0.00875 on GPT-5.2-Codex; repository review costs $0.07788 and $0.1295; the cache-heavy agent loop costs $0.0748 and $0.525. GPT-5.2-Codex has no published cached-input rate, so cached tokens use its listed input rate.
DeepSeek V4 Pro 0813 has the larger documented context window: 1M, compared with 400K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
BrowseComp
Not directly comparable
HLE w/ tools
Not directly comparable
MCP Atlas
Not directly comparable
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)
Not directly comparable
Gert Labs
Not directly comparable
JobBench
Not directly comparable
LiveCodeBench Pass@1-COT
Not directly comparable
Codeforces
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Shared sourceDeepSeek V4 Pro 0813 leads this result
Terminal-Bench 2.1
Not directly comparable
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)
GPT-5.2-Codex leads this result
SWE-bench (Vals)
DeepSeek V4 Pro 0813 leads this result
MRCR 1M
Not directly comparable
CorpusQA 1M
Not directly comparable
ARC-AGI-1
Not directly comparable
ARC-AGI-2
Not directly comparable
MMLU-Pro
Not directly comparable
SimpleQA
Not directly comparable
Chinese-SimpleQA
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
HMMT Feb 2026
Not directly comparable
IMOAnswerBench
Not directly comparable
Apex
Not directly comparable
Apex Shortlist
Not directly comparable
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Last updated September 27, 2026
