Long documents
Prompts that approach the documented context limit
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the larger documented context window.
Confidence: documented
DeepSeek V4 Pro 0813 vs A.X series
Updated September 27, 2026. We do not rank this pair: at least one has no public score. 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
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Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- DeepSeek V4 Pro 0813 only
- 42
- A.X series only
- 0
- Like-for-like categories
- 0 / 8
Estimated: DeepSeek V4 Pro 0813How the comparison works
Which one for your work
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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
A.X series is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
A.X series is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. A.X series does not fit this workload in one request. A.X series has no comparable published API token rate.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Which one for a specific job
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.
50.3DeepSeek V4 Pro 0813—A.X series
Not comparable · BenchAlign v5.7
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
42 DeepSeek V4 Pro 0813 only
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each 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.
Agentic
Not comparable- DeepSeek V4 Pro 0813
- 55.0
- Supported · #29/105
- A.X series
- Not ranked
- Basis
- BenchAlign v5.7 lane · 11 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- DeepSeek V4 Pro 0813
- 50.3
- Supported · #39/135
- A.X series
- Not ranked
- Basis
- BenchAlign v5.7 lane · 15 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V4 Pro 0813
- 56.9
- Unranked · 4 rankable rows
- A.X series
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- A.X series
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V4 Pro 0813
- 63.4
- Estimated · #29/158
- A.X series
- Not ranked
- Basis
- BenchAlign v5.7 lane · 8 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- A.X series
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- A.X series
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V4 Pro 0813
- 80.2
- Unranked · 4 rankable rows
- A.X series
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V4 Pro 0813 | A.X series | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.0Supported · #29/105 | Not ranked | Not comparableBenchAlign v5.7 lane · 11 vs 0 public rows | Not comparable |
| Coding | 50.3Supported · #39/135 | Not ranked | Not comparableBenchAlign v5.7 lane · 15 vs 0 public rows | Not comparable |
| Reasoning | 56.9Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 63.4Estimated · #29/158 | Not ranked | Not comparableBenchAlign v5.7 lane · 8 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | 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.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- DeepSeek V4 Pro 0813
- $0.0033
- Fits in one request
- A.X series
- API rate not published
- Fits in one request
A.X series has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V4 Pro 0813
- $0.07788
- Fits in one request
- A.X series
- API rate not published
- Fits in one request
A.X series has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek V4 Pro 0813
- $0.0748
- Fits in one request
- A.X series
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
A.X series does not fit this workload in one request. A.X series has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V4 Pro 0813A.X series
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4 Pro 0813
A.X series
64K
API model ID
DeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingA.X series
Not sourced
Cached-input rate
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 & PricingA.X series
No comparable hosted API rate
Documented inputs
DeepSeek V4 Pro 0813
A.X series
Not sourced
Documented outputs
DeepSeek V4 Pro 0813
A.X series
Not sourced
Provider availability
DeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricing, V4 Pro continuation footnoteA.X series
Not sourced
Reasoning profile
DeepSeek V4 Pro 0813
Reasoning
A.X series
Non-Reasoning
Weight access
DeepSeek V4 Pro 0813
Open Weight
A.X series
Proprietary
License
DeepSeek V4 Pro 0813
Open Weight
A.X series
Proprietary
Release date
DeepSeek V4 Pro 0813
2026-08-13
A.X series
Not sourced
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- DeepSeek V4 Pro 0813 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, DeepSeek V4 Pro 0813 or A.X series?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, DeepSeek V4 Pro 0813 or A.X series?
A.X series is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, DeepSeek V4 Pro 0813 or A.X series?
A.X series is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek V4 Pro 0813 or A.X series?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, DeepSeek V4 Pro 0813 or A.X series?
DeepSeek V4 Pro 0813 has the larger documented context window: 1M, compared with 64K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence42 rows
Agentic
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceA.X series—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceA.X series—Not directly comparable
BrowseComp
DeepSeek V4 Pro 081383.4%SourceA.X series—Not directly comparable
HLE w/ tools
DeepSeek V4 Pro 081360.0%SourceA.X series—Not directly comparable
MCP Atlas
DeepSeek V4 Pro 081373.6%SourceA.X series—Not directly comparable
Toolathlon
DeepSeek V4 Pro 081351.8%SourceA.X series—Not directly comparable
CyberGym
DeepSeek V4 Pro 081383.3%SourceA.X series—Not directly comparable
Toolathlon-Verified
DeepSeek V4 Pro 081374.1%SourceA.X series—Not directly comparable
Agents' Last Exam
DeepSeek V4 Pro 081325.7%SourceA.X series—Not directly comparable
AutomationBench
DeepSeek V4 Pro 081331.8%SourceA.X series—Not directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V4 Pro 081354.7%SourceA.X series—Not directly comparable
Coding
LiveCodeBench Pass@1-COT
DeepSeek V4 Pro 081393.5%SourceA.X series—Not directly comparable
Codeforces
DeepSeek V4 Pro 08133206.0SourceA.X series—Not directly comparable
SWE-bench Verified
DeepSeek V4 Pro 081380.6%SourceA.X series—Not directly comparable
SWE-bench Pro
DeepSeek V4 Pro 081355.4%SourceA.X series—Not directly comparable
SWE Multilingual
DeepSeek V4 Pro 081376.2%SourceA.X series—Not directly comparable
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceA.X series—Not directly comparable
Vibe Code Bench
DeepSeek V4 Pro 081349.93%SourceA.X series—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceA.X series—Not directly comparable
NL2Repo
DeepSeek V4 Pro 081361.5%SourceA.X series—Not directly comparable
DeepSWE
DeepSeek V4 Pro 081362.7%SourceA.X series—Not directly comparable
DSBench-FullStack
DeepSeek V4 Pro 081371.1%SourceA.X series—Not directly comparable
DSBench-Hard
DeepSeek V4 Pro 081367.2%SourceA.X series—Not directly comparable
OpenHarmony Bench
DeepSeek V4 Pro 081359.0%SourceA.X series—Not directly comparable
LiveCodeBench (Vals)
DeepSeek V4 Pro 081387.5%SourceA.X series—Not directly comparable
SWE-bench (Vals)
DeepSeek V4 Pro 081396.4%SourceA.X series—Not directly comparable
Reasoning
MRCR 1M
DeepSeek V4 Pro 081383.5%SourceA.X series—Not directly comparable
CorpusQA 1M
DeepSeek V4 Pro 081362.0%SourceA.X series—Not directly comparable
ARC-AGI-1
DeepSeek V4 Pro 081390.00%SourceA.X series—Not directly comparable
ARC-AGI-2
DeepSeek V4 Pro 081361.3%SourceA.X series—Not directly comparable
Knowledge
MMLU-Pro
DeepSeek V4 Pro 081387.5%SourceA.X series—Not directly comparable
SimpleQA
DeepSeek V4 Pro 081357.9%SourceA.X series—Not directly comparable
Chinese-SimpleQA
DeepSeek V4 Pro 081384.4%SourceA.X series—Not directly comparable
GPQA
DeepSeek V4 Pro 081390.1%SourceA.X series—Not directly comparable
GPQA-D
DeepSeek V4 Pro 081390.1%SourceA.X series—Not directly comparable
HLE
DeepSeek V4 Pro 081342.7%SourceA.X series—Not directly comparable
GPQA Diamond (Vals)
DeepSeek V4 Pro 081392.4%SourceA.X series—Not directly comparable
MMLU-Pro (Vals)
DeepSeek V4 Pro 081387.0%SourceA.X series—Not directly comparable
Math
HMMT Feb 2026
DeepSeek V4 Pro 081395.2%SourceA.X series—Not directly comparable
IMOAnswerBench
DeepSeek V4 Pro 081389.8%SourceA.X series—Not directly comparable
Apex
DeepSeek V4 Pro 081338.3%SourceA.X series—Not directly comparable
Apex Shortlist
DeepSeek V4 Pro 081390.2%SourceA.X series—Not directly comparable
42 public results · 0 shared
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Last updated September 27, 2026