Agentic
Directional only- DeepSeek V4 Pro 0813
- 55.5
- Supported · #39/157
- GPT-6 Sol
- 69.7
- Estimated · #7/157
- Basis
- BenchAlign lane · 11 vs 4 public rows
- Reading
- Directional only
DeepSeek V4 Pro 0813 vs GPT-6 Sol
Decision reading
GPT-6 Sol has the higher public score estimate, 80.45 versus 64.37, 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.
Updated September 22, 2026. Rank says GPT-6 Sol 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
Share or export
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.
Long documents
Prompts that approach the documented context limit
GPT-6 Sol
GPT-6 Sol has the larger documented context window.
Confidence: documented
Chat turn cost
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.
Confidence: listed-rates
Cache-heavy agent loop cost
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.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
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.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-6 Sol is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6 Sol is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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.
51.6DeepSeek V4 Pro 081374.3GPT-6 Sol
Directional only · BenchAlign
GPT-6 Sol scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
37 DeepSeek V4 Pro 0813 only
3 Shared
7 GPT-6 Sol only
- Shared results
- 3
- DeepSeek V4 Pro 0813 only
- 37
- GPT-6 Sol only
- 7
- Like-for-like categories
- 0 / 8
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Coding
Directional only- DeepSeek V4 Pro 0813
- 51.6
- Supported · #54/159
- GPT-6 Sol
- 74.3
- Estimated · #6/159
- Basis
- BenchAlign lane · 15 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- DeepSeek V4 Pro 0813
- 58.2
- Estimated · #45/189
- GPT-6 Sol
- 80.2
- Estimated · #6/189
- Basis
- BenchAlign lane · 8 vs 5 public rows
- Reading
- Directional only
Reasoning
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- GPT-6 Sol
- 78.5
- #5/17
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- GPT-6 Sol
- 82.8
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- GPT-6 Sol
- 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
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V4 Pro 0813 | GPT-6 Sol | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.5Supported · #39/157 | 69.7Estimated · #7/157 | Directional onlyBenchAlign lane · 11 vs 4 public rows | Directional only |
| Coding | 51.6Supported · #54/159 | 74.3Estimated · #6/159 | Directional onlyBenchAlign lane · 15 vs 1 public rows | Directional only |
| Knowledge | 58.2Estimated · #45/189 | 80.2Estimated · #6/189 | Directional onlyBenchAlign lane · 8 vs 5 public rows | Directional only |
| Reasoning | Not ranked | 78.5#5/17 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 82.8Unranked · 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 | 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
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.
AutomationBench
Agentic
DeepSeek V4 Pro 0813: 31.8%GPT-6 Sol: 33.2%Normalized gap 1.4DeepSeek V4 Pro 0813 source GPT-6 Sol source
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
- $0.00087
- Fits in one request
- GPT-6 Sol
- $0.007
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V4 Pro 0813
- $0.02436
- Fits in one request
- GPT-6 Sol
- $0.13
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek V4 Pro 0813
- $0.01812
- Fits in one request
- GPT-6 Sol
- $0.18
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V4 Pro 0813GPT-6 Sol
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4 Pro 0813
GPT-6 Sol
API model ID
DeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingGPT-6 Sol
gpt-6-sol
OpenAI GPT-6 Sol model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V4 Pro 0813
$0.003625 per 1M cached input tokens
GPT-6 Sol
$0.2 per 1M cached input tokens
OpenAI GPT-6 Sol model documentationDocumented inputs
DeepSeek V4 Pro 0813
GPT-6 Sol
text, image
OpenAI GPT-6 Sol model documentationDocumented outputs
DeepSeek V4 Pro 0813
GPT-6 Sol
Provider availability
DeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricing, V4 Pro continuation footnoteGPT-6 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchReasoning profile
DeepSeek V4 Pro 0813
Reasoning
GPT-6 Sol
Reasoning
Weight access
DeepSeek V4 Pro 0813
Proprietary
GPT-6 Sol
Proprietary
License
DeepSeek V4 Pro 0813
Proprietary
GPT-6 Sol
Proprietary
Release date
DeepSeek V4 Pro 0813
2026-08-13
GPT-6 Sol
2026-09-16
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
- GPT-6 Sol has the higher public score estimate, 80.45 versus 64.37, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.02436 vs $0.13. Cache-heavy agent loop: $0.01812 vs $0.18.
- Context tradeoff
- GPT-6 Sol has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence47 rows
Agentic
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceGPT-6 Sol—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceGPT-6 Sol—Not directly comparable
BrowseComp
DeepSeek V4 Pro 081383.4%SourceGPT-6 Sol—Not directly comparable
HLE w/ tools
DeepSeek V4 Pro 081360.0%SourceGPT-6 Sol—Not directly comparable
MCP Atlas
DeepSeek V4 Pro 081373.6%SourceGPT-6 Sol—Not directly comparable
Toolathlon
DeepSeek V4 Pro 081351.8%SourceGPT-6 Sol—Not directly comparable
CyberGym
DeepSeek V4 Pro 081383.3%SourceGPT-6 Sol—Not directly comparable
Toolathlon-Verified
DeepSeek V4 Pro 081374.1%SourceGPT-6 Sol—Not directly comparable
Agents' Last Exam
DeepSeek V4 Pro 081325.7%SourceGPT-6 Sol56.4%SourceGPT-6 Sol leads this result
AutomationBench
DeepSeek V4 Pro 081331.8%SourceGPT-6 Sol33.2%SourceGPT-6 Sol leads this result
Terminal-Bench 2.1 (Vals)
DeepSeek V4 Pro 081354.7%SourceGPT-6 Sol—Not directly comparable
OSWorld 2.0
DeepSeek V4 Pro 0813—GPT-6 Sol60.5%SourceNot directly comparable
ExploitGym
DeepSeek V4 Pro 0813—GPT-6 Sol22.1%SourceNot directly comparable
Coding
LiveCodeBench Pass@1-COT
DeepSeek V4 Pro 081393.5%SourceGPT-6 Sol—Not directly comparable
Codeforces
DeepSeek V4 Pro 08133206.0SourceGPT-6 Sol—Not directly comparable
SWE-bench Verified
DeepSeek V4 Pro 081380.6%SourceGPT-6 Sol—Not directly comparable
SWE-bench Pro
DeepSeek V4 Pro 081355.4%SourceGPT-6 Sol—Not directly comparable
SWE Multilingual
DeepSeek V4 Pro 081376.2%SourceGPT-6 Sol—Not directly comparable
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceGPT-6 Sol—Not directly comparable
Vibe Code Bench
DeepSeek V4 Pro 081349.93%SourceGPT-6 Sol—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceGPT-6 Sol—Not directly comparable
NL2Repo
DeepSeek V4 Pro 081361.5%SourceGPT-6 Sol—Not directly comparable
DeepSWE
DeepSeek V4 Pro 081362.7%SourceGPT-6 Sol68.8%SourceGPT-6 Sol leads this result
DSBench-FullStack
DeepSeek V4 Pro 081371.1%SourceGPT-6 Sol—Not directly comparable
DSBench-Hard
DeepSeek V4 Pro 081367.2%SourceGPT-6 Sol—Not directly comparable
OpenHarmony Bench
DeepSeek V4 Pro 081359.0%SourceGPT-6 Sol—Not directly comparable
LiveCodeBench (Vals)
DeepSeek V4 Pro 081387.5%SourceGPT-6 Sol—Not directly comparable
SWE-bench (Vals)
DeepSeek V4 Pro 081396.4%SourceGPT-6 Sol—Not directly comparable
Reasoning
Knowledge
MMLU-Pro
DeepSeek V4 Pro 081387.5%SourceGPT-6 Sol—Not directly comparable
SimpleQA
DeepSeek V4 Pro 081357.9%SourceGPT-6 Sol—Not directly comparable
Chinese-SimpleQA
DeepSeek V4 Pro 081384.4%SourceGPT-6 Sol—Not directly comparable
GPQA
DeepSeek V4 Pro 081390.1%SourceGPT-6 Sol—Not directly comparable
GPQA-D
DeepSeek V4 Pro 081390.1%SourceGPT-6 Sol—Not directly comparable
HLE
DeepSeek V4 Pro 081342.7%SourceGPT-6 Sol—Not directly comparable
GPQA Diamond (Vals)
DeepSeek V4 Pro 081392.4%SourceGPT-6 Sol—Not directly comparable
MMLU-Pro (Vals)
DeepSeek V4 Pro 081387.0%SourceGPT-6 Sol—Not directly comparable
HealthBench (raw)
DeepSeek V4 Pro 0813—GPT-6 Sol47.1%SourceNot directly comparable
HealthBench (length-adjusted)
DeepSeek V4 Pro 0813—GPT-6 Sol53.2%SourceNot directly comparable
HealthBench Professional
DeepSeek V4 Pro 0813—GPT-6 Sol60.8%SourceNot directly comparable
HealthBench Professional (raw)
DeepSeek V4 Pro 0813—GPT-6 Sol59.5%SourceNot directly comparable
HealthBench Hard
DeepSeek V4 Pro 0813—GPT-6 Sol30.1%SourceNot directly comparable
Math
HMMT Feb 2026
DeepSeek V4 Pro 081395.2%SourceGPT-6 Sol—Not directly comparable
IMOAnswerBench
DeepSeek V4 Pro 081389.8%SourceGPT-6 Sol—Not directly comparable
Apex
DeepSeek V4 Pro 081338.3%SourceGPT-6 Sol—Not directly comparable
Apex Shortlist
DeepSeek V4 Pro 081390.2%SourceGPT-6 Sol—Not directly comparable
Questions
Which is better, DeepSeek V4 Pro 0813 or GPT-6 Sol?
GPT-6 Sol has the higher public score estimate, 80.45 versus 64.37, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, DeepSeek V4 Pro 0813 or GPT-6 Sol?
GPT-6 Sol scores higher for coding on the public lane, 74.3 to 51.6. GPT-6 Sol is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, DeepSeek V4 Pro 0813 or GPT-6 Sol?
GPT-6 Sol scores higher for agentic tasks on the public lane, 69.7 to 55.5. GPT-6 Sol 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.
Which costs less, DeepSeek V4 Pro 0813 or GPT-6 Sol?
For the stated presets, chat costs $0.00087 on DeepSeek V4 Pro 0813 and $0.007 on GPT-6 Sol; repository review costs $0.02436 and $0.13; the cache-heavy agent loop costs $0.01812 and $0.18. Costs use the listed standard API rates.
Which has the larger context window, DeepSeek V4 Pro 0813 or GPT-6 Sol?
GPT-6 Sol has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated September 22, 2026
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