Updated November 11, 2026. Rank says GPT-5.6 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-5.6 Sol has the higher public point estimate, 74.37 versus 62.43. Their conditional score ranges overlap. These ranges do not establish rank confidence. 16 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
GPT-5.6 Sol: $4 input / $20 output
DeepSeek V4 Pro 0813: 1M
GPT-5.6 Sol: 1.05M
DeepSeek V4 Pro 0813: August 13, 2026
GPT-5.6 Sol: July 9, 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
GPT-5.6 Sol
GPT-5.6 Sol has the higher public coding point estimate, 67.2 to 47.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
GPT-5.6 Sol
GPT-5.6 Sol has the higher public agentic point estimate, 67.9 to 55, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol 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. 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.
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
GPT-5.6 Sol 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.
ARC-AGI-2Reasoning
Normalized gap 31.2SWE-bench ProCoding
Normalized gap 9.2BrowseCompAgentic
Normalized gap 8.8LiveCodeBench (Vals)Coding
Normalized gap 4.9GPQAKnowledge
Normalized gap 4.5Each 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 | GPT-5.6 Sol | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.0Supported · #44/132 | 67.9Supported · #13/132 | Like-for-likeBenchAlign v5.8 lane · 12 vs 12 public rows | GPT-5.6 Sol leads · intervals overlap |
| Coding | 47.9Supported · #53/158 | 67.2Supported · #12/158 | Like-for-likeBenchAlign v5.8 lane · 15 vs 14 public rows | GPT-5.6 Sol leads |
| Reasoning | 58.1Estimated · #39/158 | 73.7Supported · #13/158 | Directional onlyBenchAlign v5.8 lane · 4 vs 5 public rows | Directional only |
| Knowledge | 62.5Estimated · #41/179 | 75.5Supported · #12/179 | Directional onlyBenchAlign v5.8 lane · 7 vs 8 public rows | Directional only |
| Multimodal | Not ranked | 75.5Supported · #9/67 | Not comparableBenchAlign v5.8 lane · 0 vs 4 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 89.1#34/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 84.4Unranked · 4 rankable rows | 90.3Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 2 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
GPT-5.6 Sol
1.05M
OpenAI model catalogDeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingGPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogA 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-5.6 Sol
$0.4 per 1M cached input tokens
OpenAI pricingDeepSeek V4 Pro 0813
GPT-5.6 Sol
text, image
OpenAI model catalogDeepSeek V4 Pro 0813
GPT-5.6 Sol
DeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricing, V4 Pro continuation footnoteGPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogDeepSeek V4 Pro 0813
Reasoning
GPT-5.6 Sol
Reasoning
DeepSeek V4 Pro 0813
Open Weight
GPT-5.6 Sol
Proprietary
DeepSeek V4 Pro 0813
Open Weight
GPT-5.6 Sol
Proprietary
DeepSeek V4 Pro 0813
2026-08-13
GPT-5.6 Sol
2026-07-09
GPT-5.6 Sol has the higher public point estimate, 74.37 versus 62.43. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GPT-5.6 Sol has the higher public coding point estimate, 67.2 to 47.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.6 Sol has the higher public agentic tasks point estimate, 67.9 to 55, 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 $0.014 on GPT-5.6 Sol; repository review costs $0.07788 and $0.26; the cache-heavy agent loop costs $0.0748 and $0.36. Costs use the listed standard API rates.
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 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
GPT-5.6 Sol leads this result
BrowseComp
GPT-5.6 Sol leads this result
HLE w/ tools
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
GPT-5.6 Sol leads this result
CyberGym
GPT-5.6 Sol leads this result
Toolathlon-Verified
Not directly comparable
Agents' Last Exam
Not directly comparable
AutomationBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Sol leads this result
Terminal-Bench 4.1
Not directly comparable
LifeVal
Not directly comparable
Terminal-Bench 3.0
Not directly comparable
OSWorld 2.0
Not directly comparable
ExploitGym
Not directly comparable
ApprenticeBench
Not directly comparable
MazeBench
Not directly comparable
LiveCodeBench Pass@1-COT
Not directly comparable
Codeforces
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
GPT-5.6 Sol 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
GPT-5.6 Sol leads this result
NL2Repo
Not directly comparable
DeepSWE
GPT-5.6 Sol leads this result
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
Bug Hunt Bench
Not directly comparable
FrontierCode 1.1 Extended
Not directly comparable
FrontierSWE v2
Not directly comparable
CursorBench 3.2
Not directly comparable
VulcanBench v3
Not directly comparable
VulcanBench CII v1
Not directly comparable
CursorBench 4.0
Not directly comparable
PostTrainBench v1.1
Not directly comparable
FrontierCode 2.0
Not directly comparable
MRCR 1M
Not directly comparable
CorpusQA 1M
Not directly comparable
ARC-AGI-1
GPT-5.6 Sol leads this result
ARC-AGI-2
GPT-5.6 Sol leads this result
BioBasicsBench
Not directly comparable
ARC-AGI-3
Not directly comparable
GeneBench-Pro
Not directly comparable
ExtractBench (LlamaIndex)
Not directly comparable
ParseBench
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
MMLU-Pro
Not directly comparable
Chinese-SimpleQA
Not directly comparable
GPQA
GPT-5.6 Sol leads this result
GPQA-D
GPT-5.6 Sol leads this result
HLE
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Sol leads this result
MMLU-Pro (Vals)
GPT-5.6 Sol leads this result
HLE-Verified
Not directly comparable
LABBench2
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Hard
Not directly comparable
Gray Swan IPI (15 attempts)
Not directly comparable
HMMT Feb 2026
Not directly comparable
IMOAnswerBench
Not directly comparable
Apex
Not directly comparable
Apex Shortlist
Not directly comparable
FrontierMath (legacy)
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
