Updated November 13, 2026. Rank says GPT-6.1 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-6.1 Sol has the higher public score, 84.63 versus 64.51, and the 90% score intervals do not overlap. 13 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
DeepSeek V4.1 Flash 1017: $0.30 input / $1.20 output
GPT-6.1 Sol: $2 input / $10 output
DeepSeek V4.1 Flash 1017: 1M
GPT-6.1 Sol: 1.05M
DeepSeek V4.1 Flash 1017: October 17, 2026
GPT-6.1 Sol: September 29, 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-6.1 Sol
GPT-6.1 Sol has the higher public coding point estimate, 73.4 to 59.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
GPT-6.1 Sol
GPT-6.1 Sol has the higher public agentic point estimate, 65.7 to 61.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-6.1 Sol
GPT-6.1 Sol has the larger documented context window.
1K fresh input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 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 V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 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-6.1 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.
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.
FrontierMath v2 (Tier 4)Math
Normalized gap 73.2FrontierMath v2 (Tiers 1-3)Math
Normalized gap 26.3AutomationBenchAgentic
Normalized gap 24.4Each 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 V4.1 Flash 1017 | GPT-6.1 Sol | Basis | Reading |
|---|---|---|---|---|
| Agentic | 61.1Supported · #25/135 | 65.7Supported · #17/135 | Like-for-likeBenchAlign v5.8 lane · 12 vs 7 public rows | GPT-6.1 Sol leads · intervals overlap |
| Coding | 59.7Supported · #18/162 | 73.4Supported · #8/162 | Like-for-likeBenchAlign v5.8 lane · 13 vs 6 public rows | GPT-6.1 Sol leads |
| Multimodal | 64.7Supported · #21/67 | 78.9Supported · #4/67 | Like-for-likeBenchAlign v5.8 lane · 5 vs 3 public rows | GPT-6.1 Sol leads · intervals overlap |
| Knowledge | 65.1Supported · #29/180 | 85.6Supported · #3/180 | Like-for-likeBenchAlign v5.8 lane · 3 vs 5 public rows | GPT-6.1 Sol leads |
| Reasoning | 59.5Estimated · #35/160 | 80.4Supported · #5/160 | Directional onlyBenchAlign v5.8 lane · 3 vs 6 public rows | Directional only |
| 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 | 65.6Unranked · 3 rankable rows | 93.8Unranked · 2 rankable rows | Not comparableProvisional lane · 2 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 V4.1 Flash 1017 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash 1017 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4.1 Flash 1017 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 V4.1 Flash 1017
GPT-6.1 Sol
DeepSeek V4.1 Flash 1017
deepseek-flash
DeepSeek models and pricingGPT-6.1 Sol
gpt-6.1-sol
OpenAI GPT-6.1 Sol model documentationA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V4.1 Flash 1017
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingGPT-6.1 Sol
$0.1 per 1M cached input tokens
OpenAI GPT-6.1 Sol model documentationDeepSeek V4.1 Flash 1017
text, image
DeepSeek vision guideGPT-6.1 Sol
text, image
OpenAI GPT-6.1 Sol model documentationDeepSeek V4.1 Flash 1017
GPT-6.1 Sol
DeepSeek V4.1 Flash 1017
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash-1017 API changelogGPT-6.1 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API
OpenAI GPT-6.1 Sol model documentationDeepSeek V4.1 Flash 1017
Reasoning
GPT-6.1 Sol
Reasoning
DeepSeek V4.1 Flash 1017
Open Weight
GPT-6.1 Sol
Proprietary
DeepSeek V4.1 Flash 1017
Open Weight
GPT-6.1 Sol
Proprietary
DeepSeek V4.1 Flash 1017
2026-10-17
GPT-6.1 Sol
2026-09-29
GPT-6.1 Sol has the higher public score, 84.63 versus 64.51, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
GPT-6.1 Sol has the higher public coding point estimate, 73.4 to 59.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-6.1 Sol has the higher public agentic tasks point estimate, 65.7 to 61.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0009 on DeepSeek V4.1 Flash 1017 and $0.007 on GPT-6.1 Sol; repository review costs $0.0186 and $0.13; the cache-heavy agent loop costs $0.0192 and $0.16. Costs use the listed standard API rates.
GPT-6.1 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.
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
Terminal-Bench 4.0
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
GPT-6.1 Sol leads this result
HLE w/ tools
Not directly comparable
AutomationBench
DeepSeek V4.1 Flash 1017 leads this result
Agents' Last Exam
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
CWE-bench v1
Not directly comparable
MazeBench
GPT-6.1 Sol leads this result
CWE-bench
Not directly comparable
Terminal-Bench 4.1
Not directly comparable
Terminal-Bench-Science 0.1
Not directly comparable
Hermes Index
Not directly comparable
Hermes Bench
Not directly comparable
ScarfBench
Shared sourceDeepSeek V4.1 Flash 1017 leads this result
Codeforces
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
DeepSWE
DeepSeek V4.1 Flash 1017 leads this result
ProgramBench
Not directly comparable
NL2Repo
Not directly comparable
OpenHarmony Bench v1.0
Not directly comparable
Bug Hunt Bench
Shared sourceGPT-6.1 Sol leads this result
FrontierCode 2.0
Shared sourceGPT-6.1 Sol leads this result
CursorBench 4.0
Not directly comparable
VulcanBench Frontier v4
Shared sourceGPT-6.1 Sol leads this result
OpenHarmony Bench v1.2
Not directly comparable
PostTrainBench
Not directly comparable
STEMBench
Shared sourceGPT-6.1 Sol leads this result
JevBench 1.4
Not directly comparable
JevBench 1.5
Not directly comparable
BioBasicsBench
Not directly comparable
ARC-AGI-1
Not directly comparable
ARC-AGI-2
Not directly comparable
ARC-AGI-3
Not directly comparable
ARC-AGI-3 Provider Adapter
Not directly comparable
ExtractBench (LlamaIndex)
Shared sourceGPT-6.1 Sol leads this result
ParseBench
Shared sourceGPT-6.1 Sol leads this result
Chartography (tools)
Not directly comparable
BabyVision w/ Python
Not directly comparable
ZeroBench w/ Python
Not directly comparable
ExtractBench Pro
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
HealthBench (raw)
Not directly comparable
HealthBench (length-adjusted)
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Professional (raw)
Not directly comparable
HealthBench Hard
Not directly comparable
Apex
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-6.1 Sol leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-6.1 Sol leads this result
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Last updated November 13, 2026
