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. This is a same-family comparison, so migration details appear when the source data supports them.
GPT-6.1 Sol has the higher public score estimate, 84.63 versus 77.44, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 22 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
GPT-6.1 Sol: $2 input / $10 output
GPT-6 Sol: $2 input / $10 output
GPT-6.1 Sol: 1.05M
GPT-6 Sol: 1.05M
GPT-6.1 Sol: September 29, 2026
GPT-6 Sol: September 22, 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 67.3, 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 65.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
200K cached + 20K fresh input + 10K output tokens
GPT-6.1 Sol
GPT-6.1 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
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.
ARC-AGI-3Reasoning
Normalized gap 48.1FrontierMath v2 (Tier 4)Math
Normalized gap 10.0ARC-AGI-2Reasoning
Normalized gap 4.6FrontierMath v2 (Tiers 1-3)Math
Normalized gap 3.9AutomationBenchAgentic
Normalized gap 2.9Each 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 | GPT-6.1 Sol | GPT-6 Sol | Basis | Reading |
|---|---|---|---|---|
| Agentic | 65.7Supported · #17/135 | 65.1Supported · #18/135 | Like-for-likeBenchAlign v5.8 lane · 7 vs 10 public rows | GPT-6.1 Sol leads · intervals overlap |
| Coding | 73.4Supported · #8/162 | 67.3Supported · #13/162 | Like-for-likeBenchAlign v5.8 lane · 6 vs 3 public rows | GPT-6.1 Sol leads · intervals overlap |
| Reasoning | 80.4Supported · #5/160 | 72.3Supported · #15/160 | Like-for-likeBenchAlign v5.8 lane · 6 vs 4 public rows | GPT-6.1 Sol leads · intervals overlap |
| Multimodal | 78.9Supported · #4/67 | 75.0Supported · #8/67 | Like-for-likeBenchAlign v5.8 lane · 3 vs 2 public rows | GPT-6.1 Sol leads · intervals overlap |
| Knowledge | 85.6Supported · #3/180 | 80.1Estimated · #10/180 | Directional onlyBenchAlign v5.8 lane · 5 vs 5 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 | 93.8Unranked · 2 rankable rows | 92.1Unranked · 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
Modeled costs are equal
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Modeled costs are equal
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-6.1 Sol 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.
GPT-6.1 Sol
GPT-6 Sol
GPT-6.1 Sol
gpt-6.1-sol
OpenAI GPT-6.1 Sol model documentationGPT-6 Sol
gpt-6-sol
OpenAI GPT-6 Sol model documentationA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-6.1 Sol
$0.1 per 1M cached input tokens
OpenAI GPT-6.1 Sol model documentationGPT-6 Sol
$0.2 per 1M cached input tokens
OpenAI GPT-6 Sol model documentationGPT-6.1 Sol
text, image
OpenAI GPT-6.1 Sol model documentationGPT-6 Sol
text, image
OpenAI GPT-6 Sol model documentationGPT-6.1 Sol
GPT-6 Sol
GPT-6.1 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API
OpenAI GPT-6.1 Sol model documentationGPT-6 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchGPT-6.1 Sol
Reasoning
GPT-6 Sol
Reasoning
GPT-6.1 Sol
Proprietary
GPT-6 Sol
Proprietary
GPT-6.1 Sol
Proprietary
GPT-6 Sol
Proprietary
GPT-6.1 Sol
2026-09-29
GPT-6 Sol
2026-09-22
GPT-6.1 Sol has the higher public score estimate, 84.63 versus 77.44, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
GPT-6.1 Sol has the higher public coding point estimate, 73.4 to 67.3, 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 65.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.007 on GPT-6.1 Sol and $0.007 on GPT-6 Sol; repository review costs $0.13 and $0.13; the cache-heavy agent loop costs $0.16 and $0.18. Costs use the listed standard API rates.
Both models list the same context window, 1.05M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 4.1
Shared sourceGPT-6.1 Sol leads this result
AutomationBench
GPT-6.1 Sol leads this result
Terminal-Bench-Science 0.1
Not directly comparable
ExploitGym
GPT-6.1 Sol leads this result
MazeBench
GPT-6.1 Sol leads this result
Hermes Index
Shared sourceGPT-6.1 Sol leads this result
Hermes Bench
Shared sourceGPT-6.1 Sol leads this result
Agents' Last Exam
Not directly comparable
OSWorld 2.0
Not directly comparable
CWE-bench v1
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
ScarfBench
Not directly comparable
DeepSWE
GPT-6.1 Sol leads this result
Bug Hunt Bench
Not directly comparable
FrontierCode 2.0
Shared sourceGPT-6.1 Sol leads this result
VulcanBench Frontier v4
Shared sourceGPT-6.1 Sol leads this result
PostTrainBench
Not directly comparable
BioBasicsBench
Not directly comparable
STEMBench
Not directly comparable
ARC-AGI-1
GPT-6.1 Sol leads this result
ARC-AGI-2
GPT-6.1 Sol leads this result
ARC-AGI-3
GPT-6.1 Sol leads this result
ARC-AGI-3 Provider Adapter
GPT-6.1 Sol leads this result
ExtractBench (LlamaIndex)
Shared sourceGPT-6.1 Sol leads this result
ExtractBench Pro
Not directly comparable
ParseBench
Shared sourceGPT-6.1 Sol leads this result
HealthBench (raw)
GPT-6.1 Sol leads this result
HealthBench (length-adjusted)
GPT-6.1 Sol leads this result
HealthBench Professional
GPT-6.1 Sol leads this result
HealthBench Professional (raw)
GPT-6.1 Sol leads this result
HealthBench Hard
GPT-6.1 Sol leads this result
Gray Swan IPI (15 attempts)
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
