Updated November 6, 2026. Rank says GPT-6 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 Sol has the higher public score, 78.09 versus 62.59, and the 90% score intervals do not overlap. 19 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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 Sol
GPT-6 Sol has the higher public coding point estimate, 65.2 to 51.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
GPT-6 Luna
GPT-6 Luna 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
GPT-6 Luna
GPT-6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-6 Luna
GPT-6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6 Luna is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
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 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.
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.
FrontierMath v2 (Tier 4)Math
Normalized gap 33.9ARC-AGI-2Reasoning
Normalized gap 30.3FrontierMath v2 (Tiers 1-3)Math
Normalized gap 10.9ARC-AGI-3Reasoning
Normalized gap 4.5Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | GPT-6 Luna | GPT-6 Sol | Basis | Reading |
|---|---|---|---|---|
| Coding | 51.8Supported · #42/157 | 65.2Supported · #13/157 | Like-for-likeBenchAlign v5.8 lane · 3 vs 2 public rows | GPT-6 Sol leads · intervals overlap |
| Reasoning | 57.5#22/25 | 71.7#12/25 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | GPT-6 Sol leads |
| Agentic | 53.5Estimated · #45/131 | 57.6Supported · #35/131 | Directional onlyBenchAlign v5.8 lane · 5 vs 8 public rows | Directional only |
| Knowledge | 63.3Supported · #40/178 | 80.0Estimated · #9/178 | Directional onlyBenchAlign v5.8 lane · 5 vs 5 public rows | Directional only |
| Multimodal | 81.9Unranked · 1 rankable row | 86.6Unranked · 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 · 2 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, 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
GPT-6 Luna has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-6 Luna has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-6 Luna has the lower modeled cost
Costs use the listed standard API rates.
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 Luna
GPT-6 Sol
GPT-6 Luna
gpt-6-luna
OpenAI GPT-6 Luna 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 Luna
$0.01 per 1M cached input tokens
OpenAI GPT-6 Luna model documentationGPT-6 Sol
$0.2 per 1M cached input tokens
OpenAI GPT-6 Sol model documentationGPT-6 Luna
text, image
OpenAI GPT-6 Luna model documentationGPT-6 Sol
text, image
OpenAI GPT-6 Sol model documentationGPT-6 Luna
GPT-6 Sol
GPT-6 Luna
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchGPT-6 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchGPT-6 Luna
Reasoning
GPT-6 Sol
Reasoning
GPT-6 Luna
Proprietary
GPT-6 Sol
Proprietary
GPT-6 Luna
Proprietary
GPT-6 Sol
Proprietary
GPT-6 Luna
2026-09-22
GPT-6 Sol
2026-09-22
GPT-6 Sol has the higher public score, 78.09 versus 62.59, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
GPT-6 Sol has the higher public coding point estimate, 65.2 to 51.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-6 Sol scores higher for agentic tasks on the public lane, 57.6 to 53.5. GPT-6 Luna 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.
For the stated presets, chat costs $0.00035 on GPT-6 Luna and $0.007 on GPT-6 Sol; repository review costs $0.0065 and $0.13; the cache-heavy agent loop costs $0.009 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.
ExploitGym
Shared sourceGPT-6 Sol leads this result
Hermes Index
Shared sourceGPT-6 Sol leads this result
Hermes Bench
Shared sourceGPT-6 Sol leads this result
MazeBench
GPT-6 Sol leads this result
OSWorld 2.1 Offline
Not directly comparable
Agents' Last Exam
Not directly comparable
AutomationBench
Not directly comparable
OSWorld 2.0
Not directly comparable
CWE-bench v1
Not directly comparable
DeepSWE
Shared sourceGPT-6 Sol leads this result
Bug Hunt Bench
Not directly comparable
FrontierCode 2.0
Shared sourceGPT-6 Sol leads this result
BioBasicsBench
Not directly comparable
ARC-AGI-1
GPT-6 Sol leads this result
ARC-AGI-2
GPT-6 Sol leads this result
ARC-AGI-3
GPT-6 Sol leads this result
ARC-AGI-3 Provider Adapter
GPT-6 Sol leads this result
ExtractBench Pro
Not directly comparable
ExtractBench (LlamaIndex)
Shared sourceGPT-6 Sol leads this result
ParseBench
Shared sourceGPT-6 Sol leads this result
HealthBench (raw)
Shared sourceGPT-6 Luna leads this result
HealthBench (length-adjusted)
Shared sourceGPT-6 Luna leads this result
HealthBench Professional
Shared sourceTie
HealthBench Professional (raw)
Shared sourceGPT-6 Luna leads this result
HealthBench Hard
Shared sourceGPT-6 Luna leads this result
Gray Swan IPI (15 attempts)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-6 Sol leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-6 Sol leads this result
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Last updated November 6, 2026
