Updated November 6, 2026. Rank says GPT-6 Luna is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-6 Luna has the higher public score estimate, 62.59 versus 60.2, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 2 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 Luna
GPT-6 Luna has the higher public coding point estimate, 51.8 to 50, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-6 Luna
GPT-6 Luna has the larger documented context window.
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.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
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 Luna 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.
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-2Reasoning
Normalized gap 16.8Each 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 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Coding | 51.8Supported · #42/157 | 50.0Supported · #47/157 | Like-for-likeBenchAlign v5.8 lane · 3 vs 5 public rows | GPT-6 Luna leads · intervals overlap |
| Knowledge | 63.3Supported · #40/178 | 62.3Supported · #43/178 | Like-for-likeBenchAlign v5.8 lane · 5 vs 7 public rows | GPT-6 Luna leads · intervals overlap |
| Agentic | 53.5Estimated · #45/131 | 49.5Supported · #52/131 | Directional onlyBenchAlign v5.8 lane · 5 vs 5 public rows | Directional only |
| Reasoning | 57.5#22/25 | 55.4Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Multimodal | 81.9Unranked · 1 rankable row | 83.1#16/59 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.3#11/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 80.2Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 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
Muse Spark has no comparable published API token rate.
50K fresh input + 3K output tokens
Muse Spark has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Muse Spark has no comparable published API token rate.
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
Muse Spark
262K
GPT-6 Luna
gpt-6-luna
OpenAI GPT-6 Luna model documentationMuse Spark
Not sourced
A 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 documentationMuse Spark
No comparable hosted API rate
GPT-6 Luna
text, image
OpenAI GPT-6 Luna model documentationMuse Spark
Not sourced
GPT-6 Luna
Muse Spark
Not sourced
GPT-6 Luna
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchMuse Spark
Not sourced
GPT-6 Luna
Reasoning
Muse Spark
Reasoning
GPT-6 Luna
Proprietary
Muse Spark
Proprietary
GPT-6 Luna
Proprietary
Muse Spark
Proprietary
GPT-6 Luna
2026-09-22
Muse Spark
2026-04-08
GPT-6 Luna has the higher public score estimate, 62.59 versus 60.2, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
GPT-6 Luna has the higher public coding point estimate, 51.8 to 50, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-6 Luna scores higher for agentic tasks on the public lane, 53.5 to 49.5. 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
GPT-6 Luna has the larger documented context window: 1.05M, compared with 262K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
ExploitGym
Not directly comparable
Hermes Index
Not directly comparable
Hermes Bench
Not directly comparable
MazeBench
Not directly comparable
OSWorld 2.1 Offline
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
τ²-bench results
Not directly comparable
DeepSearchQA
Not directly comparable
CyberGym
Not directly comparable
Claw-Eval
Not directly comparable
DeepSWE
Not directly comparable
Bug Hunt Bench
Not directly comparable
FrontierCode 2.0
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
LiveCodeBench Pro
Not directly comparable
Vibe Code Bench
Not directly comparable
SWE-bench (Vals)
Not directly comparable
BioBasicsBench
Not directly comparable
ARC-AGI-1
Not directly comparable
ARC-AGI-2
GPT-6 Luna leads this result
ARC-AGI-3
Not directly comparable
ARC-AGI-3 Provider Adapter
Not directly comparable
ExtractBench Pro
Not directly comparable
ExtractBench (LlamaIndex)
Not directly comparable
ParseBench
Not directly comparable
CharXiv
Not directly comparable
MMMU-Pro
Not directly comparable
ERQA
Not directly comparable
SimpleVQA
Not directly comparable
ScreenSpot Pro
Not directly comparable
ZeroBench
Not directly comparable
MedXpertQA (MM)
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
Muse Spark leads this result
GPQA-D
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
MedXpertQA (Text)
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
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Last updated November 6, 2026
