Updated November 10, 2026. Rank says GPT-5.6 Luna is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-5.6 Luna has the higher public score estimate, 63.72 versus 56.88, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
GPT-5.1: $1.25 input / $10 output
GPT-5.6 Luna: $0.20 input / $1.20 output
GPT-5.1: 400K
GPT-5.6 Luna: 1.05M
GPT-5.1: November 13, 2025
GPT-5.6 Luna: 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 Luna
GPT-5.6 Luna has the higher public coding point estimate, 61.9 to 36.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna has the larger documented context window.
1K fresh input + 500 output tokens
GPT-5.6 Luna
GPT-5.6 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-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K output tokens
GPT-5.6 Luna
GPT-5.6 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-5.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
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 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.
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Each 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-5.1 | GPT-5.6 Luna | Basis | Reading |
|---|---|---|---|---|
| Coding | 36.7Supported · #81/158 | 61.9Supported · #17/158 | Like-for-likeBenchAlign v5.8 lane · 1 vs 8 public rows | GPT-5.6 Luna leads |
| Reasoning | 45.6Estimated · #66/158 | 58.3Supported · #36/158 | Directional onlyBenchAlign v5.8 lane · 1 vs 3 public rows | Directional only |
| Knowledge | 52.5Estimated · #68/179 | 63.1Supported · #40/179 | Directional onlyBenchAlign v5.8 lane · 0 vs 6 public rows | Directional only |
| Agentic | Not ranked | 59.1Supported · #33/132 | Not comparableBenchAlign v5.8 lane · 1 vs 11 public rows | Not comparable |
| Multimodal | Not ranked | 65.4Supported · #23/67 | 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 | 89.4#33/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 78.9Unranked · 3 rankable rows | Not comparableProvisional lane · 0 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
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.6 Luna has the lower modeled cost
GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
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-5.1
400K
GPT-5.6 Luna
1.05M
OpenAI model catalogGPT-5.1
Not sourced
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.1
Not published
GPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingGPT-5.1
text, image
OpenAI model page for gpt-5.1GPT-5.6 Luna
text, image
OpenAI model catalogGPT-5.1
GPT-5.6 Luna
GPT-5.1
Not sourced
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogGPT-5.1
Reasoning
GPT-5.6 Luna
Reasoning
GPT-5.1
Proprietary
GPT-5.6 Luna
Proprietary
GPT-5.1
Proprietary
GPT-5.6 Luna
Proprietary
GPT-5.1
2025-11-13
GPT-5.6 Luna
2026-07-09
GPT-5.6 Luna has the higher public score estimate, 63.72 versus 56.88, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
GPT-5.6 Luna has the higher public coding point estimate, 61.9 to 36.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
For the stated presets, chat costs $0.00625 on GPT-5.1 and $0.0008 on GPT-5.6 Luna; repository review costs $0.0925 and $0.0136; the cache-heavy agent loop costs $0.375 and $0.02. GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 400K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Gert Labs
Not directly comparable
Terminal-Bench 4.1
Not directly comparable
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
BrowseComp
Not directly comparable
OSWorld 2.0
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
Not directly comparable
Toolathlon
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
ApprenticeBench
Not directly comparable
MazeBench
Not directly comparable
Vibe Code Bench
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
DeepSWE
Not directly comparable
FrontierCode 1.1 Extended
Not directly comparable
CursorBench 3.2
Not directly comparable
VulcanBench v3
Not directly comparable
SWE-bench (Vals)
Not directly comparable
CursorBench 4.0
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Hard
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
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Last updated November 10, 2026
