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 60.05, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 15 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
GLM-5.2: $1.40 input / $4.40 output
GPT-5.6 Luna: $0.20 input / $1.20 output
GLM-5.2: 1M
GPT-5.6 Luna: 1.05M
GLM-5.2: June 16, 2026
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 54, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
GPT-5.6 Luna
GPT-5.6 Luna has the higher public agentic point estimate, 59.1 to 55.5, 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. GLM-5.2 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.
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.
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 29.2FrontierMath v2 (Tiers 1-3)Math
Normalized gap 19.4CursorBench 3.2Coding
Normalized gap 6.1GPQAKnowledge
Normalized gap 1.1MMLU-Pro (Vals)Knowledge
Normalized gap 0.7Each 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 | GLM-5.2 | GPT-5.6 Luna | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.5Supported · #43/132 | 59.1Supported · #33/132 | Like-for-likeBenchAlign v5.8 lane · 8 vs 11 public rows | GPT-5.6 Luna leads · intervals overlap |
| Coding | 54.0Supported · #36/158 | 61.9Supported · #17/158 | Like-for-likeBenchAlign v5.8 lane · 9 vs 8 public rows | GPT-5.6 Luna leads · intervals overlap |
| Reasoning | 42.9Supported · #75/158 | 58.3Supported · #36/158 | Like-for-likeBenchAlign v5.8 lane · 1 vs 3 public rows | GPT-5.6 Luna leads · intervals overlap |
| Knowledge | 56.5Supported · #57/179 | 63.1Supported · #40/179 | Like-for-likeBenchAlign v5.8 lane · 6 vs 6 public rows | GPT-5.6 Luna leads · intervals overlap |
| 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 | 89.9#29/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 74.6#3/12 | 78.9Unranked · 3 rankable rows | Not comparableProvisional lane · 4 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
GLM-5.2 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.
GLM-5.2
1M
GPT-5.6 Luna
1.05M
OpenAI model catalogGLM-5.2
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.
GLM-5.2
Not published
GPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingGLM-5.2
GPT-5.6 Luna
text, image
OpenAI model catalogGLM-5.2
GPT-5.6 Luna
GLM-5.2
Not sourced
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogGLM-5.2
Reasoning
GPT-5.6 Luna
Reasoning
GLM-5.2
Open Weight
GPT-5.6 Luna
Proprietary
GLM-5.2
Open Weight
GPT-5.6 Luna
Proprietary
GLM-5.2
2026-06-16
GPT-5.6 Luna
2026-07-09
GPT-5.6 Luna has the higher public score estimate, 63.72 versus 60.05, 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 54, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.6 Luna has the higher public agentic tasks point estimate, 59.1 to 55.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.0008 on GPT-5.6 Luna; repository review costs $0.0832 and $0.0136; the cache-heavy agent loop costs $0.352 and $0.02. GLM-5.2 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 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 3.0
Shared sourceGPT-5.6 Luna leads this result
Terminal-Bench 2.1
GPT-5.6 Luna leads this result
MCP Atlas
Not directly comparable
Toolathlon
GPT-5.6 Luna leads this result
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Luna leads this result
HLE w/ tools
Not directly comparable
MazeBench
Tie
Terminal-Bench 4.1
Not directly comparable
BrowseComp
Not directly comparable
OSWorld 2.0
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
Not directly comparable
ApprenticeBench
Not directly comparable
SWE-bench Pro
GPT-5.6 Luna leads this result
NL2Repo
Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna leads this result
ProgramBench
Not directly comparable
CursorBench 3.2
Shared sourceGPT-5.6 Luna leads this result
OpenHarmony Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
GPT-5.6 Luna leads this result
PostTrainBench v1.1
Not directly comparable
DeepSWE
Not directly comparable
FrontierCode 1.1 Extended
Not directly comparable
VulcanBench v3
Not directly comparable
CursorBench 4.0
Not directly comparable
GPQA
GPT-5.6 Luna leads this result
GPQA-D
GPT-5.6 Luna leads this result
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Luna leads this result
MMLU-Pro (Vals)
GLM-5.2 leads this result
HealthBench Professional
Not directly comparable
HealthBench Hard
Not directly comparable
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Luna leads this result
FrontierMath v2 (Tier 4)
GPT-5.6 Luna leads this result
FrontierMath (legacy)
Not directly comparable
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 7,000+ subscribers.
Last updated November 10, 2026
